Building data-informed products
Stewart Rogers on building and managing products with embedded analytics.
Measurement. (source: Pexels)
Managers have embraced the idea that data can drive value in any industry, but that assumes the right people within their organizations have access to data. Dashboards, report builders, and embedded visualization interfaces have become standard tools for bringing data and analytics to a wide range of data users.
My guest in this podcast episode is Stewart Rogers, director of product management at Lambda Solutions, which offers an open source e-learning management system; its customers are organizations that deliver online learning and need to understand how their students are progressing. Rogers is thus doubly dependent on embedded analytics in his products: he uses analytics to manage his products, measuring customer response to new features, but he also needs to pass meaningful analytics through to his customers in order to let them assess their learners and their content.
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“We’re about two years into our analytics journey,” says Rogers. “The old model was very much just pure report-based: ‘Here’s an opportunity to export some of your data.’ We’ve built it out using Jaspersoft to give [customers] richer, different visual insights that they weren’t able to get before—charts, dashboards, and embedded things.”
The richer reports have been a key selling point for Lambda, but data-savvy customers who have never used a learning management system before “almost expect it to work the way we present it now,” says Rogers. For them, embedded reporting and visualization is essential, he says: “they may have very specific requirements: ‘I need this field labeled this way and sorted this way,’” with highly specific distribution and access requirements as well.
Asked what his most sophisticated users are doing with his platform, and what that might suggest for the future, Rogers points to prediction. “A lot of our customers start with administration-type reports: who’s done what, when, that kind of thing,” he says. “Now they’re going to look more deeply at trends and potential outcomes and try to use that…to say ‘if A, B, and C, then this person is likely to struggle.’”
This post and podcast is a collaboration between O’Reilly and TIBCO Jaspersoft. See our statement of editorial independence.