Dashboard giver dig muligheden for at visualisere din data. Ved at italesætte data og visualisere det, kan man bruge det som en daglig driver til at øge motivationen. Ved synliggørelse af data, muliggør man, at virksomheden opnår en større forståelse for hvad de betyder. Data er ikke længere noget som kun lever i rapporter og bestyrelsesrum, men i stedet en integreret del af virksomhedens daglige drift.

dashboard

Today, the need to preserve the confidence and security of people as well as uphold the integrity of enterprises, and organizations is critical. To effectively monitor such aspects of the behavior of the users, or to identify risks that possibly exist, a dashboard is most important. To facilitate the process, businesses can use special data visualization tools applied to build trust and safety dashboards. This article discusses how such dashboards could be created and the advantages of creating one. What is a Trust and Safety Dashboard? A trust and safety dashboard is an interface that can represent the data collected around different aspects of user’s behavior in an easy to understand manner. These dashboards can assist organizations in monitoring and detecting policy violations, identifying prohibited content impressions and volume, observe attempts to bypass policy. For example, an e-commerce website might want to use these dashboards to identify frauds, people trying to scam the business and identify patterns that might give insights into the security aspects of transactions. The main challenge of creating a trust and safety dashboard is big data management - making the data easy to understand and represent in an actionable format. The Role of Data Visualization in Trust and Safety Business intelligence tools take data and bring it to life by creating a picture in form of a graph, chart or heatmap. This facilitates the identification of patterns, trends as well as outliers enabling decisions-making much faster. For trust and safety purposes, visualizations can: Highlight Anomalies: Identify user’s irregularities or sharp increase in suspicious content emitted by users.


Track Metrics Over Time: Track changes in near-misses or accident rates, complaints or feedback from users, or breaches of compliance regulations.


Segment Data by Categories: Identify problems based on the location or the demographic or the type of different policy violations.


Facilitate Real-Time Monitoring: Real-time information can be analyzed proactively to counter any risk factors. Steps to Develop a Custom Trust and Safety Dashboard Define Objectives and Metrics First of all, you should define the main objectives of using the selected dashboard. Are you looking for fraud detection, content moderation, or to ensure users’ data security? After objectives are defined, determine the metrics to track, whether it is flagged content, logins, or support tickets. Gather and Prepare Data Use activity logs, complaint records, or external APIs. Clean the data to make it precise and fit for analysis. Tools like Alteryx or ETL tool pipelines can help ease this process. Select a Visualization Tool Select a tool that will suit your needs and may not require technical skills. For non-technical teams, there is Power BI or Tableau where they can easily use the drag-and-drop features. For more customization, developers may go for Python libraries like Matplotlib or Plotly. Design the Dashboard Layout In design, it is always most important to ensure that the final product is clear and easy to use. Similar attributes should be grouped in one section, the use of appropriate color schemes, and the most important information should be easily and pointed out properly. Stay away from gaudiness, and hold simplicity. Implement Real-Time Data Updates For a dynamic environment, it is necessary to incorporate tools for live data feed so that the changes can be reflected in the dashboard. For this, there are technologies such as Apache Kafka or WebSockets. Test and Iterate Notify stakeholders about the created dashboard and collect feedback. Analyze the feedback to identify patterns regarding the usefulness of the visualizations, change metrics, or even the overall topology. Best Practices for Effective Dashboards User-Centric Design: Ensure that the end-user view of the dashboard is most appropriate for their needs. For instance, a compliance office will require different metrics, and a cybersecurity analyst will have different needs.
Prioritize Key Metrics: Providing users with too much information might not always be the best idea. Prioritize and draw attention to the dimensions which are most relevant for the analysis.
Interactive Features: Users should be able to zoom into a particular analysis or even filter the data by different dimensions as needed.
Security Considerations: Due to their nature, the trust and safety dashboards might involve working with delicate information, there should always be strong measures in place for controlling access, and encrypting Personal Identifiable Information(PII). Conclusion Building a trust and safety dashboard is a crucial process, especially when aiming to protect an organization’s digital integrity. With data visualization tools, businesses can build solutions for their platforms and help teams improve by giving them meaningful information. For any entrepreneur, whether they are new to the market or have been in the market for some time, such an investment is an investment trust, safety, and a positive future. Today, the need to preserve the confidence and security of people as well as uphold the integrity of enterprises, and organizations is critical. To effectively monitor such aspects of the behavior of the users, or to identify risks that possibly exist, a dashboard is most important. To facilitate the process, businesses can use special data visualization tools applied to build trust and safety dashboards. This article discusses how such dashboards could be created and the advantages of creating one. What is a Trust and Safety Dashboard? A trust and safety dashboard is an interface that can represent the data collected around different aspects of user’s behavior in an easy to understand manner. These dashboards can assist organizations in monitoring and detecting policy violations, identifying prohibited content impressions and volume, observe attempts to bypass policy. For example, an e-commerce website might want to use these dashboards to identify frauds, people trying to scam the business and identify patterns that might give insights into the security aspects of transactions. The main challenge of creating a trust and safety dashboard is big data management - making the data easy to understand and represent in an actionable format. The Role of Data Visualization in Trust and Safety Business intelligence tools take data and bring it to life by creating a picture in form of a graph, chart or heatmap. This facilitates the identification of patterns, trends as well as outliers enabling decisions-making much faster. For trust and safety purposes, visualizations can: Highlight Anomalies: Identify user’s irregularities or sharp increase in suspicious content emitted by users. Track Metrics Over Time: Track changes in near-misses or accident rates, complaints or feedback from users, or breaches of compliance regulations. Segment Data by Categories: Identify problems based on the location or the demographic or the type of different policy violations. Facilitate Real-Time Monitoring: Real-time information can be analyzed proactively to counter any risk factors. Highlight Anomalies: Identify user’s irregularities or sharp increase in suspicious content emitted by users. Highlight Anomalies : Identify user’s irregularities or sharp increase in suspicious content emitted by users. Highlight Anomalies Track Metrics Over Time: Track changes in near-misses or accident rates, complaints or feedback from users, or breaches of compliance regulations. Track Metrics Over Time : Track changes in near-misses or accident rates, complaints or feedback from users, or breaches of compliance regulations. Track Metrics Over Time Segment Data by Categories: Identify problems based on the location or the demographic or the type of different policy violations. Segment Data by Categories : Identify problems based on the location or the demographic or the type of different policy violations. Segment Data by Categories Facilitate Real-Time Monitoring: Real-time information can be analyzed proactively to counter any risk factors. Facilitate Real-Time Monitoring: Real-time information can be analyzed proactively to counter any risk factors. Facilitate Real-Time Monitoring: Steps to Develop a Custom Trust and Safety Dashboard Define Objectives and Metrics First of all, you should define the main objectives of using the selected dashboard. Are you looking for fraud detection, content moderation, or to ensure users’ data security? After objectives are defined, determine the metrics to track, whether it is flagged content, logins, or support tickets. Gather and Prepare Data Use activity logs, complaint records, or external APIs. Clean the data to make it precise and fit for analysis. Tools like Alteryx or ETL tool pipelines can help ease this process. Select a Visualization Tool Select a tool that will suit your needs and may not require technical skills. For non-technical teams, there is Power BI or Tableau where they can easily use the drag-and-drop features. For more customization, developers may go for Python libraries like Matplotlib or Plotly. Design the Dashboard Layout In design, it is always most important to ensure that the final product is clear and easy to use. Similar attributes should be grouped in one section, the use of appropriate color schemes, and the most important information should be easily and pointed out properly. Stay away from gaudiness, and hold simplicity. Implement Real-Time Data Updates For a dynamic environment, it is necessary to incorporate tools for live data feed so that the changes can be reflected in the dashboard. For this, there are technologies such as Apache Kafka or WebSockets. Test and Iterate Notify stakeholders about the created dashboard and collect feedback. Analyze the feedback to identify patterns regarding the usefulness of the visualizations, change metrics, or even the overall topology. Best Practices for Effective Dashboards User-Centric Design: Ensure that the end-user view of the dashboard is most appropriate for their needs. For instance, a compliance office will require different metrics, and a cybersecurity analyst will have different needs. Prioritize Key Metrics: Providing users with too much information might not always be the best idea. Prioritize and draw attention to the dimensions which are most relevant for the analysis. Interactive Features: Users should be able to zoom into a particular analysis or even filter the data by different dimensions as needed. Security Considerations: Due to their nature, the trust and safety dashboards might involve working with delicate information, there should always be strong measures in place for controlling access, and encrypting Personal Identifiable Information(PII). User-Centric Design : Ensure that the end-user view of the dashboard is most appropriate for their needs. For instance, a compliance office will require different metrics, and a cybersecurity analyst will have different needs. User-Centric Design Prioritize Key Metrics : Providing users with too much information might not always be the best idea. Prioritize and draw attention to the dimensions which are most relevant for the analysis. Prioritize Key Metrics Interactive Features : Users should be able to zoom into a particular analysis or even filter the data by different dimensions as needed. Interactive Features Security Considerations : Due to their nature, the trust and safety dashboards might involve working with delicate information, there should always be strong measures in place for controlling access, and encrypting Personal Identifiable Information(PII). Security Considerations Conclusion Building a trust and safety dashboard is a crucial process, especially when aiming to protect an organization’s digital integrity. With data visualization tools, businesses can build solutions for their platforms and help teams improve by giving them meaningful information. For any entrepreneur, whether they are new to the market or have been in the market for some time, such an investment is an investment trust, safety, and a positive future.

Developing a Custom Trust and Safety Dashboard Using Data Visualization Tools

The vast majority of modern systems are built on a microservice architecture. This means that you can be the maintainer of about a hundred services, which are being worked on by several teams of software engineers. As the person responsible for the system, it is important for you to monitor the relevance of the master branch of all projects. One of the easiest and fastest ways to do this is to check how much your main project branch lags behind the test branch after the release. Thanks to this simple check, you will be sure that the teams have brought all the services to production. Also, a quick report on the lag of the master branch from dev will be useful for understanding the state of development. If more than a few hundred commits have accumulated in the dev branch, then this is a red flag and you, as a team lead, should raise this issue at the sprint retro, because it is obvious that there were interruptions in the process of delivering improvements to production. To solve the problem of quickly viewing ahead/behind my projects, I use the rest API for GitLab (https://docs.gitlab.com/ee/api/rest/), which provides me with all the necessary capabilities. As a language, I chose JavaScript without frameworks, because we have a fast development for ourselves, which should not require a special environment. As a result of the script's work, I want to see a table with columns in the console: group_id - gitlab project group id
group_name - the name of the project group
id - project id
name - project name
behind - how many commits is the master branch ahead of the dev
ahead - how many commits is the dev branch ahead of the master To access the data, I will use a personal access token, which can be obtained in the settings of my GitLab profile. First, I'll get a list of groups using the/api/v4/groups method. After that, I need to get a list of project repositories using the /api/v4/groups/${group.id}/projects method. All that remains is to get the number of commits using the /api/v4/projects/${project.id}/repository/compare method. Next, I collect the data in a table, perform a sorting that is convenient for me, and highlight problematic lines. To perform REST requests, I use axios, and to output a table with data, I use the console-table-printer project. As a result, I got the following script. const { Table } = require('console-table-printer');
const axios = require('axios');

const token = token';
const baseUrl = 'https://git.my-company.net';
const from = 'master';
const to = 'dev';

(async () => {
    let data = [];
    const groups = await axios
        .get(`${baseUrl}/api/v4/groups?private_token=${token}`)
        .then(res => res.data);

    await Promise.all(groups.map(async (group) => {
        const projects = await axios
            .get(`${baseUrl}/api/v4/groups/${group.id}/projects?per_page=100&private_token=${token}`)
            .then(res => res.data);

        await Promise.all(projects.map(async (project) => {
            const behind = await axios
                .get(`${baseUrl}/api/v4/projects/${project.id}/repository/compare?from=${from}&to=${to}&private_token=${token}`)
                .then(res => res.data)
                .catch(err => {});
            const ahead = await axios
                .get(`${baseUrl}/api/v4/projects/${project.id}/repository/compare?from=${to}&to=${from}&private_token=${token}`)
                .then(res => res.data)
                .catch(err => {});
            if (behind && behind.commits && ahead && ahead.commits){
                data.push({
                    group_id: group.id,
                    group_name: group.name,
                    id: project.id,
                    name: project.name,
                    behind: behind.commits.length,
                    ahead: ahead.commits.length,
                });
            }
        }));
    }))

    data = data
        .sort((a,b) =>
            (a.group_name > b.group_name)
                ? 1
                : ((b.group_name > a.group_name) ? -1
                    : (a.name > b.name)
                        ? 1
                        : ((b.name > a.name) ? -1 : 0)));

    const table = new Table();
    data.forEach((row) => {
        table.addRow(row, {
            color: row.behind ? 'red' : undefined,
        });
    });
    table.printTable();
})(); After its execution, the following table is output to the console: group_id

group_name

id

name

behind

ahead



235

common

34

common_service

5

2



235

common

455

master_data

0

0



379

core

453

core_service

7

0



379

core

344

core_tasks

3

1 The vast majority of modern systems are built on a microservice architecture. This means that you can be the maintainer of about a hundred services, which are being worked on by several teams of software engineers. As the person responsible for the system, it is important for you to monitor the relevance of the master branch of all projects. One of the easiest and fastest ways to do this is to check how much your main project branch lags behind the test branch after the release. Thanks to this simple check, you will be sure that the teams have brought all the services to production. Also, a quick report on the lag of the master branch from dev will be useful for understanding the state of development. If more than a few hundred commits have accumulated in the dev branch, then this is a red flag and you, as a team lead, should raise this issue at the sprint retro, because it is obvious that there were interruptions in the process of delivering improvements to production. To solve the problem of quickly viewing ahead/behind my projects, I use the rest API for GitLab ( https://docs.gitlab.com/ee/api/rest/ ), which provides me with all the necessary capabilities. https://docs.gitlab.com/ee/api/rest/ As a language, I chose JavaScript without frameworks, because we have a fast development for ourselves, which should not require a special environment. As a result of the script's work, I want to see a table with columns in the console: group_id - gitlab project group id group_name - the name of the project group id - project id name - project name behind - how many commits is the master branch ahead of the dev ahead - how many commits is the dev branch ahead of the master group_id - gitlab project group id group_name - the name of the project group id - project id name - project name behind - how many commits is the master branch ahead of the dev ahead - how many commits is the dev branch ahead of the master To access the data, I will use a personal access token, which can be obtained in the settings of my GitLab profile. First, I'll get a list of groups using the /api/v4/groups method. /api/v4/groups After that, I need to get a list of project repositories using the /api/v4/groups/${group.id}/projects method. /api/v4/groups/${group.id}/projects All that remains is to get the number of commits using the /api/v4/projects/${project.id}/repository/compare method. /api/v4/projects/${project.id}/repository/compare Next, I collect the data in a table, perform a sorting that is convenient for me, and highlight problematic lines. To perform REST requests, I use axios , and to output a table with data, I use the console-table-printer project. As a result, I got the following script. axios console-table-printer const { Table } = require('console-table-printer');
const axios = require('axios');

const token = token';
const baseUrl = 'https://git.my-company.net';
const from = 'master';
const to = 'dev';

(async () => {
    let data = [];
    const groups = await axios
        .get(`${baseUrl}/api/v4/groups?private_token=${token}`)
        .then(res => res.data);

    await Promise.all(groups.map(async (group) => {
        const projects = await axios
            .get(`${baseUrl}/api/v4/groups/${group.id}/projects?per_page=100&private_token=${token}`)
            .then(res => res.data);

        await Promise.all(projects.map(async (project) => {
            const behind = await axios
                .get(`${baseUrl}/api/v4/projects/${project.id}/repository/compare?from=${from}&to=${to}&private_token=${token}`)
                .then(res => res.data)
                .catch(err => {});
            const ahead = await axios
                .get(`${baseUrl}/api/v4/projects/${project.id}/repository/compare?from=${to}&to=${from}&private_token=${token}`)
                .then(res => res.data)
                .catch(err => {});
            if (behind && behind.commits && ahead && ahead.commits){
                data.push({
                    group_id: group.id,
                    group_name: group.name,
                    id: project.id,
                    name: project.name,
                    behind: behind.commits.length,
                    ahead: ahead.commits.length,
                });
            }
        }));
    }))

    data = data
        .sort((a,b) =>
            (a.group_name > b.group_name)
                ? 1
                : ((b.group_name > a.group_name) ? -1
                    : (a.name > b.name)
                        ? 1
                        : ((b.name > a.name) ? -1 : 0)));

    const table = new Table();
    data.forEach((row) => {
        table.addRow(row, {
            color: row.behind ? 'red' : undefined,
        });
    });
    table.printTable();
})(); const { Table } = require('console-table-printer');
const axios = require('axios');

const token = token';
const baseUrl = 'https://git.my-company.net';
const from = 'master';
const to = 'dev';

(async () => {
    let data = [];
    const groups = await axios
        .get(`${baseUrl}/api/v4/groups?private_token=${token}`)
        .then(res => res.data);

    await Promise.all(groups.map(async (group) => {
        const projects = await axios
            .get(`${baseUrl}/api/v4/groups/${group.id}/projects?per_page=100&private_token=${token}`)
            .then(res => res.data);

        await Promise.all(projects.map(async (project) => {
            const behind = await axios
                .get(`${baseUrl}/api/v4/projects/${project.id}/repository/compare?from=${from}&to=${to}&private_token=${token}`)
                .then(res => res.data)
                .catch(err => {});
            const ahead = await axios
                .get(`${baseUrl}/api/v4/projects/${project.id}/repository/compare?from=${to}&to=${from}&private_token=${token}`)
                .then(res => res.data)
                .catch(err => {});
            if (behind && behind.commits && ahead && ahead.commits){
                data.push({
                    group_id: group.id,
                    group_name: group.name,
                    id: project.id,
                    name: project.name,
                    behind: behind.commits.length,
                    ahead: ahead.commits.length,
                });
            }
        }));
    }))

    data = data
        .sort((a,b) =>
            (a.group_name > b.group_name)
                ? 1
                : ((b.group_name > a.group_name) ? -1
                    : (a.name > b.name)
                        ? 1
                        : ((b.name > a.name) ? -1 : 0)));

    const table = new Table();
    data.forEach((row) => {
        table.addRow(row, {
            color: row.behind ? 'red' : undefined,
        });
    });
    table.printTable();
})(); After its execution, the following table is output to the console: group_id

group_name

id

name

behind

ahead



235

common

34

common_service

5

2



235

common

455

master_data

0

0



379

core

453

core_service

7

0



379

core

344

core_tasks

3

1 group_id

group_name

id

name

behind

ahead 235

common

34

common_service

5

2 235

common

455

master_data

0

0 379

core

453

core_service

7

0 379

core

344

core_tasks

3

1 group_id group_name id name behind ahead group_id group_id group_name group_name id id name name behind behind ahead ahead 235 common 34 common_service 5 2 235 235 common common 34 34 common_service common_service 5 5 2 2 235 common 455 master_data 0 0 235 235 common common 455 455 master_data master_data 0 0 0 0 379 core 453 core_service 7 0 379 379 core core 453 453 core_service core_service 7 7 0 0 379 core 344 core_tasks 3 1 379 379 core core 344 344 core_tasks core_tasks 3 3 1 1

Git Ahead Dashboard: A Quick Guide

These tools do more than just display data; they help in quickly reading, analyzing, and making informed decisions, which is critically important for both developers and businesses. A well-designed dashboard can become a key element in forecasting trends and anticipating outcomes, impacting the efficiency and motivation of the team. Hello! My name is Lisa, and I have worked as a product designer in large IT companies and small startups. Among my experience was the creation of internal products that help monitor metrics and the state of service operations. Thanks to this, I have accumulated significant experience in dashboard development, and today I want to share this experience with you. Today, I want to inspire you to create useful and effective dashboards by sharing my approach and emphasizing the importance of UI design in this process. The sense of dashboard I want to start with three fundamental principles that should guide the creation of dashboards. These are precisely what you, as a product manager or product designer, should advocate for: One of the less obvious but critically important tasks in dashboard creation is the ability . Visualization should not only display numbers but also convey context, show cause-and-effect relationships, and identify trends. This requires a deep understanding of how data correlates with real business processes and user behavior. Dashboard developers should aim not only to present data but also to explain it, creating an intuitive and engaging narrative structure. to tell a story with data Often in the dashboard development process, you may encounter the issue of “expectation overload” — when users want to see too many features and data. It’s important to be able to manage these expectations, explaining limitations and focusing on the most important aspects that truly add value. This might require communication and persuasion skills, as well as the ability to and the impact of various features on the final product. objectively assess priorities Technical and organizational obstacles often become a serious challenge. For instance, integrating data from various sources can be a complex task due to format incompatibilities or API limitations. In such cases, it’s crucial to to find solutions that ensure smooth integration and reliable system performance. It’s also important to consider organizational aspects, such as training the team to use the new dashboard and implementing it into work processes. closely collaborate with developers Goals Every successful dashboard begins with a clear understanding of its goals and audience. At my current job, before starting the development of a dashboard, we conduct . This could be, for example, monitoring key performance indicators for executives or providing technical data for the development team. an analysis of the objectives it needs to achieve and the audience it is intended for To illustrate, in one of our projects, the goal was to create a dashboard for monitoring server operations. The audience consisted of technical specialists who required detailed, real-time information about the system’s status. The goals and audience defined the structure and design of the dashboard, making it maximally informative and user-friendly for the end users. Moreover, an effective dashboard helps reduce risks for the company by providing For instance, thanks to the instant display of server downtime data, the team can quickly respond to issues, minimizing downtime and potential losses. quick access to critically important information. Equally important is the impact of the dashboard on the team’s motivation and awareness. When the team sees a dashboard tailored specifically for them with up-to-date metrics every day, it enhances their engagement and provides a clear understanding of the current state of the project. This creates a sense of control and confidence in the decisions being made, which in turn contributes to an overall increase in work efficiency. Users Before beginning the design of a dashboard, it’s critically important to understand . In my practice, this starts with a deep dive into the world of the end-users — developers, project managers, data analysts, and so on. I conduct interviews, analyze workflows, and gather feedback to understand their daily tasks and challenges. who the primary users will be and what tasks they will be addressing For example, might need real-time system performance monitoring, while may require an overview of task completion timelines and resources. By identifying these key usage scenarios, I can ensure that the dashboard will not only be functional but also maximally useful for specific users. developers project managers Next, I link the functionality of the dashboard to the overall It’s not just about presenting data; it’s about how these data can help achieve specific objectives. For instance, if a business goal is to increase efficiency, then the dashboard should highlight metrics that track progress and identify bottlenecks. business goals of the company. In one of the projects, the goal was to improve customer service quality. For this, we developed a dashboard that displayed key indicators in real-time related to the processing of customer inquiries, allowing the team to respond promptly to issues and improve service quality. Thus, understanding both user needs and business goals not only displays data but actively contributes to achieving the organization’s ultimate goals. Strategy When we start defining the key functions and priorities of a dashboard, it’s important to find a balance between what users want and what is truly needed to achieve business goals. In my experience, I begin by creating a list of features that could be useful and then prioritize them based on their impact on users and business objectives. For instance, for a dashboard designed for monitoring application performance, key features might include displaying system response time, code errors, and server load levels. This is not just about having “nice-to-have” features; it’s about creating a “must-have” list that is guided by strategic goals and the real needs of the user. In developing a dashboard, I always think about how it will fit into the broader context of the product. 👾How will it interact with other features? 📎 How will it help the user better understand and use the product as a whole? For example, if a dashboard is designed for a CRM system, it should not only provide data but also simplify interactions with customers and improve processes for working with them. In this way, we make the dashboard . a valuable addition, not just an isolated tool Design. Prototyping. Iterations. I start with basic sketches that allow for a quick assessment of ideas and structure. These sketches are more than just drawings; they help me and my team visualize the dashboard concept and initiate a dialogue about how it should function. Once the concept is approved, I move on to creating more detailed prototypes. I develop prototypes that not only resemble the final product but also . This is a critically important step as it allows for testing with real users and gathering feedback that will influence subsequent design iterations. give an insight into user interaction When designing the dashboard interface, my main goal is to create an intuitive and effective user experience. Here are a few key principles I always keep in mind: 🎧 Clarity and Noise Reduction It’s important that users can quickly understand what is displayed on the dashboard. I aim to avoid information overload, focusing on key metrics and data necessary for the specific audience. 🧭 Consistency and Predictability I use consistent design elements and layouts so that users can intuitively understand how to interact with the dashboard. This includes uniformity in colors, fonts, and layouts. 💠 Interactivity and Feedback The dashboard should be informative as well as interactive. I include elements such as filters and dropdown menus that allow users to customize data display according to their preferences. 🍟 Accessibility and User-Friendliness I believe that design should be accessible to all users. This includes considering people with different abilities and preferences, as well as ensuring that the dashboard is user-friendly on various devices. Data Visualization and Information Architecture One of the key aspects of creating effective dashboards is the proper selection of data . Each data type requires its own approach to presentation to ensure the information is understandable and easily digestible. visualization methods For example, line charts are ideal for time series as they clearly illustrate the dynamics of changes. Bar or pie charts work well for comparing values across categories. Heat maps can be useful in visualizing complex datasets, allowing for quick identification of patterns and anomalies. It’s important to remember that visualization should be not only informative but also . I use colors and shapes to make data more accessible and comprehensible, while avoiding overloading with visual information. appealing Additionally, I focus on and ensuring a consistent flow of information, which aids users in easily navigating and finding the data they need. For instance, in a dashboard designed for sales analysis, I might group data related to customer demographics, sales figures, and product performance in distinct but interconnected sections. This way, a sales manager can quickly locate specific insights, such as the best-selling products in a particular demographic segment. grouping related data Another example is a financial dashboard where I categorize data into revenue, expenses, and profit margins. By doing so, financial analysts can effortlessly switch between different financial aspects without losing track of the overall financial health of the company. These thoughtful groupings and layouts not only improve the usability of the dashboard but also enhance the decision-making process by presenting a coherent narrative of the data. Testing and Iteration To identify usability issues and understand how well the dashboard meets users’ needs and expectations, we showcase our developments to real users. Based on the feedback collected, the process of iterations begins. The key is to be the design in response to the data received. Iterations can affect both minor details of the interface and larger aspects of structure and functionality. open to changes and ready to adapt In one of our recent projects on creating a dashboard for server performance monitoring, user testing became a pivotal moment. After the initial development of the dashboard, we conducted a series of tests with our development team, who were the primary users of the product. During these sessions, I observed how they interacted with the dashboard, . A key issue was that the developers had trouble interpreting the performance time series. The charts were overloaded with data, making it difficult to quickly analyze the situation. noting any difficulties or misunderstandings Based on this initial testing, we began the process of iterative improvements. Our first step was to simplify the charts by highlighting the most critical metrics and reducing visual clutter. We also added the ability to filter data by time intervals, allowing developers to focus more easily on specific periods. After implementing these changes, we conducted , this time with a broader group of users, including project managers. The feedback was significantly more positive: the charts became clearer, and the data filtering feature was particularly appreciated for its usefulness. a second round of testing Product Integration The next key stage is the integration of the dashboard into the overall product. In my project of creating a dashboard for server monitoring, one of the main tasks was to ensure its . This required close collaboration with the development team to ensure that the dashboard accurately collects and displays data in real-time. seamless integration with the existing system We utilized APIs to connect the dashboard to various data sources, ensuring high performance and reliability. Work was also done to optimize data loading so that the dashboard could respond quickly to user queries, even when processing large volumes of information. It’s crucial that the dashboard not only meets current needs but can also . To this end, we developed a modular architecture that allows for the easy addition of new features and the integration of additional data sources. Ensuring continuous support and updates for the dashboard was also an important part of the process, guaranteeing its stability and the relevance of its data. adapt to future changes and expansions of the system Creating dashboards is an integral part of product design. They not only provide valuable information for decision-making but also significantly enhance user convenience and efficiency. My experience has shown that a well-designed dashboard can have a substantial impact on business, improving processes and aiding in the achievement of goals. Tips and Tricks for Product Designers Start by figuring out what your users and . It’s like being a detective, but instead of solving crimes, you’re solving design puzzles. business really need Regularly . Be ready to iterate based on the feedback test the dashboard with real users Aim for in data visualization. Avoid information overload and focus on the key metrics. simplicity Develop dashboards with and potential data or process changes in mind. future scaling Postscript Alternative Ways to Create Dashboards: No-Code and Low-Code Solutions In the modern world of product design, there is an increasing number of . These no-code and low-code platforms offer convenient means for quick and efficient dashboard development, making them accessible not just for professional developers but also for business analysts, marketers, and other specialists. tools that allow for dashboard creation without deep programming knowledge Platforms: is  a powerful platform for creating interactive dashboards, particularly valuable for monitoring and analyzing time-series data. Ideal for real-time system monitoring, network analysis, and IoT applications, Grafana supports multiple data sources, including Prometheus, MySQL, PostgreSQL, and many others. This tool is highly flexible in visualization customization, allowing for detailed and adjustable dashboards. However, it may require deeper technical knowledge for setup and use compared to other no-code/low-code solutions. Grafana is one of the most popular platforms for data visualization. It offers robust tools for creating a variety of dashboards with the ability to connect to different data sources. Tableau makes it easy to integrate data from various sources and create interactive dashboards. It is ideal for integration with other Microsoft products, such as Excel and Azure. Microsoft Power BI is a free solution from Google that allows for creating customizable dashboards. Particularly well-suited for working with data from Google Analytics, Google Ads, and other Google services. Google Data Studio : This tool provides flexible options for dashboard creation and business analytics. It enables not just data visualization but also in-depth analysis. Looker Using no-code and low-code platforms has its advantages, including . However, one should consider their limitations in terms of customization and integration with some complex systems. Therefore, it’s important to assess the needs of the project and choose a tool that best meets these requirements. development speed, ease of use, and flexibility in design alteration

Creating the Perfect Dashboard: Visualizing Your Metrics

নিখুঁত ড্যাশবোর্ড তৈরি করা: আপনার মেট্রিক্স ভিজ্যুয়ালাইজ করা

Erstellen des perfekten Dashboards: Visualisierung Ihrer Metriken

Creando el panel perfecto: visualizando tus métricas

Créer le tableau de bord parfait : visualiser vos métriques

परफेक्ट डैशबोर्ड बनाना: अपने मेट्रिक्स को विज़ुअलाइज़ करना

完璧なダッシュボードの作成: 指標の視覚化

완벽한 대시보드 만들기: 측정항목 시각화

Criando o painel perfeito: visualizando suas métricas

Создание идеальной информационной панели: визуализация показателей

Mükemmel Kontrol Panelini Oluşturma: Metriklerinizi Görselleştirme

Tạo Trang tổng quan hoàn hảo: Trực quan hóa các chỉ số của bạn

创建完美的仪表板：可视化您的指标

Hi! My name is Mava. Since 2017 I have been interested in exchanges, stocks, etc. At the beginning of 2019, I was completely overwhelmed by the wave of crypto hype. In this story, I will tell you how I created a crypto dashboard, which was born from the simple idea of ​​calculating profit. At first, I traded manually, then I used trading bots. One bot - I didn’t like the management. The other one was awkward to set up. The third was quite suitable, with its inconveniences, but I could live with it. The fourth one was the most successful! And it was cool: trade was going on, there was a profit! And only 99% remained until the Lambo (just kidding). I tried several bots and even used one of them until now, they all had the same problem: they did not show profit they did not show the average price of my assets. they did not have any statistics on my trading. The exchange can give you PNL or a list of your transactions in Excel and that’s it. And what should I do about it? What is my profit? How much is invested now? What is the value of the assets at market prices? Therefore, I made myself an Excel file into which I transferred the list of completed transactions. I wrote formulas so that based on these transactions, it was possible to calculate the profit for each coin. I also added a loading of coin prices to understand what the average cost of the coins I have is. It turned out to be a huge file that needed to be updated constantly. But it showed me the information I needed. I switched the bot from one coin to another because I saw that it did not bring profit. I realized how badly I was screwing up with another coin whose trading needed to be stopped. I could calculate the amount needed to average to break even. And all this time, in the bot support chat, I saw users asking the developers “How to calculate profit? How to calculate the average price? How can I average?”. The response was vague answers that made no sense. So I decided to "make my Excel file accessible to everyone" - I decided to create a service that would help people understand what was happening with their trading, what was happening with their assets, and what their options were. I gave up trading and devoted myself entirely to creating a service. My dear wife has created a name for this service - TotalProfit. It was the most suitable name that fully revealed the idea of the service! I found a programmer who is helping me create a service. The two of us have been doing this for quite a long time because I don’t have investors to hire several freelancers - I do everything with my funds. I came up with a logo that has already changed ten times and now this is exactly what I need, which does not bother me to this day. It's funny, but instead of finishing current work, I constantly come up with new features for the project. During the creation of the project, it acquired other auxiliary functions: from a simple calculation of profit, it turned into a large crypto dashboard, with a portfolio chart, profit charts, trading volume charts, calculators, a portfolio that shows the real value of assets with profit or losses, as well as averaging options, convenient viewing of trading history and so on. And now, I understand that I have reached the MVP level. But I need to go further - not all my ideas are brought to life in this project: I need to implement other exchanges (now only one for testing time), I need to check the performance of the service under load, and so on. In plans, we will add tax calculations according to different schemes: LIFO, FIFO, and average; futures trading analysis, notifications in telegrams about the price reaching a particular level and much more. I have too much at stake to stop! But I can already see that the project is alive, and working and has already started helping people!

From Crypto Trader to Dashboard Creator

Dashboard software has become a necessary tool for businesses. With all the information you must gather and monitor about your business, you may wonder how to get the most out of it. Actionable insight can help you gain a competitive edge, considering the gap between data and business value. In fact, in 2023, achieved measurable value from and analytics investments, which reinforces the need for data-driven decision-making. 91.9% of organizations data to ensure you understand your data and benefit from it. Dashboard software merges data from linked databases and provides a customizable and interactive platform for data visualization Additionally, it empowers everyone in your company, from a data analyst to a junior manager, allowing them to visualize, present, and use data in any way that makes sense to them. In this article, we’ll explore the most eminent benefits of dashboard software and how you can leverage them to get the most out of your investment. 1. Effective Data Visualization Businesses gather huge amounts of data. Understanding such amounts of data can be challenging and time-consuming, to say the least, necessitating the need for tools that can help organize your data in a manner you can comprehend. When this is not possible, your business may experience challenges in making decisions and executing them in time. How can you make decisions based on data communicated ineffectively? Similarly, . This leads to a lot of time wasted trying to organize and present huge amounts of data. without data visualization, your employees may not know where to focus their attention to improve productivity One of the benefits of dashboard software is its ability to transform your business data into easy-to-understand graphs, images, and charts, which are essential considering that the human brain processes visual information more readily than textual information. Actually, the use of visual language is proven to enhance effectiveness and efficiency, leading to 24% shorter meetings. It is safe to conclude that this statistic is only possible because visualization enables data to be easily communicated and understood. The software interprets and simplifies the data for you to help generate true value for your business. It becomes easier to make decisions, recognize patterns and errors, and improve data analysis. GenerationE is an IT solutions provider that captures and automates best practices for IT operations by up to 85%. InetSoft provided a visualization layer for GenerationE, helping the company and saving its team the effort of charting and analyzing log data in external reporting systems. monitor the performance of automated processes 2. Improves Decision Making Great data, when analyzed correctly, can help enhance decision-making. It is important to gather and analyze business data to understand key business metrics and your performance to help you make better decisions to upscale. Without good dashboard software, your business may experience challenges collecting and managing data as it is scattered across multiple databases. Even if you can access this data from siloed systems, moving and analyzing it manually takes time and can impede accurate reporting and fast decision-making. Slow and inefficient decision-making can negatively impact your business in 2024 by costing you missed opportunities, time, and money. Dashboard software merges data from different sources and provides immediate access to at-a-glance information, ensuring you make decisions quickly to avoid missing out on opportunities. Additionally, with clear, actionable insights, you can make more accurate data-driven decisions and create business-boosting strategies to give you a competitive edge. In fact, Insight-driven businesses are growing at an average of , according to a report by Forrester. 30% each year This figure summarizes the when it comes to decision-making. Data in the digital era is extremely critical, and it helps to have the right tools to help you leverage. benefits of dashboard software Kreditech is a financial company based in Germany that offers clients loans based on their creditworthiness. Data analysis and decision-making are therefore critical. After incorporating Datapine, Kreditech was able to decentralize decision-making and achieve faster and smarter data-driven decisions. Thanks to the software’s friendly interface, users were able to work with zero IT overhead. 3. Increases Business Efficiency Enhancing business efficiency can help grow your business. It is undeniable how critical business efficiency is to grow and survive in the current economic climate. Without dashboard software, your business risks inefficiencies due to siloed information, manual data entry processes, ineffective data organization, and presentation. To err is human, so inefficiencies can frequently occur without the right tools to streamline business intelligence operations. Manual processes can also lead to data errors that are more prevalent when dealing with large amounts of data. In fact, Gartner found out that organizations believe poor data quality to be responsible for per year in losses. an average of $15 million For starters, reducing manual tasks is a key component for successful business management and plays a huge role in increasing efficiencies. It is a no-brainer that automation technologies like dashboard software can achieve more than a human and deliver more efficient results through workflow automation. A dashboard software offers smart, autonomous data management solutions to help you save time and money by gathering quality insights. Additionally, dashboard software brings data from different sources into a central respiratory system, eliminating the need for multiple external solutions. Therefore, you can be able to take quick action on your data-driven insights to upscale your business. Parcero is a Columbian marketing agency that was looking for a solution to help save time on manual processes. After implementing Klipfolio, Parcero was able to automate client reporting and enhance efficiency. They recorded in repetitive advertising tasks and 73% in repetitive reporting tasks. 33% time savings 4. Empowers End-Users Dashboard software empowers your employees to be self-reliant. The amount of time wasted waiting for data analysts and IT teams to present and explain data is a lot. This is not to mention that it distracts them from focusing on their job to advance the business’s goals. Unfortunately, there remains a huge gap between having data and being able to use it. Even with huge amounts of data, most businesses need help to take action on the valuable insight because they cannot interpret it. In fact, struggle with utilizing their , according to Talend. 78% of executives data for decision-making Imagine how much you could achieve if your employees could access and interpret data on their own without relying on data analysts. Well, one of the in business intelligence is empowering end-users by providing a simplified and clear platform where they can access data and manipulate it in a way that makes sense to them. benefits of dashboard software This ensures enhanced productivity, prompt decision-making, and minimized costs thanks to the ability to get answers independently without needing help from the BI or IT team. This also allows your IT team to focus on their job without being distracted by frequent help requests. GMS is a leading distributor of construction products based in Georgia. The company was looking for an easy-to-implement solution to help users access valuable insights. Thanks to InetSoft, GMS users can based on data consolidated into interactive reports that are easy to read. make quick business decisions 5. Reduces Operating Costs Dashboard software can minimize business costs. Without a great dashboard software in 2024, data remains fragmented across multiple systems and databases, impeding prompt decision-making. Siloed data can result in expensive operational costs caused by inefficiencies, analysis, and manual data collection. . Without , your employees may spend hours fetching and trying to interpret data. Time is money, and when you are losing both, you could be laying a foundation for your business’s failure the right tools In fact, Airtable’s research revealed that knowledge workers reported spending nearly 29% of their week (11.6 hrs) searching for the key information they need to do their work. Conversely, implementing dashboard software provides a central repository that combines and organizes data from different sources to provide clear visibility of key metrics. . It also provides a unified view that lets you easily identify cost-saving optimization opportunities and budget allocation opportunities In addition to that, dashboard software capabilities such as automation help to eliminate manual and repetitive tasks that can be time-consuming. Consequently, this reduces the chances of inefficiencies and errors that could cost you money. Dashboard software significantly reduces operational expenses by identifying inefficiencies promptly, optimizing resource allocation, and making data-driven decisions, ultimately contributing to substantial cost savings. Just Spices is a German-based company that manufactures spices. The company had been investing a lot of time in extracting and analyzing data for a while. With Klipfolio, Just Spices as they could access and visualize key metrics that show different departments where help is needed instead of having people extract data. saved time and money Conclusion In the digital age, data is extremely essential for businesses. How you use the business data you gather can influence the success of your business. However, with vast amounts of data, it can be challenging even for experts to organize and leverage data to get the best out of it. Dashboard software benefits such as reduced costs, enhanced efficiency, and clear visibility into key metrics can set your business apart. Actually, according to Capgemini, say its company’s Big Data initiatives are profitable. 27% of business executives Investing in a great dashboard software in 2024 can empower small businesses with enterprise-level insight without exaggerated costs.

5 Benefits of Dashboard Software for Business Intelligence in 2024

In a data-intensive industry like finance, data comes from numerous entries and goes to numerous exits. Such status quo can easily, and almost inevitably, lead to chaos in data analysis and management. For example, analysts from different business lines define their own financial metrics in data reports. When you pool these countless reports together in your data architecture, you will find that many metrics overlap or even contradict each other in definition. The consequence is, developing a simple data report will require lots of clarification back and forth, making the process more complicated and time-consuming than it should be. As your business grows, your data management will arrive at a point when "standardization" is needed. In terms of data engineering, that means you need a data platform where you can produce and manage all metrics. That's your architectural prerequisite to provide efficient financial services. This article introduces the lifecycle of financial metrics in a database (in this case, ), from how they're produced to how they're efficiently presented in data reports. You will get an inside view of what's behind those fancy financial dashboards. Apache Doris Define New Metrics & Add Them to Your Database Fundamentally, metrics are fields in a table. To provide a more concrete idea of them, I will explain with an example in the banking industry. Banks measure the assets of customers by AUM (Assets Under Management). In this scenario, AUM is an , which is often a field in the source data table. On the basis of AUM, analysts derive a series of , such as "year-on-year AUM growth", "month-on-month AUM growth", and "AUM per customer". atomic metric derivative metrics Once you define the new metrics, you add them to your data reports, which involves a few simple configurations in Apache Doris: Developers update the metadata accordingly, register the base table where the metrics are derived, configure the data granularity and update frequency of intermediate tables, and input the metric name and definition. Some engineers will also monitor the metrics to identify abnormalities and remove redundant metrics based on a metric evaluation system. When the metrics are soundly put in place, you can ingest new data into your database to get your data reports. For example, if you ingest CSV files, we recommend the Stream Load method of Apache Doris and a file size of 1~10G per batch. Eventually, these metrics will be visualized in data charts. Calculate Your Metrics As is mentioned, some metrics are produced by combining multiple fields in the source table. In data engineering, that is a multi-table join query. Based on the optimization experience of an Apache Doris user, we recommend flat tables instead of Star/Snowflake Schema. The user reduced the query response time on tables of 100 million rows after such a change. from 5s to 63ms The flat table solution also eliminates jitter. Enable SQL Caching to Reduce Resource Consumption Analysts often check data reports of the same metrics on a regular basis. These reports are produced by the same SQL, so one way to further improve query speed is SQL caching. Here is how it turns out in a use case with SQL caching enabled. All queries are responded within 10ms; When computing 30 metrics simultaneously (over 120 SQL commands), results can be returned within 600ms; A TPS (Transactions Per Second) of 300 is reached, with CPU, memory, disk, and I/O usage under 80%; Under the recommended cluster size, over 10,000 metrics can be cached, which means you can save a lot of computation resources. Conclusion The complexity of data analysis in the financial industry lies in the data itself other than the engineering side. Thus, the underlying data architecture should focus on facilitating the unified and efficient management of data. Apache Doris provides the flexibility of simple metric registration and the ability of fast and resource-efficient metric computation. In this case, the user is able to handle 10,000 active financial metrics in 10,000 dashboards with 30% less ETL efforts.

Database in Fintech: How to Support 10,000 Dashboards Without Creating a Mess

A Guide to Effective Dashboard Design for DevOps and SRE The previous posts were about Phantom Metrics and Expensive Metrics . In this post, we’ll look at Unreadable Metrics and how to design effective dashboards. Dashboards are powerful tools for monitoring and troubleshooting your system. Too often, however, we run into an incident and jump to the dashboard, just to find ourselves drowning in endless data and unable to find what we need. This could be caused not just by the data overload, but also due to seeing too many or too few colors, inconsistent conventions, or the lack of visual cues. We then need to waste precious time deciphering the dashboard rather than investigating the case at hand and end up frustrated. Dashboards are a tricky thing. It’s not enough to just have all the data needed: the dashboard needs to be designed in a way that allows users easily access and interpret the data. It requires more than the engineering mindset to do it right. Take these two guiding principles: When designing a dashboard, , and . think like a UX designer keep it simple In this guide, we’ll go over the basics of effective dashboard design and how to create a dashboard that will make it easy for you to troubleshoot an incident. Understand Your Dashboard’s User Persona and Use Case Start with understanding the persona of the user who will utilize the dashboard. After all, you’re not writing the dashboard to yourself (unless you do). Adapt your dashboard to the familiarity and knowledge level of that persona. If needed, add static text inside the dashboard to provide background on the metrics, their meaning, and upstream origin, as well as potential downstream dashboards, runbooks, or other actions to consider. Equally important is to define the use case the dashboard aims to serve, and keep the dashboard design focused on the data and visualizations that serve this use case. If the dashboard grows too big and broad, consider splitting it into several more targeted dashboards. Taking an example from the Kubernetes domain, I may want to have a dashboard showing the pod status, and a separate dashboard showing the cluster status, and not mix the two. This guiding principle of focus applies to the individual panels and visualizations. Each visualization should have a clear question it comes to answer. You should be able to answer the defined question when looking at the visualization without doing much additional analysis in your head. Remember that . Too many visualizations and data points can be overwhelming and make it difficult to find the information you’re looking for. simplicity is key for dashboard design Keep your dashboard focused on your use case, and try to avoid adding unnecessary elements. Utilize the Right Data Visualizations The best dashboards use the right visualizations to convey the most important information. For example, a graph might be a better choice than a table for displaying trends over time, while a pie chart might be a better choice than a line chart for comparing proportions between different groups. Try to use the visualization that best conveys your data. Sometimes a single number or a visual status indicator is more helpful than a ton of line graphs and data points. For example, just showing the P99 (99th percentile) current latency as a single digit top-level figure over showing all 5 different percentiles over time. Remember: keep it simple. Image caption: Single number count of nodes and pods. Below is the trend over time graph to elaborate. Source: Logz.io Another option is to overlay the real-time single figure clearly on top of the trend graph to get both into a single visualization: Image caption: A line chart of latency over time, and the latest latency value overlayed. Source: Logz.io Create a Clean Layout With an Intuitive Flow A clean and uncluttered layout is essential for effective dashboard design. Avoid overcrowding the dashboard with too much information and ensure that the design is easy to read and understand. Organize the layout according to importance, from top to bottom and from left to right. That’s how our brain thinks and how we scroll through dashboards. Place the most important and high-level information, such as summary and aggregated data or SLO (service level objectives), at the top, and then finer-detail visualizations downwards (and to the right). If we look at the example of monitoring application performance, you may put the service's overall performance at the top, then drill down to the individual operations of that service below. Image caption: Top view of service performance metrics, then drill down into individual operations. Source: Logz.io Don’t overuse the scrolling and drill down. Try and fit dashboards in one screen to save the scrolling down and remain focused. This is especially important if this dashboard is meant to also be displayed on a central screen at the office. No matter what, avoid horizontal scrolling. Fit the dashboard into the screen width, and if it doesn’t fit the page, result to vertical scrolling only. Humans are wired to scroll down but can oftentimes miss parts that are to the sides. Remember, think UX. Keep the Consistency of the Layout Once you define a layout that works well, keep consistency with this layout, both within the same dashboard and across dashboards. Seeing similar information presented in different ways in different places can be very confusing. We all know that green is good and red is bad, right? You wouldn’t like to see blue signifying bad anywhere. Consistent color codes are one aspect, but it can also be a consistent graph type to visualize each data type, horizontal vs. vertical alignment, and so forth. Consistency of the panel dimensions within the dashboard is also important. We often detect patterns by visually correlating the behavior of the wiggly line in different visualizations over time. To ease this visual correlation, use the same panel width for graphs, and make sure the different panels automatically sync to show the same time window. Here’s an example of the Grafana dashboard showing a Cassandra NoSQL database cluster. The different graphs all have the same width and are all synchronized on the master time window of the dashboard (that is, in fact, the default behavior in Grafana OSS). In this layout, it is easy to correlate a spike in read latency with the drop in cache hit rate over the same time, as the panels are visually aligned. Image caption: Easy to see spike correlation using the same width and time frame. Source: Logz.io Correlate Between Different Dashboards and Views Correlation doesn’t end with panels and visualizations within the same dashboard. You may find yourself needing to correlate a certain metric with metrics found on other dashboards or drill down into a specialized dashboard to carry on the investigation. Or even correlate with other types of telemetry such as logs or traces. Take the time to incorporate the link to the referred dashboard to ease the transition with the existing investigative context. Some tools have built-in support for such links, such as Dashboard and Panel data links in Grafana open source, or Logz.io’s , which carries the search context (the time frame, filters, etc.) over to the next dashboard. telemetry correlation If you can’t incorporate links natively, make notes on the dashboard as a static data panel with relevant guidelines, markdown, and links. Annotate Thresholds, Alerts, and Events on the Graph Does your metric have known limits, goals, or threshold values to be aware of? Visualize them as horizontal markers on the graph. Classic examples are maximum memory or disk capability or the error budget. You can also annotate reference numbers such as last week’s average. Similarly, annotate thresholds of alerts that have been defined on that metric or panel. In the following example, you can see the warning threshold annotated in yellow at 50% utilization and the error threshold annotated in red at 80% utilization: Image caption: A line graph with a warning threshold (yellow) and an error threshold (red). Source: Logz.io Include the real-time alert status on the top line, so you can spot at first glance if the threshold is currently breached without having to inspect the graph. It can be as simple as a thumbs-down icon or a broken heart one. If you can, annotate significant events that occur in the system’s lifecycle, such as configuration change, deployment of a new service version, or planned maintenance downtime as vertical marker lines on the graph. This will help correlate changes in metric behavior with system changes that may have caused them, such as a buggy release or a misconfiguration. In the below dashboard, we see a spike in the frontend latency (and a drop in new orders rate), and we can see a marker just before the spike, indicating a new version of the Payments service was deployed. Image caption: Deployment marker on top of the graphs. Source: Logz.io Overlay Values on the Same Graph Only When Makes Sense As engineers, we want to be efficient and overlay data on the same graph to ease correlation. But this oftentimes goes against the principle of simplicity and easy UX. If you have 100 instances, plotting their CPU utilization on a single graph can be overwhelming. Avoid plotting too many values on a single graph to make sure it’s manageable. One useful pattern here is to plot only the “10 highest” CPU consumers. Even if the number of instances is not so high, but they behave very differently, and therefore have different scales, overlaying them over a single graph will make it quite unreadable. When a single instance is presented in a graph, the Y-axis range and scale are calibrated for that specific instance, but when multiple metrics share the same graph, the “noisy” metric will determine the overall graph scale, and we’ll find it difficult to see issues in some other metrics. Similarly, consider carefully if it makes sense to overlay different metrics altogether. Plotting request rate, error rate, and latency on a single graph will probably make the graph quite difficult to use. Three adjacent graphs for the same time window will probably work better. In some cases, however, it is useful such as overlaying your Kafka consumer lag and latency, which are naturally highly correlated: Image caption: Kafka consumer lag and latency metrics plotted together with strong correlation. Source: Logz.io It’s important to note the scale of the graph even when plotting a single metric on it. With a single metric, some relatively minor jitters may visually look big and disturbing if we don’t pay attention to the scale. Having threshold annotations overlaid, as we said above, can help put things in perspective. Endnote It’s not enough that you have all the data needed, it needs to be accessible and usable so that people can actually understand what goes on and investigate issues. When designing a dashboard, keep in mind the user experience. Make sure the data is presented in a way that allows users to quickly and easily understand what’s going on at a glance. This means ensuring that the dashboard is free of data overload and that it uses consistent conventions and visual cues to make the data more accessible. Remember: think like a UX designer, and keep it simple. These guidelines will help you create an effective dashboard that is both intuitive and actionable. Liked it? Read also about: Expensive Metrics: Why Your Monitoring Data and Bill Get Out Of Hand Phantom Metrics: Why Your Monitoring Dashboard May Be Lying to You Also published here

How to Design an Effective Dashboard: The Essential Guide

More and more organizations rely on business dashboards for various reasons – including gaining critical insights into their data, monitoring and tracking key performance indicators, studying past performance, and predicting future trends. Creating reliable, robust, and effective dashboards is of utmost importance in this context. Keeping this in view, we have compiled together a list of ten resources that will make you better at . dashboard creation Continue reading to discover the world’s best websites containing practical tips, valuable principles, tutorials, coding examples, how-to guides, and much more to help you develop great business dashboards. 1. Where Can I Find Free Live Examples With Source Code for Different Business Dashboards Creation? One of the best places to start developing your dashboard is to browse through this comprehensive list of . beautiful business dashboards built using FusionCharts for web and mobile This site contains live examples of different dashboards, including SaaS, sales management, marketing, wealth management, shipment analysis, web series, and many more. All the dashboards are built using FusionCharts plugins for different frameworks, including Javascript, Ruby, Vue.js, Angular, React, etc. Once you figure out the requirements of your dashboard, you can browse through the list of these dashboards to find the one that best aligns with your needs. Next, download its source code and tweak it to match your desired functionality. Finally, you can get a great business dashboard going in a brief time. 2. What is FusionBrew – the FusionCharts Blog? You can visit , which contains different technical articles for developers and data scientists. FusionBrew- the FusionCharts Blog Here you’ll find articles on business dashboards and data visualization topics, including how-to series articles, technology articles, best practices blogs, and much more. Visit the for dashboard-related tutorials, examples, and blogs. category dashboard page 3. Are There Any White Papers on Dashboard Primers? The is another excellent resource for dashboard creation. Learn everything from , designing , and creating . Dashboard Primers page conceptualizing an effective business dashboard informative dashboards responsive dashboards This is a must-see resource for developing professional and effective dashboards. 4. What are the Emerging Trends in Dashboard Design? is a white paper that highlights the best practices for designing dashboards. It also describes the design elements that make a comeback for today’s dashboard interfaces. 10 Emerging trends in dashboard design In addition, it contains essential tips and principles for dashboard creation to provide users with an in-depth understanding of their data without overwhelming them with too much information. 5. How Do I Create Dashboards That Yield Insights? is a white paper worth reading to help you with dashboard creation. ‘Create Dashboards That Yield Insights’ It is a guide that explains how you can ensure that your dashboard is insightful and practical, along with which data visualization to use for your particular data. This paper lays down valuable principles to adhere to when creating dashboards. 6. What are Some Tips to Increase the Usability of My Dashboards? Another white paper with ten handy practical tips that are guaranteed to increase the usability of your dashboards and reports is .’ ‘Ten Tips to Increase the Usability of Your Dashboard It has tips on compelling chart captions, creating readable Javascript charts, dealing with multiple levels of data, and more. 7. Where Can I Find FusionCharts Plug-ins for Front-End and Back-End Integrations for Dashboard Creation? To integrate different FusionCharts visualizations in your business dashboard, the resource on describes various FusionCharts components for other frameworks like Ember, Vue.js, React, and Angular. front-end integrations The page lists various plug-ins for back-end integration. The best thing is that all plug-ins are open source, so that you can use them in any of your dashboard development projects. back-end integrations 8. How Do I Compare Different Javascript Charting Libraries for Dashboard Creation? It is not uncommon to feel overwhelmed by the different charting and graphing libraries available in the market today. Finding the proper charting library and visualization tool for dashboard creation can be time-consuming. To make it easy for you to pick the right tool, a comprehensive is a great resource that lists other features of various tools along with their pros and cons. There are details of FusionCharts, HighCharts, Chart.js, and more. comparison of different Javascript charting libraries 9. Are There Any Forums for FusionCharts? Yes, absolutely! The is a great resource page for company news, product information, and general topics such as FusionCharts jobs and consultation. FusionCharts forum Through these forums, the FusionCharts family holds discussions. Developers can get technical help, and users can read about the latest product updates. 10. What is the FusionCharts Suite XT Dev Center? The is one of the best resources for developers. This is the website that can help you get started creating interactive charts and dashboards for your web and mobile applications. FusionCharts Suite XT Dev Center It has numerous code examples and tutorials for different types of development environments. It also contains extensive documentation for all FusionCharts APIs and how to render various charts, graphs, and maps in your app. How Do I Get Started With Dashboard Creation Using FusionCharts? FusionCharts is the best Javascript library for business dashboard creation. You can create professional, practical, and aesthetically pleasing business dashboards that yield critical insights about the data in question. What’s more,  FusionCharts has a very short learning curve. You can quickly and easily develop an excellent business dashboard without having much coding knowledge. Make the most of your data. Head out now. ! Visit our website and sign up for your free FusionCharts trial today

10 Resources That’ll Make You Better At Dashboard Creation

In this article I will show how to develop a dashboard with several charts and spend minimum time on that. That approach does not require deep knowledge in programming, just some basic things of HTML and CSS. For charting, we will use Google charts. It has very clear documentation and a huge choice of charts, which we can just copy-paste and adjust with styles and titles we need. For demo purposes, I have created a simple project, which contains an index file with dashboard markup, a file with styles, and several JavaScript files for charts. So first, we just need to create some simple markup and styles, here how it looks like: <head> <title>Dashboard</title> </head>
<body>
<header>
    <span>Dashboard with google charts</span>
</header>
<section> <!DOCTYPE html> <html lang= "en" > <meta charset= "UTF-8" /> <link rel= "stylesheet" href= "styles/styles.css" > <link href= "https://fonts.googleapis.com/css2?family=Roboto&display=swap" rel= "stylesheet" > <script type= "text/javascript" src= "https://www.gstatic.com/charts/loader.js" ></script> <script type= "module" src= "js/pieChart.js" defer></script> <script type= "module" src= "js/barChart.js" defer></script> <script type= "module" src= "js/lineChart.js" defer></script> <script type= "module" src= "js/map.js" defer></script> <div class =" container "> < div class =" row "> < div class =" chart - widget "> < div class =" widget - header "> < span > Company Performance </ span > </ div > < div class =" chart "> < div id =" bar - chart " style =" width: 44vw ; height: 400px ;"></ div > </ div > </ div > < div class =" chart - widget "> < div class =" widget - header "> < span > Monthly performance for 2021 year </ span > </ div > < div class =" chart "> < div id =" line - chart " style =" width: 44vw ; height: 400px ;"></ div > </ div > </ div > </ div > < div class =" row "> < div class =" chart - widget "> < div class =" widget - header "> < span > Most popular products </ span > </ div > < div class =" chart "> < div id =" pie - chart " style =" width: 34vw ; height: 500px ;"></ div > </ div > </ div > < div class =" chart - widget "> < div class =" widget - header "> < span > Map </ span > </ div > < div class =" chart "> < div id =" map " style =" width: 54vw ; height: 500px ;"></ div > </ div > </ div > </ div > </ div > </ section > </ body > </ html > We are almost ready to implement charts. Let's add a link to google charts in the head and we are ready to go. < script type = "text/javascript" src = "https://www.gstatic.com/charts/loader.js" > </ script > I've picked for my dashboard donat, line, bar charts, and geomap. To implement them, we just need to drag code from documentation and paste it into our project. Then we need to adjust with the data set we need to show. We can also change colors if needed. There are also more advanced options available, but we will not use them during my example. Here is how the donut chart code looks like. ]); }; chart.draw(data, options);
} google.charts.setOnLoadCallback(drawChart); const drawChart = () => { var data = google.visualization.arrayToDataTable([ [ 'Product' , 'Sales' ], [ 'TV' , 9 ], [ 'Xbox' , 3 ], [ 'Playstation' , 3 ], [ 'Iphone' , 6 ], [ 'MacBook' , 3 ] var options = { pieHole : 0.4 , // Here we have to pass an ID ov div where we want to append a chart. var chart = new google.visualization.PieChart( document .getElementById( 'pie-chart' )); google.charts.load( "current" , { packages :[ "corechart" ]}); The rest of the charts have very similar logic. We just change the data set and a call for the proper chart. Code for all charts you can check it out in my . GitHub And here is a full picture, the dashboard with four charts we added. As you see it's quite quick and simple to implement such things. Wrapping up, there are both advantages and disadvantages of using Google Charts. Advantages - Provides a big collection of charts; - Easy to use and implements; - Does not require deep knowledge of coding; Disadvantages - Does not provide as much flexibility in customizing charts as other libraries do; - Some charts do not support a large amount of data; - Requires network connection; Wrapping up Google charts are very easy to use and can provide a good choice of high-quality charts. They can be implemented even by a non-tech savvy person. It is a good solution in case there is a need to introduce a quick demo for the presentation of some demo app for end-user. But I believe for a bigger scale it is better to pick some other library, for example, D3.js. It provides enough flexibility in customizing the charts and can handle a huge amount of data. But the downside is that it is a bit more complicated to learn. Hope it was useful. More about another library you get from my previous articles on Hacker Noon or on Github!

Implementing Dashboards Using Google Charts.

Search Engine Optimization (SEO) or the process of improving a website in order to increase its visibility is a complex one. There are several tools available in the market today focusing specifically on this. However, these tools can be prohibitively expensive, especially when each new add-on feature increases the costs. Organic traffic is one of the most sought-after types of traffic. It indicates that people are searching for solutions to their problem, and your product shows up as a solution leading to more business leads, and revenue. The premise sounds simple right? Google connects the two sides of the problem. People with questions can reach sites with answers. But to ensure that your site appears first when your potential visitors are searching requires monitoring and improving various metrics. An automated dashboard is able to check the search results ranks, and provide information about how well your website is placed in them. SEO Once you have an SEO Dashboard, you will know which keywords are well ranking, and be able to get a better idea about what kind of content is working and what isn’t. You can update pages that aren’t performing well, you can build new links and see how they affect your ranking. With this in mind, that can help us improve our organic reach. We will also learn some interesting concepts about using , its widgets, API connections, and more. our goal today is to create an SEO Dashboard Appsmith Let’s go! Clean UI for Clean Insights Here’s a look at the SEO Dashboard we will build. As you can see, for a good SEO reporting dashboard, the main KPIs should be clear. Right at the top, we can see our main key performance indicators (KPIs). They give us a clear vision of our overall status, what has been improving, and what isn’t. Of course, the indicators used in our SEO Dashboard are just a suggestion. You can create your own metrics based on what you need to track. Overview of your SEO Performance These lists show us performance for many keywords at a glance. These components allow for searching, filtering, pagination. In addition, the dropdown component modifies the data table. This allows you to see not just the current period but past months as well. This SEO dashboard includes a component of keyword-specific analysis. This allows you to select which keyword you want to see, and then analyze its progression chart. The final touch in our SEO reporting dashboard is the aggregate rankings chart. This gives you an understanding of how many well-ranking keywords you have. Now let’s start building our dashboard! If you’re curious to see the app in action, take a look here: https://app.appsmith.com/applications/61a0ee5cf6f14050cbd7dbb1/pages/61a0ee5cf6f14050cbd7dbb3 A good SEO Dashboard starts with pen and paper It might seem odd. But good reporting starts with good planning. If you plan ahead, you know exactly which data points you need to collect, what kind of transformations you need to do, and how your data is presented. But you don’t need to design a full mockup using pen and paper. Once you know what kind of information you want, it’s possible to use Appsmith itself for a mockup. Follow to see a mockup of the app! this link In this example, we created a page and just used the drag and drop tool to add our desired components to a page. We can add our data later. At this point we just need to get a feel of what the SEO Dashboard looks like and if it has all the information we need. Planning your KPIs and metrics In the end, our main KPIs are: #1 Keywords – How many keywords we have in the 1st place of search results Backlinks.- How many external links lead to our site Keywords in top 10 – How many keywords are in 1-10 places of search results Organic visits – how many visits came from our SEO efforts In addition, we want to know: Rank position for each keyword on a specific date Visits coming from a specific keyword on each date These two points allow us to build the tables and charts. You can add other metrics as well. This will depend on what kind of reporting you need in your own SEO dashboard. Here are some examples of interesting metrics that you could use. Here are some examples of interesting metrics that you could use: Bounce rate Conversion rate coming from organic results ROI Time spent per visit Word count vs Keyword Ranking Exit pages Last Updated Date vs Keyword Ranking Just keep in mind that each of these requires more data. If you have them all already, then it’s just a matter of loading it all into Appsmith. Google Sheets: Our Data Source Appsmith allows you to connect different data sources, databases and even perform cURL imports. However,  data source of choice today is Google Sheets. Yep, no DB, no fancy API. Just good old Google Sheets. Of course, there’s no right or wrong, just what works for you. And in SEO Dashboards, having a flexible data source is immensely helpful. Google sheets allows you to connect with external tools such as Google Analytics, store management, and paid traffic campaigns. And then you can manipulate this data using its spreadsheets functions, giving you the exact data in the exact format you need. This saves a lot of time and effort in data processing. Instead of coding a lot of JS functions, you just use a simple formula. We will learn how to use JS code here and there as well, but Google Sheets gives you the option of doing it all in separate spreadsheets. The key to a clean Google Sheets import is to make use of its lookup functions, formulas, and spreadsheets. This allows you to build tables that work in the ways we need them to on our widgets. This way, you do the data entry once, and then just manipulate it in the ways you need to. You can follow along in our public Google Sheet. In our example, we have three main tables that are our data sources: KW List – This table contains a simple list with all our monitored keywords Raw KW Rank – This table contains the search rank results for each of our keywords on each date along with the URL. Raw KW Visits – This table contains the visits for each keyword on each specific date And with these three tables, we generate all other tables of our SEO Dashboard. This is an interesting no-code path if you just want to use Appsmith and don’t have much JS experience. If you want to build along with this tutorial, you can create a copy of our sheet and play around with it. You can store and transform data from one sheet to another. And, you can see how each sheet is tailor-made for a specific widget. This makes it easy to load the relevant data. The KPIs sheet gives us all our KPIs as well as the text about them KW Rank Summary has our overall rankings that are used in the columns chart Backlinks can be used in a chart by itself but is used to create our backlinks KPI KW Rank transforms the Raw KW Rank, making it ready to be loaded as a list in Appsmith KW Visit does the same with the Raw KW Visit, transposing it, allowing easier readability as a list Now we have our data and tables. But how can we update it? Auto-updating Keyword Rankings And here we are, the question of the century. How do I know the exact rank for each of my keywords? The answer: Well, it’s tricky. First of all, if you try to create a simple crawler and query google with your 100 desired keywords it might work. But it’s likely that it won’t work for long. As a site gets bigger, unwanted crawling turns into a big problem. It's fine to have a couple of people visiting your site and maybe a bot or two loading pages en-masse. But as you grow, you might have thousands of bots loading thousands of pages at the same time, while your server is busy loading pages to real users and customers. To avoid crashes, many of these sites will block crawlers except the ones that they really want (Google and Bing, for example). For you, this means that if you try crawling one of these sites, you will be blacklisted, blocked and left with no data. Then, what’s the solution? There are many APIs out there to help you. The best one to get started is Google’s own search API. It allows you to create your own search tool, which then can be used to search the entire web, just like Google does. You can create your search tool on the page. Just make sure you select “Search the Entire Web” option. Here’s a screenshot for reference: Programmable Search Engine After this step, you will need to sign up for an This allows you to run 100 queries for free each day. You can track 100 keywords for free. That is better than most paid services. API key for the Google Cloud services. By far, the best and easiest way to load the API data into Google sheets is to use a custom . This allows you to load and understand the search results via JS and make sure there are no errors. Appscript function There’s also an option to do it using only the Google Sheets formulas, in case you want to give it a try. There’s an example of that in the “Crawler” sheet. Even though Gsheets doesn’t have a JSON reading function, you can use the IMPORTDATA function to load it. The trick is, this data isn’t fully understood. We need to add helper columns to understand the current search position. Then we need to find where our desired search result is. After that, you can use macros and recorded actions to loop through all your keywords. In addition, macros can be scheduled to run every day, for example. Thus your results are always updated. You can use the same technique to load your conversions, backlinks, and sales. Anything else that you want to include in your dashboard can be added to Google Sheets to make it easier to load into Appsmith. SEO Reporting Dashboard with Appsmith Now that we have everything ready, let’s tie it all together in Appsmith. Our SEO Dashboard mockup was done in our planning phase, but we will show you how to create each of the components we have as well as the data connections. Once you have your first page created, let’s load some content into it. How to Connect GSheets and Appsmith You can connect your  Google sheets straight from the API connection under “Datasources”. Click the + icon and select Google Sheets: You can read more about . how to use Google Sheets in Appsmith in our docs Then, create one API call for each of our sheets. Here are the names we used: getKWRankList – Gets KW Rank getKWRankSummary – Gets KW Rank Summary getKeywords – Gets KW List getKWVisits – Gets KW Visit getKPIs – Gets KPIs Adding our main widgets In our dashboard, we used an image for our logo, and a container for all the other components. Make sure you drag and drop them into your page. There’s nothing special about their setup, we just add the logo URL, resize it and make sure that it is centered making it look nice. Adding our SEO KPIs Next, we add our KPIs. We will be able to display them using the stats box. You could use other components if you want to as well. But the stats box has the most appropriate design for displaying KPIs. The stats box itself is a container, thus you need to select the internal components to edit them. We don’t need an image here, let’s delete it. Then select the first title and in the text section use. {{getKPIs.data[0].Name}} This should load the KPI name automatically: In short, what we are saying to Appsmith is: “In this section, I want you to use the getKPIs API call, then load its data, select just the first row, and get the name of that row.” The same applies to the KPI value and text we use {{getKPIs.data[0].Value}} and {{getKPIs.data[0].Txt}}. We are getting the first row, and the Value or Txt columns. Now you can duplicate this widget and just replace [0] (first row) with 1, 2, 3. This makes you get the second, third and fourth rows. Therefore, we will have all KPIs loading dynamically. Here is an example of the value of the third row: How to create the period switcher for the data table The data table itself is quite simple to implement. We just use our API call in the Table Data field, and it is auto-filled with our Sheets data. But we want the ‘Rank’ column to load different values depending on the “Period” dropdown. The same goes for the visits table. Now it’s time to create our first JS function. The idea here is to create a reusable function since we might use the same dropdown for multiple components. Thus, we head over to the Explorer > JS Objects: Then we create a new function inside of myObj, that allows us to get the current row name, depending on the period. If the period is “current,” we know that the KW Rank table should load the “Rank” column. But if it’s 30 days ago, then the KW Rank column name is Rank30d. The visits columns have different names, thus we need to check which kind of table we are looking at. This allows us to add more columns as well. You can include a backlinks table, and add another type to the function. Here is the full code: export default {
	currentkwRow: ( type = null ) => {
		var current = "none";
		if ( type == "visit" ) {
			// exception for visit type
			switch( periodSelect.selectedOptionValue ) {
				case '30d':
					current = "Last30dVisits";
					break;
				case '60d':
					current = "Last60dVisits";
					break;					
				default:
					current = "CurrentVisits";
			}
		} else {
			//default behavior for rank variable
			switch( periodSelect.selectedOptionValue ) {
				case '30d':
					current = "Rank30d";
					break;
				case '60d':
					current = "Rank60d";
					break;					
				default:
					current = "Rank";
			}
		}
		return current;
	}
} Here, it’s important to have the dropdown name and values correct. This is our current dropdown: Now we have a function that gets the dropdown value and gives us the right column name. Thus we can make the “Rank” column to be like this: This is the value we use: {{ currentRow[ myObj.currentkwRow() ] }} In essence, what we are saying to Appsmith is “the value for this row is the myObj.currentRow() index of the currentrow Array”. And the same applies to the Visits table, but the “Visits” column passes the “visit” type to our function, it outputs the column name for the visits table, not the rank table: {{ currentRow[ myObj.currentkwRow('visit') ]}} Creating an Inverse Line Chart in Appsmith When it comes to rankings for an SEO dashboard, a line chart is not enough. You might have noticed that we use a line chart with negative values in our mockup: This works fine and gives us the correct understanding of our data. Lower values are better here, then they are higher. But the negative values can be a bit annoying. We can use a different chart type instead. One of the beautiful things about Appsmith is that you can use any Fusion Chart you want. Just pass the right arguments based on chart type and the correct chart is built. Here we can use the InverseMSLine chart. You can read more about it in the . fusion charts docs Thus, when creating your chart, just pick the “Custom Chart” type, and pass your data. This is what we are using here: {
  "type": "InverseMSLine",
  "dataSource": {
    "chart": {
        "showBorder": "0",
        "xAxisName": "Dates",
        "yAxisName": "Rank",
        "theme": "fusion",
        "lineColor": "#f8692b",
                "yAxisMinValue": 1
    },
    "dataset": [
        {
            "data": {{ myObj.filterkw(); }}
        }
    ],
    "categories": [
        {
            "category": [
                {
                    "label": "120d"
                },
                {
                    "label": "90d"
                },
                {
                    "label": "60d"
                },
                {
                    "label": "30d"
                },
                {
                    "label": "today"
                }
            ]
        }
    ]
  }
} Notice that we have a function in our dataset: "data": {{ myObj.filterkw(); }} That’s because we use a switcher here, just like we did for the data tables. Our dropdown is just our KW list, with a small adjustment where the values have underscores instead of spaces. This is needed to make sure it works with JS code. This is our code: {{
  getKeywords.data.filter( (item) => (item.value = item.label.replaceAll( ' ', '_') ) );
}} In short, this filters through all values, but it returns all of them, while it creates a new index for item.value that is just the label with underscores replacing spaces. Thus, our chart just needs to get the value from this dropdown and return the keyword ranks for that specific keyword from the getKWRankList. Think of it as a chart version for a single line of the KW rank table. This is the code for the filterkw method in our myObj Object: filterkw: () => {
		var newdata = [];
		var compare;
		getKWRankList.data.forEach( function(el, i){
			compare = el.Keyword;
			compare = compare.replaceAll( ' ', '_') ;
			if ( compare == kwselect.selectedOptionValue ) {
				newdata[0] = { value: el.Rank120d };
            newdata[1] = { value: el.Rank90d };
            newdata[2] = { value: el.Rank60d };
            newdata[3] = { value: el.Rank30d };
            newdata[4] = { value: el.Rank };
			}
		});
		return newdata;
	} How to create a custom column chart Our line chart has many interesting things going on that you might have missed one thing: it has a custom color. This goes back to the fusion chart options we’ve mentioned. But let’s expand on this idea for the columns chart. It’s possible to pass multiple colors for column charts, creating an interesting visual effect. We use a custom chart again, but for the most part, we are interested in the chart setup now. This is what we use: {
  "type": "column2d",
  "dataSource": {
    "chart": {
        "xAxisName": "Rank Position",
        "yAxisName": "No. of Keywords",
        "palettecolors": "f8692b, f9814d, fa8f61, fb9d74, fbab88",
        "theme": "fusion"
    },
    "data": {
      {
    	 getKWRankSummary.data;
      }
    }
  }
} The magic lies in the “palettecolors” variable. Each chart has its own setup for colors, thus it’s worth checking the fusion charts reference when you are building your own charts. Today we went step by step, from planning to implementation of a complete SEO Dashboard. We explored many aspects of data structure, crawling, Appsmith widgets, custom charts, and custom styling. I hope you enjoyed this tutorial, and I can’t wait to see your SEO reporting dashboards! If you have any questions, feel free to reach me at appsmith@roch.com.br and I’d be glad to help you. Thanks!

How to Build Your Own SEO Dashboard

A Guide On Building a Django-Oscar Application with a Dashboard

Dashboards are an integral part of enterprise applications. An organization runs on a sea of data and it is imperative to have an intuitive enterprise app that can gather and organize such data. When you look deeper into an organization, you’ll realize that what’s more important than data is the information it is conveyed. A dashboard becomes valuable when this “information” is presented easily and quickly to the users. So, when you design such an important element, keep some of the following tips in mind to avoid common mistakes. 1. Getting the Navigational models wrong The underlying approach behind designing navigational menus is often misunderstood. It’s common to find people using a single menu to contain all the navigation. This makes the menu difficult to use. Instead, Global navigation, which includes tools, resources, profile, logout, etc, can be placed on top. To the left side, you can place your dashboard navigation which includes views, types of data, etc. These are the menus that are frequently used by users. It demands quick and easy access. You can have breadcrumbs including deeper-drill-downs within a content flow. This will empower users to take quicker navigational actions. 2. Not matching the user’s mental model What the user believes about the system constitutes to their mental model. User’s actions on the dashboard depend on what they already know about it. For good user experience, you must incorporate their existing beliefs while designing the product. For example, users have a certain mental model of expecting & finding contents within a collapsed dashboard menu. So, when you’re categorizing these menus, align with their mental model to group them together. If you don’t account for their mental model, you’ll end up frustrating these users. How do you get the mental model right? Conduct extensive user research to understand and categorize menus and other elements in the dashboard. Contextual inquiry and card sorting processes can be made use of at this stage. 3. Failing to match the right data visualizations for the data Different types of data demand different types of visualizations. Some are comparative, relative and transactional. If you don’t fully understand the data and it’s subsequent information, you’ll end up using the wrong visualizations. Histograms, line graphs, pie charts, scatterplot, and streamgraph are some of the commonly used visualizations. They cannot be used for all types of data. For example, pie charts would be terrible for plotting a website’s traffic data. So, before choosing the type of visualization, have a concrete understanding of the information that’s conveyed through the data. 4. Static content on a dashboard serves no purpose Dashboard is the place where users go for dynamic content. Static content on the dashboard serves no purpose. If the data doesn’t get updated frequently, there is no incentive for the users to come back to the application. If they can predict the contents of an application, there is no scope for re-engagement. Dynamic content should be placed at the heart of the app. They require attention & therefore your visual design should be aligned to highlighting it. 5. Avoid excess padding and white spaces Static websites are made attractive using plenty of whitespaces. Sufficient padding and margin can give your site a modern appeal. However, for dashboards, your focus should be on enabling users to navigate around and complete tasks in the most efficient manner. It is frustrating for them when they are compelled to scroll around a lot for completing regular functions. Global interactions such as sort, search and filter should be easily accessible. If there is a mantra for this, it is “compact design” 6. Not designing for different user roles Enterprise apps are used by users in different roles and positions. It is imperative to design your dashboard keeping every single user role in mind. Extensive user research must be conducted to understand their needs and context of use. This is much more important than understanding the data alone. Dashboard can have different views for different users. Understanding what’s important for each of them is critical in providing a good user experience for everyone. Also, an enterprise application can be used by people outside of the enterprise. In that case, you have to communicate with all those stakeholders to better understand their requirements. These are some of the common mistakes while designing a dashboard. Keep these in mind to avoid them and provide a seamless user experience.

Designing a Dashboard? - Don't Make These 6 Common Mistakes

KPI's or Key Performance Indicators are a modern instrument to make a system (a business, for example) work effectively. KPI's show how successful the business is, or how professional the employee is. It works with the help of measurable values, that are intended to show the success of achieving your strategic goals. KPIs are indicators that you should track, calculate, analyze, and represent. measurable If you read this article, it means that you want to find or build an app to help you in all operations above. But before we list the top dashboard templates for tracking KPIs, it’s essential to understand a set of indicators that boost the growth of a business. how exactly to choose For KPI's to be useful, they should be relevant to a business. That is crucial not only for entrepreneurs who try to improve their businesses but also for the developers of the software for tracking KPIs. Why? Developers need to be aware of what instruments they should include in the app so the users will be able to use KPI’s easily and effectively. Since there are much more than a handful of articles and approaches on how to find the right performance indicators, what KPI's to choose, how to track them, development of a quality web application can be complicated. However, from our point of view, the most challenging part of such an app is building a dashboard that displays all necessary KPI's on a single screen. We have explored the Internet, analyzed different types of tools to represent KPI's, found great dashboards, and made two lists: one consists of the charts and instruments you should definitely include in your future app, the other is top dashboards we found that contain elements from the first top. Let’s start from the first list. Enjoy reading! What Indicators Do We Need to Build an Effective Dashboard for KPI's? Absolute Numerical Values and Percentage (in absolute amount) With the help of percentage, you can make it more informative by adding the comparison of KPI with the previous periods. Non-Linear Chart One of the core charts. Bar Chart Another core element to display KPI's. Stacked Bar Graphs It’s a more complex instrument, but more informative respectively. Progress Bars Can be confused with a horizontal bar chart. The main difference: a horizontal bar chart is used to compare the values in several categories, while a progress bar is supposed to show the progress in a single category. Pie Charts Donut Chart You can replace pie charts with a donut chart, the meaning is the same. Gauge Chart This chart helps users to track their progress towards achieving goals. It’s interchangeable with a progress bar. Pictograms Instead of using an axis with numbers, it uses pictures to represent a relative or an absolute number of items. Process Behavior Chart Especially valuable for financial KPI's. The mainline shows measurement over time or categories, while two red lines are control limits that shouldn’t be surpassed. Combined Bar and Line Graph : Some Additional Tools These tools are also essential for building a dashboard for tracking KPI: calendar, dropdowns, checkboxes, input fields. The option to create and download a report will also be helpful. The Top Dashboards for Tracking KPI's: Sing App Admin Dashboard If you look through a huge number of dashboard templates and don’t find one that you need, you should take a look at Sing app. Sing is a premium admin dashboard template that offers all necessary opportunities to turn data into easy to understand graphs and charts. Besides all charts and functions listed above, with Sing, you get such options as downloading graphs in SVG and PNG format, animated and interactive pointer that highlights the point where the cursor is placed, and change the period for values calculation inside the frame with the graph! MORE INFO DEMO Retail Dashboard from Simple KPI That is a dashboard focused on the retail trade sphere. It already contains relevant KPI's and Metrics for that sector, so you need just to download it and use it. Since it’s an opinioned dashboard you will not get a great customization option. If you are a retailer or trader you should try that dashboard to track the performance when selling goods or services. MORE INFO DEMO Light Blue React Node.js It is a React Admin dashboard template with Node.JS backend. The template is more suited for KPI's that reflect goals in web app traffic analysis, revenue and current balance tracking, and sales management. However, Light Blue contains a lot of ready-to-use working components and charts to build a necessary dashboard. It’s very easy to customize and implement, both beginners in React and professional developers can benefit from that template and get a track on KPIs, metrics, and business data. MORE INFO DEMO Limitless Dashboard Limitless is a powerful admin template and a best-seller on ThemeForest. It goes with a modern business KPI dashboard that simplifies the processes of monitoring, analyzing, and generating insights. With the help of that dashboard, you can easily monitor the progress of growing sales or traffic and adjust the sales strategy according to customer behavior. Furthermore, the dashboard contains a live update function to keep you abreast of the latest changes. MORE INFO DEMO Cork Admin Dashboard That is an awesome bootstrap-based dashboard template that follows the best design and programming principles.  The template provides you with more than 10 layout options and of the extremely rare dashboard. Several pages with charts and two dashboards with different metrics ensure you have the basic elements to build a great dashboard for tracking KPI. Laravel Version MORE INFO DEMO Paper Admin Template This template fits you if you are looking for a concrete solution since Paper goes with dashboards in the package! They all are unnamed so it will take time to look through them, but that time will be less than time for building your dashboard. Every dashboard provides a simple single-screen view of data and allows sharing it with your collages. eleven MORE INFO DEMO Pick Admin Dashboard Template Pick is a modern and stylish solution for the IT industry. It’s a multipurpose dashboard that helps you to gain full control over the performance. MORE INFO DEMO Able Pro Admin Dashboard If you believe that the most qualified products are the most rated products, take a look at Able pro. Able pro is a best-rated bootstrap admin template on Themeforest. The human eye captures information within the graph blazingly fast! With that dashboard, you can go much deeper into the understanding of KPIs and make the decision-making process much easier. MORE INFO DEMO Architect UI Admin Template Those who download Architect UI make the right choice. That template created with hundreds of build-in elements and components, and three blocks of charts. The modular frontend architecture makes dashboard customization fast and easy, while animated graphs provide insights about KPI's. MORE INFO DEMO Flatlogic One Admin Dashboard Template Flatlogic one is a one-size-fits-all solution for any type of dashboard. It is a premium bootstrap admin dashboard template that has been released recently in July 2020. It goes with two developed dashboards: analytics and visits. But it also offers four additional pages with smoothly animated charts for any taste and needs. The dashboard is flexible and highly customizable, so you easily get the benefit from that template. MORE INFO DEMO Thanks for reading. Previously published at https://flatlogic.com/blog/examples-of-dashboard-templates-for-tracking-kpi-s/

10 Dashboard Templates and Effective Indicators for Tracking KPI’s

I use information from all over the internet. I visit hundreds of new web pages every day, both for personal and professional projects. It’s part of the process, and I’m happy to do it. But, every day, I also waste precious time checking the same old websites for other vital information; weather, news, stock portfolio, email, Twitter, work alerts, and so on. And we all have a list like this. And you may find yours just as frustrating. What annoys me the most is that the second I leave the site, I instantly wonder if the information has changed, and I stress out until I check it out again. I finally got so fed up with all this chasing after information! Instead of me going to look up information in twenty different places, why can’t this information come to me in one single spot? And that’s when it hit me 🥊 I Need a Dashboard! A dashboard makes so much sense! It can contain any information that I want! It can be updated several times a day and can be permanently displayed on a monitor. All I need to do is glance at it for a few seconds, and I know what is going on. What sealed the deal for me was that most web services can be easily queried with , which can then talk to , a dashboarding system. Combining these three technologies (web services, n8n and Smashing) would save me significant time every day and keep me better in the loop with what is going on in other areas of my life. n8n Smashing And the best part? You can build your custom dashboard as well! In this article, we will pull information from , , , and about the n8n project and then display it using a Smashing dashboard. Since this information is constantly changing, n8n will perform this every minute. The workflow for this project looks something like this: GitHub Docker npm Product Hunt n8n and Smashing The two essential pieces of this project are n8n and Smashing. They make up the core of the project and are very well suited to working together. Every minute, n8n gathers the data from the four data sources using their interfaces. It then takes this data and extracts the pieces which are useful and then pushes it to the Smashing dashboard. API At this point, Smashing takes over and displays the information it receives from n8n based on how the dashboard was built inside the Docker container and which Smashing API endpoint receives the information. How Smashing Works While it is outside of the scope of this article to go into detail on how Smashing works, it is important for you to understand some of the fundamentals of Smashing. Each Smashing dashboard is made up of a series of widgets. Each widget displays a piece of information. This information is fed to the widget through the Smashing API. Each widget has its own unique API endpoint. When the endpoint receives information, the widget displays that information. These are the endpoints that have been created for this project and where their information originates: This dashboard API interface along with the widget types are defined in the file located in the docker container. (If you are interested in seeing how this file creates the dashboard, it is available .) n8n_overview.erb here Prerequisites If you want to build this project yourself, you will need a couple of things ready to go before you start: — You can get this up and running by checking out the page. You should have a fresh install without any workflows. n8n Quickstart — To save you time, we have built a Docker container with all of the Smashing pieces pre-configured. This way, you can have this piece running quickly and easily. For more information on setting up a Docker environment, please check out one of these . Docker tutorials — In order to ensure that you do not run into issues accessing the GitHub API, you can use your account to increase how frequently you can retrieve information from the API. If you do not have a GitHub account, you can and you can learn how to set up your credentials for GitHub in n8n . GitHub Account join here here — To use the Product Hunt API, you are required to authenticate with them using your account and a developer token (see “But… I just wanted to run a simple script?” in the ). If you do not have an account with Product Hunt, you can . Product Hunt Account Product Hunt API documentation sign up here Quick Start Many of you want to experience the result before committing to a project or already know the majority of what you will be learning in this article. For you, I have put together this option. Follow these steps to get up and running quickly. If something is unclear or you want to learn more about how it works, feel free to dig deeper into the sections that follow. Quick Start Here are the quick-start steps: Install the docker container with the following two commands: docker pull tephlon/n8n_dashboard
docker run --name n8n_dashboard -d -p 8080:3030 --rm tephlon/n8n_dashboard:latest Copy the n8n workflow from and paste it into your n8n installation here Modify the following nodes with your information. I have highlighted them in red in the workflow for easy identification: — set value of to your docker install Dashboard Configuration dashboardHostname — set your value based on your Retrieve Product Hunt Data token developer token Set up your GitHub credentials Activate workflow Browse to port 8080 of your docker installation Now that you have a fully functioning dashboard, let’s take a look at what everything does, and maybe inspire you to tweak this workflow to suit your needs. The Five Stages of an n8n Workflow I have noticed in many of the workflows that I create there are five distinct stages that the workflow goes through from start to finish and this workflow is no exception. Trigger Configuration Data Retrieval Data Processing Action This is how the dashboard workflow looks broken up into these different stages: Let’s work through setting up these five stages as they pertain to the dashboard project. Stage 1: Trigger Every workflow has to be told how to start, and this is referred to as the trigger. In this project, we want to update the dashboard with new information every minute. We’ll use the node for that. Cron Set the parameter to . Doing this will run the workflow (you guessed it) every minute. Mode Every Minute Now, the workflow knows how and when to run. Stage 2: Configuration The configuration stage is generally a little more defined in my workflows than they are for others. I like to create a node with the majority of the configuration options so that they are all in one place (although there are exceptions to this rule which we will cover in a minute). For those of you who have developed in other tools before, you can think of this node as that are available to all other nodes within the workflow. Set global variables Not all configuration settings are set at this time for two reasons: The value is retrieved in a later stage node values get copied when exported. If you have sensitive data such as API tokens in a node, they would also get exported (which would be bad) Set Set As a convention for myself, I like to color the borders of my nodes red that require configuration, and you will see that I have done this as well for this workflow. For the configuration of this workflow, we have created a node called which contains several string values. Most of these values can be ignored at this point, but if you want to customize the dashboard to monitor your project, this is where you would make these changes. (More on this later.) Set Dashboard Configuration, As described in the quick start section, the only change you need to add to the node is to set the value so that it matches your docker container deployment. This is very specific to your docker installation and deployment of the tephlon/n8n_dashboard container. If your n8n installation is on the same system as your docker installation, this will be . Dashboard Configuration dashboardHostname localhost:8080 If docker is on a different system than your n8n installation, this value will be either or . So, if your docker installation is on 192.168.4.25, this value would be . You should be able to get this information from your docker admin. (If it turns out that this person is you and you are uncertain about what this value is, I have found a handy which may point you in the right direction.) <docker IP address>:8080 <docker hostname>:8080 192.168.4.25:8080 YouTube video This node is connected to the previous node so that these values are loaded every time that the workflow runs, and the values are reset if one of them gets accidentally changed by a different node. Cron Stage 3 : Data Retrieval In this stage, we are collecting all of the data from the different data sources, often using settings from the configuration stage. We are using two different types of nodes to collect data, depending on the service. n8n has a built-in node, so it makes sense to use it for gathering the GitHub data. But, there are no custom nodes for the other three services, so we will use the node to pull information from each service’s API. GitHub HTTP Request The output of the node connects into these four nodes. They then use these settings to know which project to be monitoring. Dashboard Configuration You will need to change the settings in two nodes for this stage. The node will need your to work, and the node will need your . GitHub GitHub credentials Retrieve Product Hunt Data developer token You should now have the ability to retrieve all of the raw data provided by these services. Stage 4 : Data Processing Now that we have this data, we need to make sure that it is in the proper format. The two challenges that need to be overcome are: Large numbers are difficult to read Decimal numbers are too long to display properly To transform these values into something more usable, we will use the node. The Function node allows us to write our custom code when a pre-built node may not exist. Function A single Function node is added for each service and connected to the output of the nodes created to retrieve the service data. To add the thousands separator to a value, you reassign the original value with the updated value. The updated value is created by appending the value name with . This tells the system to convert the number to a string and replace every third space between characters with a comma. .toString().replace(/\B(?=(\d{3})+(?!\d))/g,",") For example, to reformat the from the Docker service, you would enter the following code: pull_count items[ ].json.pull_count = items[ ].json.pull_count.toString().replace( , “,”); 0 0 /\B(?=(\d{3})+(?!\d))/g To round a value to two decimal places, we perform a similar action using the function and method. parseFloat toFixed() So, to round the value from the npm service, use the following code: score.final items[ ].json.score.final = (items[ ].json.score.final.toFixed( )); 0 parseFloat 0 2 Each value that needs to be changed has the appropriate line of code added to its node. Function Stage 5: Action The final stage is the action. This is where the n8n workflow performs an action on something. In this case, the workflow posts a value to the dashboard API for a specific dashboard widget. For example, to update the number of GitHub Stars on the dashboard, the workflow needs to post the value from the formatted data originally generated by the node. This is performed using the node, one for each widget. stargazers_count GitHub HTTP Request And that is the final piece! Once the workflow is activated, it will update all of the dashboard widgets every minute with the information it pulls from each service. Monitoring Your Own Project The one thing that most people will want to do is modify this workflow to monitor their own project. I have tried to make this easy by putting all of the changes in one the node. Dashboard Configuration : This should be the hostname and port of your docker installation. See for more details. dashboardHostname (default http://192.168.1.14:8080 ) Stage 2 — Configuration : Used to authenticate with the Smashing dashboard. There should be no need to change this unless you are playing around with the docker image. dashboardAuthToken (default n8n-rocks! ) : The post_id of the product that is being monitored at Product Hunt. You can find this number by going to your product page on Product Hunt and clicking on the button. In the embed code, look for The number immediately follows this string. product_hunt_post_id (default 170391 ) Embed . https://cards.producthunt.com/cards/posts/ : Name of the n8n package that is being monitored. You can find your project name by searching for your product at and copying the name exactly as it is on the webpage. npm_package (default n8n ) https://www.npmjs.com/ : Name of the user or organization who owns the docker repo being monitored. Find the repository that you are using at (e.g. ). This is the portion of the string before the “/” (e.g. ) docker_name (default n8nio ) https://hub.docker.com jim/nasium jim : Name of the docker repo being monitored. Find the repository that you are using at (e.g. ). This is the portion of the string after the “/” (e.g. ) docker_repository (default n8n ) https://hub.docker.com jim/nasium nasium : Name of the user or organization who owns the GitHub repo being monitored. Find the repo that you will be monitoring at (e.g. ). This is the portion of the string before the “/” (e.g. ) github_owner (default n8n-io ) https://github.com jim/nasium jim : Name of the GitHub repo being monitored. Find the repo that you will be monitoring at (e.g. ). This is the portion of the string after the “/” (e.g. ) github_repo (default n8n ) https://github.com jim/nasium nasium If there are any of these services which you do not wish to monitor, delete the link between that services’ data retrieval node and the node. This will prevent the node from capturing the data and the widgets on the dashboard will remain unchanged. Cron What’s Next? We have just touched the tip of the iceberg when it comes to dashboarding. Some other ideas that are possible include: Charting stock prices Displaying weather Aggregating RSS feeds Monitoring Twitter feeds Showing videos from a YouTube channel Company phone activity Personnel in/outboard Fleet vehicle tracking Our Journey We’ve covered a lot of ground today. Let’s review what we have accomplished: Installed the custom dashboard in Docker Set the workflow to run every minute Designed a global configuration node to easily manage common variables Gathered data from four different online services Modified the data so that is displays properly Pushed the information for display in the dashboard using its API I’d love to hear about what you’ve built using n8n! Or if you’ve run into an issue while following the tutorial, feel free to reach out to me on or ask for help on our 💙 Twitter forum Also published at https://medium.com/n8n-io/dashing-through-the-data-visualizing-metrics-with-n8n-78f3f0309da5

Data Dashboards: Visualizing Metrics with n8n 📈

For most SEO specialists it is mandatory to keep track on search visibility and keyword rankings in search engines. There are a lot of tools who provides this, such as AWR Cloud, STAT, Ahrefs, SEMrush and many more. This data can be overwhelming when you have a lot of keywords to track and want to be able to compare current rankings with rankings in the past for you and your main competitors. Also, this amount of information (together with server speed) can impact the fun of analyzing when the information you want is not instantly loaded. Python can be a good way to analyze a lot of data, make some custom calculations on this and plot it just the way you want! In this article I will write a step-by-step process for creating your own dashboard using Python’s Pandas and (in my case) SEMrush exports. As said, there are a lot of keyword tracking tools, so any other export is fine as well but needs some customization tweaks. Global idea and working of the dashboard The dashboard is set up in 4 main steps: Collect and clean the exports from SEMrush (or any other ranking export file) and create a useful work file to continue Add a visibility calculation to the data and plot this over categories Plot rankings overtime per category and keyword Create a pivot dashboard for specific insights The result will be good understanding of ranking and visibility fluctuations, find issues per category and even per keyword group. This can give you a very specific idea where to optimize. Let’s get started For my company we track 571 keywords at SEMrush over 88 categories (tags). For this article I have collected exports from SEMrush on a weekly base for mobile rankings. I also track the rankings of the 4 main industry competitors. 1: Collect and clean the exports from SEMrush (or any other ranking export file) and create a useful work file to continue Now that we have some background information, open a Jupyter Notebook (recommended because of the visualizations) and import the main libraries to use. win32com.client win32 os pandas pd numpy np re matplotlib mpl matplotlib.pyplot plt
%matplotlib inline ipywidgets interactive, widgets, interact, interact_manual ipywidgets widgets IPython.core.display display, HTML pivottablejs pivot_ui warnings
warnings.simplefilter( ) #import all libraries and widgets import as import import as import as import import as import as from import import as from import from import #and let’s ignore warnings import 'ignore' Now it is time to ask Pandas for help and make some fine tweaks to the data. Notice the ‘header=7’ in the script below. This looks weird, and I think it is weird. But that is how SEMrush creates the exports. Perfectly fine. But weird 😊. df_20200930 = pd.read_excel( , header= ) df_20200930.replace( ,np.nan,regex= ).replace( ,np.nan, inplace= ) df_20200930[Date] = pd.options.mode.chained_assignment = df_20200930[Date] =  pd.to_datetime(df_20200930[Date], format= , infer_datetime_format= ) df_20200930.rename(columns={ : , : , : , : , : , : , : , : , : , : , : , : }, inplace= ) use_cols_20200930 = df_20200930[[ , , , , , , , , , , , , , , ]] df_20200930.head() #read .xlsx into dataframe "./rankings_converted/20200930.xlsx" 7 #replace empty landing pages r'\s+' True '' True #add date to columns, date should be changed manually '2020-9-30' #to avoid a warning None #date to datetime64 dtype '%Y%m%d' True #create more readable names for the columns 'tags' 'Tags' '*.expert.nl/*_20200930' 'Rankings_Expert' '*.expert.nl/*_20200930_landing' 'Expert_url' '*.bcc.nl/*_20200930' 'Rankings_BCC' '*.bcc.nl/*_20200930_landing' 'BCC_url' '*.coolblue.nl/*_20200930' 'Rankings_Coolblue' '*.coolblue.nl/*_20200930_landing' 'Coolblue_url' '*.mediamarkt.nl/*_20200930' 'Rankings_Mediamarkt' '*.mediamarkt.nl/*_20200930_landing' 'Mediamarkt_url' '*.wehkamp.nl/*_20200930' 'Rankings_Wehkamp' '*.wehkamp.nl/*_20200930_landing' 'Wehkamp_url' 'Search Volume' 'Search_Volume' True #define which columns will be used 'Datum' 'Keyword' 'Tags' 'Rankings_Expert' 'Expert_url' 'Rankings_BCC' 'BCC_url' 'Rankings_Coolblue' 'Coolblue_url' 'Rankings_Mediamarkt' 'Mediamarkt_url' 'Rankings_Wehkamp' 'Wehkamp_url' 'CPC' 'Search_Volume' #check if it works After that we can save this to a .xlsx file with customized columns. For these files I have created a new folder: [./use_cols]. use_cols_20200930.to_excel( ) #save to new .xlsx file './use_cols/20200930.xlsx' We need to do this for all periods. So, just go find and replace the dates when editing and saving. If someone have any clue on how to automate this process, please let me know! Because we need to work with all this configured Excel files it is time to gather all the DataFrames and combine all files from the folder. data_20200129 = pd.read_excel( )
data_20200205 = pd.read_excel( )
data_20200212 = pd.read_excel( )
data_20200219 = pd.read_excel( )
data_20200226 = pd.read_excel( )
data_20200304 = pd.read_excel( )
data_20200311 = pd.read_excel( )
data_20200318 = pd.read_excel( )
data_20200325 = pd.read_excel( )
data_20200401 = pd.read_excel( )
data_20200408 = pd.read_excel( )
data_20200415 = pd.read_excel( )
data_20200422 = pd.read_excel( )
data_20200429 = pd.read_excel( )
data_20200506 = pd.read_excel( )
data_20200513 = pd.read_excel( )
data_20200520 = pd.read_excel( )
data_20200527 = pd.read_excel( )
data_20200603 = pd.read_excel( )
data_20200610 = pd.read_excel( )
data_20200617 = pd.read_excel( )
data_20200624 = pd.read_excel( )
data_20200701 = pd.read_excel( )
data_20200708 = pd.read_excel( )
data_20200715 = pd.read_excel( )
data_20200722 = pd.read_excel( )
data_20200729 = pd.read_excel( )
data_20200805 = pd.read_excel( )
data_20200812 = pd.read_excel( )
data_20200819 = pd.read_excel( )
data_20200826 = pd.read_excel( )
data_20200902 = pd.read_excel( )
data_20200909 = pd.read_excel( )
data_20200916 = pd.read_excel( )
data_20200923 = pd.read_excel( )
data_20200930 = pd.read_excel( ) #gather all files './use_cols/20200129.xlsx' './use_cols/20200205.xlsx' './use_cols/20200212.xlsx' './use_cols/20200219.xlsx' './use_cols/20200226.xlsx' './use_cols/20200304.xlsx' './use_cols/20200311.xlsx' './use_cols/20200318.xlsx' './use_cols/20200325.xlsx' './use_cols/20200401.xlsx' './use_cols/20200408.xlsx' './use_cols/20200415.xlsx' './use_cols/20200422.xlsx' './use_cols/20200429.xlsx' './use_cols/20200506.xlsx' './use_cols/20200513.xlsx' './use_cols/20200520.xlsx' './use_cols/20200527.xlsx' './use_cols/20200603.xlsx' './use_cols/20200610.xlsx' './use_cols/20200617.xlsx' './use_cols/20200624.xlsx' './use_cols/20200701.xlsx' './use_cols/20200708.xlsx' './use_cols/20200715.xlsx' './use_cols/20200722.xlsx' './use_cols/20200729.xlsx' './use_cols/20200805.xlsx' './use_cols/20200812.xlsx' './use_cols/20200819.xlsx' './use_cols/20200826.xlsx' './use_cols/20200902.xlsx' './use_cols/20200909.xlsx' './use_cols/20200916.xlsx' './use_cols/20200923.xlsx' './use_cols/20200930.xlsx' The sad part for me is that we don’t use SEMrush API and therefore this needs to be updated manually per week. Boehhh! files = [file file os.listdir( )]
workfile = pd.DataFrame() file files:
    df = pd.read_excel( +file)
    workfile = pd.concat([workfile, df]) workfile.info() #combine all files from directory for in './use_cols/' for in './use_cols/' #check results And let’s now forget to categorize the keywords! In a subfolder ([./workfile]) I created a .txt file with the tags of the keywords. Example of the tags.txt can be found . here tags = pd.read_csv( , sep= ) output = workfile.merge(tags, on= )
output.head()
output.to_csv( , index= ) #categorize all keywords from txt file './workfile/tags.txt' ';' #merge tags on keywords, check and export to new .csv (which will be a work file) 'Tags' './workfile/output.csv' False 2: Add a visibility calculation to the data and plot this over categories Now that we have our work file it is time for some fun stuff and get actual plots and useful insights. df = pd.read_csv( ,) 
df.drop( , axis= , inplace= ) df.replace( ,np.nan,regex= ).replace( ,np.nan, inplace= ) #open work file and get rid of empty fields './workfile/output.csv' 'Unnamed: 0' 1 True #replace empty landing pages r'\s+' True '' True For a visibility report some calculations needs to be done. When ranking in organic search for position one you have well deserved a 100% visibility score. This will be less when ranking lower up till position 30 where the visibility percentage stops. An example can be downloaded . here df_vis_expert = pd.read_excel( )
df_vis_calc = df.merge(df_vis_expert,on= , how= )
df_vis_calc[ ] = df_vis_calc.Visibility_Expert.astype(float) df_vis_bcc = pd.read_excel( )
df_vis_calc2 = df_vis_calc.merge(df_vis_bcc,on= , how= )
df_vis_calc2[ ] = df_vis_calc2.Visibility_BCC.astype(float) df_vis_coolblue = pd.read_excel( )
df_vis_calc3 = df_vis_calc2.merge(df_vis_coolblue,on= , how= )
df_vis_calc3[ ] = df_vis_calc3.Visibility_Coolblue.astype(float) df_vis_mediamarkt = pd.read_excel( )
df_vis_calc4 = df_vis_calc3.merge(df_vis_mediamarkt,on= , how= )
df_vis_calc4[ ] = df_vis_calc4.Visibility_Mediamarkt.astype(float) df_vis_wehkamp = pd.read_excel( )
df_vis_calc5 = df_vis_calc4.merge(df_vis_wehkamp,on= , how= )
df_vis_calc5[ ] = df_vis_calc5.Visibility_Wehkamp.astype(float) df_workfile = df_vis_calc5.dropna(subset=[ ])
df_workfile.rename(columns={ : }, inplace= ) df_workfile.reset_index() df_workfile.head() visibility = df_workfile[[ , , , , , , , , ]]
visibility.fillna( , inplace= )
visibility.to_csv( , index= ) #create visibility_dataframe for each website ##visibility_Expert './visibility_competitors/vis_expert.xlsx' 'Rankings_Expert' 'outer' 'Visibility_Expert' ##visibility_BCC './visibility_competitors/vis_bcc.xlsx' 'Rankings_BCC' 'outer' 'Visibility_BCC' ##visibility_Coolblue './visibility_competitors/vis_coolblue.xlsx' 'Rankings_Coolblue' 'outer' 'Visibility_Coolblue' ##visibility_Mediamarkt './visibility_competitors/vis_mediamarkt.xlsx' 'Rankings_Mediamarkt' 'outer' 'Visibility_Mediamarkt' ##visibility_Wehkamp './visibility_competitors/vis_wehkamp.xlsx' 'Rankings_Wehkamp' 'outer' 'Visibility_Wehkamp' #rename file and do some editing 'Keyword' 'Tag2' 'Categorie' True #reset index #check if it works #create visibilty_dataframe for all websites and export to .csv 'Datum' 'Keyword' 'Tags' 'Categorie' 'Visibility_Expert' 'Visibility_Coolblue' 'Visibility_Mediamarkt' 'Visibility_Wehkamp' 'Visibility_BCC' 0 True './workfile/visibility.csv' False Notice that we also have created a DataFrame with visibility scores for all websites and categories. In step 3 we are going to plot rankings per categories as well. So why not create a DataFrame for that purpose right now with a very similar line of code. ranking = df_workfile[[ , , , , , , , , , ]]
ranking.fillna( , inplace= )
ranking.to_csv( , index= ) #we are going to use the following export later on 'Datum' 'Keyword' 'Search_Volume' 'Tags' 'Categorie' 'Rankings_Expert' 'Rankings_Coolblue' 'Rankings_Mediamarkt' 'Rankings_Wehkamp' 'Rankings_BCC' 0 True './workfile/ranking.csv' False A final tweak and we are ready for some plotting! Just to keep things organized a new DataFrame is created for plotting visibility. dataset = pd.read_csv( ) dataset.fillna( , inplace= )
dataset.groupby([ ]).mean().reset_index()
dataset[ ] = pd.to_datetime(visibility[ ], format= , infer_datetime_format= )
vis.info() #new dataframe './workfile/visibility.csv' #with some edits 0 True 'Categorie' 'Datum' 'Datum' '%Y%m%d' True Plot it like it's hot! pivot = dataset.groupby([ ]).mean()
pivot.plot(kind= ,title= )
plt.xlabel( , fontsize= )
plt.ylabel( , fontsize= )
plt.style.use( )
plt.show() 'Datum' 'line' 'Visibility Overtime' 'Period' 14 'Visibility in Google Organic Search' 14 'Solarize_Light2' This is already a nice-looking chart, but this is a visibility score for all categories. Let’s create an interactive widget to filter the data per category! tags = widgets.Dropdown(
    options=[ ] + list(dataset[ ].unique()),
    value= ,
    description= ,     
) > :
        print( ) : 
        dataset2 = dataset.copy() tags != :
            dataset2 = dataset2[dataset2.Categorie == tags]
            plt.style.use( )
            plt.show() len(dataset2) > :
            dataset2.groupby([ ]).mean().plot(kind= ,figsize=( , ))
            plt.xlabel( , fontsize= )
            plt.ylabel( , fontsize= )
            plt.title( .format(tags), fontsize= )
            plt.style.use( )
            plt.show() : 
            print( ) display(HTML( )) interactive(plotit, tags=tags) 'All' 'Category' 'All' 'Category:' : def plotit (tags) if 0 1 'Nothing to show' else if 'All' 'Solarize_Light2' if 0 'Datum' 'line' 14 4 'Period' 14 'Visibility in Google Organic Search' 14 'Search Visibility in Google for {}' 20 'Solarize_Light2' else 'Nothing to show' #personally, I like showing this full-width with some styling "<style>.container { width:100% !important; }</style>" #and call the interactive widget Let me add some screenshots of the output. 3: Plot rankings overtime per category and keyword The Search Visibility per category can pinpoint categories where action can be required (or just to show of how good things are going 😊). But, especially in the case of ‘action required’, it needs some more information. Which keywords are losing its rankings? Over what time period? In the previous step we already created a dataset for the rankings. It is time to get this data into a DataFrame and make some final tweaks. rnk = pd.read_csv( ) 
rnk.fillna( , inplace= )
rnk[ ] =  pd.to_datetime(rnk[ ], format= , infer_datetime_format= )
rnk[rnk.eq( )] = np.nan
rnk.info() './workfile/ranking.csv' 0 True 'Datum' 'Datum' '%Y%m%d' True 0 Also, with keyword rankings, a similar widget can be very useful. tags = widgets.Dropdown(
    options=[ ] + list(rnk[ ].unique()),
    value= ,
    description= ,  
) > :
        print( ) : 
        dataset2 = rnk.copy() tags != :
            dataset2 = dataset2[dataset2.Categorie == tags]
            plt.show() len(dataset2) > :
            dataset2.drop( , axis= , inplace= )
            dataset2.groupby([ ]).mean().plot(kind= ,figsize=( , ))
            plt.xlabel( , fontsize= )
            plt.ylabel( , fontsize= )
            plt.gca().invert_yaxis()
            plt.title( .format(tags), fontsize= )
            plt.style.use( )
            plt.show() : 
            print( )

interactive(plotit, tags=tags) 'All' 'Categorie' 'All' 'Category:' : def plotit (tags) if 0 1 'Nothing to show' else if 'All' if 0 'Search_Volume' 1 True 'Datum' 'line' 14 4 'Period' 14 'Rankings in Google' 14 'Rankings in Google for category {}' 20 'Solarize_Light2' else 'Nothing to show' Again, here are some examples of the output. If you notice some improvements or drops in rankings per category, a deep dive per keyword can be very interesting! kws = widgets.Dropdown(
    options=[ ] + list(rnk[ ].unique()),
    value= ,
    description= ,        
) > :
        print( ) : 
        dataset2 = rnk.copy() kws != :
            dataset2 = dataset2[dataset2.Keyword == kws]
            plt.show() len(dataset2) > :
            dataset2.drop( , axis= , inplace= )
            dataset2.groupby([ ]).mean().plot(kind= ,figsize=( , ))
            plt.xlabel( , fontsize= )
            plt.ylabel( , fontsize= )
            plt.gca().invert_yaxis()
            plt.title( .format(kws), fontsize= )
            plt.style.use( )
            plt.show() : 
            print( ) 
            
interactive(plotit2, kws=kws) 'All' 'Keyword' 'All' 'Keyword:' : def plotit2 (kws) if 0 1 'Nothing to show' else if 'All' if 0 'Search_Volume' 1 True 'Datum' 'line' 14 4 'Period' 14 'Rankings in Google' 14 'Rankings in Google for keyword [{}]' 20 'Solarize_Light2' else 'Nothing to show' 2 screenshots of rankings per keyword overtime. 4: Create a pivot dashboard for specific insights The final step is to use the pivot method to create nice overviews for a keyword group per website. The fun part of this isthat it gives very specific information about keyword groups. For example: in which period did my keywords lose rankings? Or: in what time period an improvement shows after optimization X or Y? This can be a useful reporting method when a feature has been released or a linkbuild campaign has rolled out. Or… whatever you want to keep track on. pivot_df = pd.read_csv( )
pivot_df[pivot_df.eq( )] = np.nan
pivot_df[ ] = pivot_df[ ].astype(int)
pivot_df[ ] =  pd.to_datetime(pivot_df[ ], format= , infer_datetime_format= ) './workfile/ranking.csv' 0 "Search_Volume" "Search_Volume" 'Datum' 'Datum' '%Y%m%d' True Because, in this case, I am mostly interested in the performance of the rankings of BCC, I only need to call rankings for this website. piv = pivot_df.pivot_table(index=[ , , ],columns = [ ],values=[ ])
rankings = pivot_df[[ , , , , , ]]
rankings.sort_values(by= ) 'Keyword' 'Categorie' 'Search_Volume' 'Datum' 'Rankings_BCC' 'Datum' 'Keyword' 'Categorie' 'Search_Volume' 'Tags' 'Rankings_BCC' 'Datum' Finally, we can create an interactive pivot table inline. There is an option to pop out the pivot table to have a new window pop up with a very nice overview of the information you have collected. The URL of this location will be something like >> . http://localhost: YOURPORT}/files/{YOUR_LOCATION}/reporting/pivottablejs.html Playing a bit with some filters it is clear that some fluctuations are going on for the which can lead to actionable things to work or focus on! washing machine category of BCC To wrap up If you are using any kind of rank tracker to monetize keyword rankings in Google it can be very useful to get instant actionable information and keep track of the effect of changes that had been made. Python in combination with the Pandas and Matplotlib libraries can help with this. A lot of rank trackers already can show this kind of information, but I think it is more fun to create this with Python. The most important reason for me is multilingual. Understand the data / rankings better Get used to work with Python and visualization Customize visualization Instant speed Make a shareable dashboard for stakeholders The last point is an interesting one because not everyone in your company has access to third party SEO tools (in my case SEMrush). When putting this script and pivot table to the public (or local) web, it is possible to have instant access for everyone with a dashboard URL. And because SEO can be very difficult for some people to understand sharing performance and effect of optimizations helps creating support by different stakeholders and the board. The full script can be downloaded from my Github >> . https://github.com/marcvh80/seo-dashboard Also published at https://medium.com/how-to-create-your-own-actionable-seo-dashboard/how-to-create-your-own-actionable-seo-dashboard-with-python-86d5767dbd90

How to Create an Actionable SEO Dashboard with SEMRush and Python

The importance of user experience is more emphasized than ever. As a result, the number and variety of dashboard tools is on the rise. These tools are used as an essential piece in any good customer experience strategy. But, here comes the hard part. How can you create a that provides users with a simple and swift access to the information they need on a website? dashboard UI Before we move onto creating the perfect dashboard, let’s first answer one highly important question: What is a Dashboard Interface? Dashboards are a powerful opportunity and boost retention rates. By using the data collected through research, designers can create a dashboard that displays actionable and relevant data. to influence user behavior If you design a proper and modern dashboard, it will give the users easy access to the information they need. For this to happen, the dashboard information needs to be easy-to-scan and contain only the key information. But, with many tools available, it can be hard to make the right choice or not exaggerate with the use of features. This is one of the many and as such, it demands knowing certain tricks to be handled the right way. roles of a UX designer proves that the human brain can’t process a vast scope of information at the same time. Cognitive psychology Thankfully, for all those who’ve been wondering how to make a dashboard interface, here are the top 9 ways that make all this possible. 1. Understand the Needs of the User The data a designer collects through various user research methods can seriously impact the quality of the design and the user experience. To create a dashboard, you need to perform such research, too. When you research the target audience, you will learn what they want and need. Based on that information, you can determine what is the most important data to use in the dashboard. 2. Decide on an End Goal No matter how creative you are, you can’t simply go with the flow and see where your gut takes you. If you want to succeed in your dashboard design, you need to have many end goals in place. Among those , one is making the decision of what your dashboard will present. end goals 3. Pick a Dashboard Type According to , there are four main types of dashboards to choose from: DataPine : Dashboards used for business intelligence purposes. Their goal is to measure, monitor, and manage operations with a more immediate or shorter time scale. Operational : Unlike the operational dashboards, strategic dashboards are used for long-term strategies of a business. They work by analyzing the most essential information based on trends. Strategic : Dashboards full with an array of data. This data is being used mostly to help formulate the strategies for growth in mid-management circles. Tactical : Dashboards with large bits of comprehensive data. Analytical Such data is used by analysts to help them create insights that help the business grow at an executive level. If you aren’t certain which type is for you, the best way to find out is by example. Seek dashboard inspiration in your competition. 4. Use the Five-Second Rule Dashboards give users access to relevant information. Their main purpose is to resolve the frequently asked questions within a short timeframe. With that in mind, use the five-second rule. How can you do this? Well, keep in mind that a user will be willing to spend five seconds to find the information they need. Does your dashboard provide this? If it doesn’t, this might be the reason for the problems you’re facing. low app retention rate 5. Go Minimalistic You shouldn’t go overboard with a dashboard. Why is this? , if a dashboard has multiple visualizations, it will be crammed with too much information. According to MidInventory Here is a clue: proves that the human brain can’t process a vast scope of information at the same time. Our scope is rather limited, which makes minimalism your very best option. Cognitive psychology 6. Put Emphasis on Information Architecture Everything from data hierarchy to layout is important for the success of your dashboard. But still, your design needs a balance to function. To achieve a balance, aim for a design that emphasizes the most important elements over the less important ones. 7. Choose Your KPIs Carefully A successful dashboard design is a KPI (Key Performance Indicator) dashboard design. What does this mean? KPIs are metrics used to display a visual representation of the most relevant data based on different areas of your business or brand. Once again, you can use your audience research to select the best KPIs to use in the dashboard. 8. Provide Context How are users supposed to get your message if you don’t provide context and comparison values? The numbers on a given dashboard carry no meaning without context. Also, the users won’t know if you require any action on their behalf. When designing, provide maximum information in the provided space. Even if the data seems obvious to you, it can still appear strange to the user. Therefore, provide comparison values and add titles to all of the charts you use. 9. Don’t Forget about Aesthetics We live in a society that’s highly focused on aesthetics. That’s why every designer needs to treat his dashboard as a publisher would design his book. Why, you might ask? As the saying goes, books are judged by their cover. So is a dashboard. It is judged by its appearance. Therefore, when you finish with the data and hierarchy of it, it’s time to move onto the dashboard’s aesthetics. Pick , format, font, and layout. the right colour combination Regardless of your experience in the field, good practices like the ones above will surely make your dashboard stand out. When you put your time and creativity into it, you can influence a huge portion of the target audience. Creating a useful and helpful dashboard is only one step in that direction, but a really important one. ( Disclaimer : The author is the Founder at Adam Fard Studio ) (Originally published ) here

Best Practices for Designing a User-Centric Dashboard

Imagine that you need to develop a web application from scratch to monitor and analyze raw data. For example, you own an average size online store. You are not satisfied with the existing solutions for data analytics and you want to manage what components will be in your admin panel by yourself. It is also very important for you to work with raw data and build analytical charts yourself. Often, you have to work with bulky libraries and write your own web application, which takes an enormous amount of time. The solution comes from Cube.js + Flatlogic dashboards. The best way to start is to use one of and . Flatlogic React’s dashboards Cube.js You can check out the final dashboard here. The source code is available on Github here. Let’s Build the Dashboard In this example, we will use the in order to eliminate steps of setting up a react environment from scratch and to have a dashboard interface from the beginning. Sing App React template The first step is connecting your database with Cube.js. First of all, you need to install , , and recharts as a charts library. Then, you need to find the API token in the project settings. After getting and installing all needed “equipment,” you have to import them into your react component and apply the API token. @cubejs-client/core @cubejs-client/react { LineChart, Line, XAxis, YAxis } ; cubejs ; { QueryRenderer } ; cubejsApi = cubejs( ); 1 import from 'recharts' 2 import from '@cubejs-client/core' 3 import from '@cubejs-client/react' 4 5 const `api-token` Now you are ready to get any data you like. According to the docs, here’s how your component with chart should look: <QueryRenderer query={{ measures: [ ], dimensions: [ ] }} cubejsApi={cubejsApi} render={({ resultSet }) => { (!resultSet) { ; } ( <LineChart data={resultSet.rawData()}> <XAxis dataKey= /> <YAxis/> <Line type= dataKey= stroke= /> < > 2 3 'Stories.count' 4 'Stories.time.month' 5 6 7 8 if 9 return 'Loading...' 10 11 12 return 13 14 "Stories.time" 15 16 "monotone" "Stories.count" "#8884d8" 17 /LineChart>
18    );
19  }}
20/ is the component from the Cube.js library. Let’s split it on in props. QueryRender The main property is . There you can define what data to get from Cube.js. query You don’t have to know all Cube.js concepts to implement because you can easily configure any query in Cube.js then export it in JSON format and paste it right into your component. query QueryRender The render property of gets a function that returns whatever you want. In our case, we want to return the chart with already received data. The best feature is that you can return the loader while you are receiving data. Another lovely feature is the ability to export query data in different formats, like CSV or xlsx, right from the Cube.js dashboard. QueryRender Advantages Implementation speed and easy-to-understand API Cube.js is visualization agnostic. This means you can use your favorite chart library, like Chart.js, Recharts, C3.js, or any other. Cube.js Data Schema works as an ORM for your analytics. It allows you to model everything from simple counts to cohort retention and funnel analysis. It is designed to work on top of your database, so all your data stays with you. All major SQL databases are supported. The Final Result Now with Cube.js and Flatlogic Dashboards, you can easily start your own web app with custom graphics and connect any database to visualize raw data. Here is an example using e-commerce data. You can play with it here. Originally published at https://statsbot.co/blog/building-analytics-react-dashboard-with-cube.js

Building Analytics React Dashboard With Cube.js

Most of the time in the part of development in software application, users need to have an admin page to show multiple data and information such as sales performance, operation performance, etc. As one of PHP framework, laravel built an administrative panel called . Nova is a beautifully designed administration panel for Laravel. By using this tool, you can easily create and manage your admin panel of your website without having create it from scratch. The cool thing about Nova is it developed using PHP or Vue.js because of Laravel compatibility. Hence it makes your admin panel can be flawless and writing custom components is a cinch. laravel nova On this article, I want to share about one package from laravel nova that can provide a nice chart with simple configuration, called Nova ChartJS / Chart JS Integration. Preparation I assumed that you already has laravel nova that can be downloaded , so that we can focus on create our custom dashboard. First, download component from your bash: here nova-chartjs composer require coroowicaksono/chart-js-integration Basic Usage After installation completed, Open your nova service provider in as a default dashboard for Laravel Nova and edit function cards with this line code: App\Providers\NovaServiceProvider.php ( StackedChart())
    ->title( )
    ->series( ([ => , => , => , => [ , , ],
    ],[ => , => , => , => [ , , ],
    ]))
    ->options([ => [ => [ , , ]       
        ],
    ])
    ->width( ), new 'Revenue' array 'barPercentage' 0.5 'label' 'Product #1' 'backgroundColor' '#ffcc5c' 'data' 30 70 80 'barPercentage' 0.5 'label' 'Product #2' 'backgroundColor' '#ff6f69' 'data' 40 62 79 'xaxis' 'categories' 'Jan' 'Feb' 'Mar' '1/3' So your code will be look like this: After that, you can save the file and see your dashboard should be like this: By using , there are multiple type of chart that we can use, e.g. , , , and . this tool Bar Chart Line Chart Area Chart Doughnut Chart Pie Chart Use Resource / Model The next question is, how we get data from our database? With this tool, you can easily re-use your defined laravel model. You only need to add this line to your cards function, and change the model with your defined model ( StackedChart())
    ->title( )
    ->model( ) ->width( ), new 'Revenue' '\App\Models\Sales' // Use Your Model Here '2/3' Since I already has a defined model, then my dashboard will be look like this: sales Afterwards At the end, there are a lot of package that you can easily download from . Chart JS Integration is one of many package that can save your time and help you maintain consistency across standard elements such as Bar, Stacked, Line, Area, Doughnut and Pie Chart. laravel nova package For more documentation related this tool, you can take a look at . Nova ChartJS documentation Source: https://medium.com/@coroo.wicaksono/create-your-own-dashboard-easily-with-laravel-nova-c5fe4554ba17

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