Embedded Analytics in the Self-Service BI Enterprise

Table of Contents

  1. Summary
  2. Why Organizations Need Visual Data Discovery
  3. Accessing Data: Where It Lives and Who Sees It
  4. Reporting in a Self-service BI World
  5. Key Takeaways
  6. About George Anadiotis
  7. About Izenda

1. Summary

Production reporting has been around for a long time, but its requirements change as much as the tech industry itself does. Delivery expectations have shifted from quarterly to hourly, so the entire business intelligence stack must now be user-driven and flexible. Licensing by number of users is obsolete, since there is no way to predict who will need the reports or who will create them. And in the mobile era, sending users to IT for their reports is doomed to failure, and desktop apps are legacy technology.

If the learning curve for reporting isn’t low, adoption will be. Reporting and analytics must be embedded inside applications and end-users must be able to use their interfaces not just for running reports but also for designing them. Highly complex reports shouldn’t take two expensive developers to build if one single client services person can do it instead.

This report is for executives, product managers and developers at independent software vendors (ISVs), SaaS vendors, solutions providers, or anyone building business applications. It will investigate how reporting needs, capabilities, and implementation requirements have changed, and present a new set of “do’s and don’ts” for successful embedded analytics.

Key findings in this report include:

  • It is imperative to embed modern BI (reporting, dashboards, and visualizations) in the application users’ daily work. A generation of users that has been brought up on mobile and browser-based applications that are self-contained is unlikely to accept switching to a separate application to access BI features.
  • Democratizing access to data is key, and can be achieved by enabling access to transactional databases (or read-only copies) instead of creating separate analytical databases. In doing so, the burden on IT or the data science team is minimized, and users are empowered to access data on their own.
  • Democratized access to data should not equal a lack of control. There must be a mechanism for user authorization and access rights, as access to the reports and data should be restricted to only the appropriate users for any given scenario.
  • Reporting functionality should have a professional look and feel that is natural for users. Reports should be easy to create, embedded in applications, and have production-level polish.
  • ISVs and solution providers now face a build-versus-buy strategy for BI implementation. Arguments tend to favor “buy” because these organizations lack core BI expertise and often spend too much time and too many resources building and maintaining it in-house.

Thumbnail image courtesy of Sergey Nivens/iStock.

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