Here’s a detailed list of considerations when reviewing embedded analytics capabilities:
- Do the chart types meet business needs? Data visualization tools compete on the breadth and variety of their chart types, as well as on the flexibility of their configuration.
- Do the layout capabilities and device compatibility meet your needs? When you embed a visualization, you need to review how it fits and interacts within the layout(s) of your application. The visualization should take advantage of the full screen and responsively adjust for mobile device layouts.
- How easy is it to integrate? Review whether the platform’s approaches to embed analytics into applications meet business needs and are easy to implement. For easy integration, there should be simple embed codes to drop the visualization into HTML, but you should also review the APIs in case additional flexibility is required. For example, if you want to pass parameters from the application to the data visualization, you’ll want to make sure this level of API is exposed.
- Can you extend the platform with interactivity and workflow? After you embed a visualization, verify whether it meets business requirements. In addition to checking functionality built into the platform, like changing sort orders, selecting the metrics used in visualizations, choosing which columns to display in a table, or switching between chart types, you’ll want to verify you can extend the platform’s functionality in the ways that you need, especially if you want users to update the underlying data.
- Is the security configurable for the required end-user entitlements? If you are building applications where different groups and users need access to different data views, review how the platform enables row-level and column-level security. Verify that the user login can trigger the data entitlements and that visualizations properly adjust for the accessible data.
- Do visualizations perform fast enough to be embedded in an application? Performance expectations vary depending on how end-users leverage the visualizations in analysis and workflow. When a data visualization is accessed by a user of a BI application, there is typically a higher tolerance for latency because the users are more sensitized to the quantity of data and the complexity of the analytics. By contrast, users of applications in which data visualizations are only part of the user experience are likely to have greater expectations of snappy performance. Further, in the case of visualizations embedded in public-facing web pages that require search-engine optimization, fast page loads are critically important to ensure page rank is not penalized by a slow visual.
- How “real-time” are your data requirements? Related to performance is whether the platform enables real-time access to data sources or whether computing analytics on cached or aggregated data is sufficient. There’s often a trade-off between real-time data availability, performance, and implementation complexity, so having the controls to switch from real-time to scheduled updates and validating performance are required for larger data sets.
- Are the development capabilities flexible and scalable? When you incorporate embedded analytics in an application development cycle, you want to ensure the embedded analytics platform fits your requirements for version control, development, deploying workflow, testing practices, and continuous integration.
- Are the platform’s pricing and total costs aligned with your business model? If you’re going to embed a visualization and provide access to thousands of users, make sure the pricing and costs are aligned with the application’s usage model. Modeling the costs is particularly important when visualizations are embedded in customer-facing applications because the data visualization platform’s per-user charge could amount to a significant percentage of your total expenses.