Tech Trend

How to choose the right data visualization tool


When developing an application that shares data with users, you may need to present a visualization of graphs, charts, dashboards, or other data embedded in the application. This feature helps users better understand the data, discover insights, and improve the user experience. Looking at a well-designed data visualization, you use more of your application and are happy with the results.

Developers may be thrilled to use the code to develop charts and graphs. There are many chart frameworks that you can use to customize your data visualization. However, before embarking on an approach that requires frameworks, libraries, and coding, it’s a good idea to consider a data visualization tool with built-in analytics. Modern data visualization tools not only make it easier to create visualizations, but often provide the ability to embed or deliver visualizations directly into web or mobile applications.

Also on InfoWorld: How to choose a data analytics platform ]

In contrast, visualization libraries may be easier for developers to use, but they may not be the best development approach for embedding analyzes that require frequent iterations. This is especially true in areas such as journalism and marketing. In this area, the goal is to enable users to design, develop, and publish data visualizations without the need for developer or engineer support.

Criteria: How to choose a data visualization tool with built-in analytics

Many data visualization tools such as Tableau, Microsoft Power BI, Looker, Sisense, GoodData, Qlik, ThoughtSpot, etc. provide data visualization embedding capabilities. If your organization is already using one of these tools, start there. If not, try prototyping and proof-of-concept deployments using several tools to learn their features. Prototypes help you validate graph types, assess the ease of developing data visualizations, and determine if application integration options, security configurations, and operational requirements are fit for your environment.

The following is a detailed list of considerations when reviewing embedded analytics capabilities.

  • Does the chart type meet your business needs? Data visualization tools compete for the breadth and variety of chart types and configuration flexibility.If your organization wants widespread use Box plotMake sure that the tool is not only of this graph type, but also available in the way your organization requires it.
  • Do layout features and device compatibility meet your needs? When embedding a visualization, you need to see how the visualization fits and interacts within the layout of your application. The visualization is full screen and needs to be adjusted according to the layout of the mobile device.
  • How easy is the integration? See if your platform’s approach to incorporating analytics into your application meets your business needs and is easy to implement. For easy integration, you need a simple embed code to drop the visualization into HTML, but if you need additional flexibility, you should also check the API. For example, if you want your application to pass parameters to your data visualization, you need to make sure that this level of API is exposed. In addition, many applications require some form of authentication, so we will verify that platform integration easily works with single sign-on services.
  • Can you extend the platform with interactivity and workflow? After embedding the visualization, check to see if it meets your business requirements. In addition to reviewing the features built into the platform (changing the sort order, selecting metrics used for visualization, selecting columns to display in the table, switching graph types, etc.), the platform has been enhanced. You need to make sure you can. This is a necessary method, especially if you want the user to update the underlying data.Some data visualization platforms allow developers to explore the full capabilities of the platform and future technological directions: Extend visual capabilities using APIs..
  • Can I configure security for the required end-user entitlements? If you’re building an application where different groups and users need access to different data views, see how your platform enables row-level and column-level security. Make sure that the user login can trigger data entitlement and that the visualization is properly tuned for accessible data. You also need to have administrator-level tools on your platform to review your visualizations as different users and verify that your visualizations reflect the appropriate data entitlements.
  • Does the visualization run fast enough to be embedded in the application? Performance expectations depend on how end users leverage visualization in their analytics and workflows. When users of BI applications access data visualizations, they are usually more tolerant of latency because they are sensitive to the amount of data and the complexity of the analysis. In contrast, users of applications where data visualization is only part of the user experience can have high expectations for snappy performance. In addition, for visualizations embedded in public web pages that require search engine optimization, fast page loads are very important to avoid being penalized by slow page-ranked visuals.
  • How “real-time” are your data requirements? Performance is relevant whether the platform allows real-time access to data sources, or whether compute analysis of cached or aggregated data is sufficient. There is often a trade-off between real-time data availability, performance, and implementation complexity. As a result, large datasets need controls to switch from real-time to scheduled updates to validate performance.
  • Are development features flexible and scalable? When incorporating embedded analytics into the application development cycle, the embedded analytics platform provides version control, development, workflow deployment, test practices, and Continuous integration..
  • Do platform pricing and total costs match your business model? Most data visualization platforms have an upfront cost and a per-user fee. If you embed a visualization and provide access to thousands of users, make sure that the price and cost match your application’s usage model. Cost modeling is especially important when visualization is embedded in customer applications, as per-user fees for data visualization platforms can account for a significant percentage of the total cost.

One of the main considerations is whether business stakeholders are ready to define a user experience and design that matches the capabilities of the platform. Standardizing the visualizations provided by these platforms often benefits. Best practices for graph types, color schemes, labeling, etc. It is usually baked with.

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