Today, many organizations are developing data-intensive applications that include interactive dashboards, infographics, personalized data visualizations, and charts that respond to a user’s data entitlements. In cases where an application needs to display a bar chart or other simple data visualization, it’s easy enough to use a charting framework to configure the visual and render the chart. But a data visualization platform’s embedded analytics capabilities may offer richer end-user experiences and tools to support easier and faster enhancements.
Embedding analytics can be a powerful approach to enhancing applications when experimentation around the visualizations is important. For example, an application’s product owner may start with a simple visual but then realize that different user personas require specialized dashboards. A data visualization platform makes it a lot easier to develop, test, and iterate on these dashboards rather than coding 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 into code, the visualizations are iteratively improved by a group of people who best know the business need, the data, 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 required.
- Analytics can be embedded into an enterprise system that includes data from several other data sources. An example is a dashboard for sales managers displayed within the customer relationship management (CRM) application that includes 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 of 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 want to pursue data journalism, in which a journalist writes an article about a data set and one or more data visualizations, and data and analytics are the foundation of the story.
- Marketing infographics, including graphic designs or data visualizations, are embedded in websites and other marketing tools.
- For businesses trying to be data driven, this may be the opportune time to select a data visualization platform to develop analytics and embed them in enterprise or customer-facing applications.
- Organizations 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 may be data visualizations for data products and services. The approach is common for data, financial services, insurance, and e-commerce businesses where the data is the product, and analytics can be a differentiator. In these cases, using a data visualization platform to develop the product and leveraging the platform’s flexibilities to embed it in 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 learn more about the data and what insights are useful, they are 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 may 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 used on the web, on mobile, or both? This requirement qualifies the screen dimensions, number 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 a 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 are 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 that provide consistent user experiences.
- Review the data visualization embed options that often include easy-to-implement iframe integrations, REST APIs, and JavaScript SDKs.
- As the data can change, it is a best practice to create 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 some 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 useful as stand-alone tools, they can deliver greater business value when embedded in customer-facing and internal workflow applications.