Three Steps Product Owners Must Consider When Planning for Insights-Driven Applications

What does the future of embedded analytics look like? For starters, as we get closer to a world where analytics is everywhere, we’re starting to see enterprises transforming to become more insights-driven. These enterprises are using actionable insights to their competitive advantage, and Forrester predicts that in the next three to five years, embedded BI that is contextual, actionable, and prescriptive will become the new norm for operational and tactical insights. And as other enterprises look to capitalize on these benefits, I think we can expect to see analytics become embedded into every business application as insights become essential to delivering business results.

But let’s take a step back and look at what needs to happen before insights-driven applications are deployed at the enterprise level, because this trend also has implications for the teams who actually build these insights-driven applications. Embedded BI vendors and IT teams developing data-driven applications must deliver on a new set of capabilities, namely the ability to deliver contextual, actionable information that empowers a broad network of users across the enterprise. During product strategy discussions, the product owner must determine how the insights derived from data play a role in the future product—and how data is used to augment decision-making as part of business process(es).

We’ve found that successful product teams start out by following three steps:

Step 1: Determine whether embedded BI is a good fit for your application

The first thing to consider is whether embedding BI into your application aligns with your overall product strategy. Put simply, does embedding BI make sense when you think about what you want this application to be able to do in the future?

To figure this out, I find it’s helpful to think about your business users. Are they data savvy or data novices? Would embedding BI improve their daily workflows, or would things stay pretty much the same? True embedded BI tends to be a great fit for products that target business users, like teams in sales, marketing, HR, customer support, insurance underwriting, or facilities management—but not such a great fit for products designed for small teams or only a few customers. In addition, consider the business process your product supports. Business processes where one or more personas make frequent decisions—on a daily basis rather than an ad-hoc basis—are likely to be a good fit for embedded BI.

Step 2: Review in-product decision-making

Next, consider how decisions are made as part of the business process, and begin thinking about how that decision-making process could be improved. How are decisions made today in your application? Is decision-making already part of the business process, with actions tightly tied to those decisions? Or are decisions made completely separately from the business process?

I also encourage you to look at the adoption of your product. Are most users logging in every day? Every hour? If not, why? What if you could deliver a capability that would inform a business critical process? Would that improve adoption? If so, you might be a good fit for embedded BI.

Step 3: Determine what’s urgent

The last area I would encourage you to think about is to determine how urgent embedding BI is—and be honest. Many companies tend to put off innovative projects because they think they’re too busy addressing a backlog of other requests, but staying in this mode where you’re constantly reacting to requests without taking the time to step back and consider the best way forward can be detrimental to your product.

Ask yourself if your current analytics roadmap is connecting users to critical insights or if some features are merely stepping stones until something better can be delivered (these stepping stones can be some of the biggest roadblocks to innovation). For example, we see many cases where SaaS products provide a myriad of reports to users and hope that users find what they are looking for. Later, they learn that analytic adoption is low, because users either can’t find what they’re looking for or they can’t make sense of what they’ve found. So take the time to review your product roadmap and ensure that you’re focusing on what’s actually going to provide value—like making it easier for users to use your application—not updates for the sake of checking off a feature checkbox.

Once you’ve done your homework and considered these three ideas, the next step is to actually get into the nitty gritty of building an insights-driven application.

Curious about key areas to focus on during the build? I’ll dive into that in my next post.

Already starting your evaluation? Make sure your IT team or vendor can support these next gen Embedded BI capabilities.