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

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!

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