Why embed data analytics and visualizations in apps

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Today, many organizations are developing data-intensive applications that include interactive dashboards, infographics, custom data visualizations, and charts that respond to a user’s data access rights.

In cases where an application needs to display a bar graph or other simple data visualization, it is quite easy to use a charting framework to set up the visual and display the graph. But the built-in analytics capabilities of a data visualization platform can deliver richer experiences and tools to the end user to support easier and faster improvements.

Integrating analytics can be a powerful approach to improving applications when experimentation around visualizations is important. For example, the product owner of an app might start with a simple visual and then realize that different user profiles require specialized dashboards. A data visualization platform makes it much easier to develop, test, and iterate on these dashboards rather than coding the visuals.

Another key benefit of using data visualization platforms is that data scientists and subject matter experts can participate in the application development process.

Instead of having them write requirements for a software developer to translate them into code, visualizations are iteratively enhanced by a group of people who know best the business needs, the data, and the best. data visualization practices.

Why you should use data visualization tools

Let’s take a look at a few use cases for integrating data visualizations when rapid development and experimentation is required.

  • Analyzes can be integrated into an enterprise system that includes data from several other data sources. An example is a dashboard for sales managers displayed in the customer relationship management (CRM) application that includes financial data from the ERP (enterprise resource planning) system and prospecting data from business platforms. marketing automation.
  • In mobile and web applications for customers, a simple graph or chart can stimulate user interaction. Think of a stock trading app that lists stocks on an investor’s watch list and highlights those that are approaching their low prices when it’s potentially a good time to buy.
  • Media and other organizations that publish content may want to pursue data journalism, in which a reporter writes an article about a set of data and one or more data visualizations, and data and analytics is the foundation of the story. .
  • Marketing infographics including graphic designs or data visualizations are integrated into websites and other marketing tools.
  • For businesses trying to be data-driven, now might be the time to select a data visualization platform to develop analytics and integrate it into business or customer-facing applications.
  • Organizations that already use data visualization tools may need to extend a visualization with integrations and custom functionality to manipulate or process data through a workflow.
  • Entire applications for customers can be data visualizations for data products and services. The approach is common for data, financial services, insurance, and e-commerce companies where data is the product and where analytics can be a differentiator. In these cases, using a data visualization platform to develop the product and taking advantage of the platform’s flexibilities to integrate it into another system allows teams to innovate and support rapid improvements.

Integration of analytics drives innovation

What’s different with data visualization is that the requirements, design, and functionality required are likely to be highly iterative. As more stakeholders and users learn more about data and useful information, they are likely to change the experience, design, and functionality requested.

Therefore, while visualization libraries may be easy for the developer to use, they may not be an optimal development approach for integrating analyzes where frequent iterations are required. Iterative design is especially the case in journalism and marketing where the goal is to enable users to design, develop, and publish data visualizations without needing the help of developers and technologists.

Steps for integrating analytics into applications

When considering integrating analytics into applications, consider these development considerations:

  • Who are the users and what questions do you help them answer through analytics? The best dashboards and data visuals answer specific questions and perform a business function rather than just reporting data.
  • Will the app be used on the web, mobile, or both? This requirement qualifies the screen dimensions, number of graphics, and data volume considerations that developers should take into account in the design.
  • How much data needs to be processed and what are the performance requirements? For larger data sets and better performance, the use of materialized database views, in-memory databases, and aggregate data visualization may be required.
  • What governance and data security define the access rights to a user’s data? Developers should size these rules as use cases and create test cases to validate that implementations adhere to data governance. Additionally, visuals may require modification when there are important data governance rules at the row and column level.
  • Teams should develop standards and a center of excellence on data visualization that guide the types of charts, color schemes, labels, style guides, and other rules that deliver consistent user experiences.
  • Review data visualization integration options which often include easy-to-implement iframe integrations, REST APIs, and JavaScript SDKs.
  • Because data can change, it is recommended that you create test automations on data visualization that run in continuous integration and continuous delivery (CI / CD) pipelines, but can also run as Application monitors alerting to production incidents.

These are some of the steps developers, data scientists, and agile teams should include when integrating analytics into applications.

Want some inspiration? See Analytics on Tableau Public, Microsoft Power BI Galleries, Sisense Dashboard Samples, and the Qlik Gallery for samples. While many dashboards are useful as stand-alone tools, they can provide greater business value when integrated into internal and customer-facing workflow applications.

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