Precisely why embed analytics plus data visualizations within apps

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These days, many organizations are usually developing data-intensive programs that include interactive dashes, infographics, personalized information visualizations, and graphs that respond to the user’s data entitlements. In cases where an application has to display a club chart or additional simple data visual images, it’s easy sufficient to use a charting construction to configure the particular visual and provide the chart. Yet a data visual images platform’s embedded analytics capabilities may provide richer end-user encounters and tools to back up easier and quicker enhancements.

Embedding analytics could be a powerful approach to improving applications when testing around the visualizations is essential. For example , an application’s product owner may start using a simple visual however realize that different consumer personas require specific dashboards. A information visualization platform can make it a lot easier to develop, check, and iterate upon these dashboards instead of coding visuals.

Another important benefit of using information visualization platforms is the fact that data scientists plus subject matter experts may participate in the application advancement process. Instead of getting write requirements for any software developer in order to translate into code, the particular visualizations are iteratively improved by a crowd who best know the business need, the info, and best practices in data visualizations.

Why you should use data visualization tools

Let’s look at some use cases to embed data visualizations when rapid development and experimentation are needed.

  • Analytics can be embedded into an enterprise system that includes data from several other data sources. An example is really a dashboard for sales managers displayed within the customer relationship management (CRM) application that features financial data from the ERP (enterprise resource planning) system and prospecting data from marketing automation platforms.
  • In customer-facing mobile and web applications, a simple chart or graph can drive user interaction. Think about a stock-trading application that charts stocks on an investor’s watch list and highlights ones near their low prices when it’s potentially the right time to buy.
  • Media organizations and others that publish content may choose to pursue  data journalism , in which a journalist writes an article in regards to a data set plus one or more data visualizations, and data and analytics would be the foundation of the story .
  • Marketing infographics , including graphic designs or data visualizations, are embedded in websites along with other marketing tools.
  • For organizations trying to be data driven , this may be the opportune time to decide on a data visualization platform to produce analytics and embed them in enterprise or customer-facing applications.
  • Businesses that are already using data visualization tools may need to extend a visualization with custom integrations and functionality to manipulate or process data through a workflow.
  • Entire customer-facing applications might be data visualizations for data products and services. The approach is common for data, financial services, insurance, and e-commerce businesses where in actuality the data is the product, and analytics can be quite a differentiator. In these cases, employing a data visualization platform to develop the product and leveraging the platform’s flexibilities to embed it in still another system enables teams to innovate and support rapid enhancements.

Embedding analytics drives innovation 

What’s different about data visualization is that the requirements, design, and functionality required are likely to be highly iterative. As more stakeholders and users find out about the data and what insights are useful, they have been likely to modify the requested experience, design, and functionality.

That’s why, even though visualization libraries may be easy to use for the developer, they could not be an optimal development approach for embedding analytics where frequent iterations are required. Iterative design is especially the case in journalism and marketing where the goal is to let users design, develop, and publish data visualizations without requiring support from developers and technologists.

Steps to embedding analytics in apps

When thinking about embedding analytics in applications, review these development considerations:

  • Who are the users, and what questions are you helping them answer with the analytics? The best dashboards and data visuals answer specific questions and perform a business function rather than just reporting on data.
  • Will the app be utilized on the web, on mobile, or both? This requirement qualifies the screen dimensions, quantity of charts, and data volume considerations that developers must factor into the design.
  • How much data needs processing, and what are the performance requirements? For larger data sets and greater performance, using database materialized views , in-memory databases , and visualizations on aggregate data may be necessary.
  • What data governance and security define an user’s data entitlements? Developers should dimensionalize these rules as use cases and create test scenarios to validate that implementations adhere to data governance. Furthermore, the visuals may need modifications when there will be significant row- and column-level data governance rules.
  • Teams should develop standards and a center of excellence on data visualizations that guide chart types, color schemes, labels, style guides, and other rules offering consistent user experiences.
  • Review the data visualization embed options that frequently include easy-to-implement iframe integrations, REST APIs, and JavaScript SDKs.
  • Because the data can change, it’s a best practice to produce test automations on data visualizations that run in continuous integration and continuous  delivery (CI/CD) pipelines but can also run as application monitors alerting on production incidents.

These are a few of the steps developers, data scientists, and agile teams should include when embedding analytics in apps.

Want some inspiration? Review the analytics on Tableau Public , Microsoft Power BI Galleries , Sisense example dashboards , and Qlik gallery for examples. While many dashboards are helpful as stand-alone tools, they can deliver greater business value when embedded in customer-facing and internal workflow applications.

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Follow our CRM News page for breaking articles on Customer Relationship Management software. Find useful articles like How to Choose a CRM System, CRM 101, the CRM Method and CRM and the Cloud. And when you're ready let us help you find the right Customer Relationship Management software.

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