Unlike traditional BI, discovery hinges on the ability to explore data in a “fail-fast” iterative process that cycles through repetitive steps of accessing the data, exploring it, blending and integrating it (or “interrogating it”) with other data, analytically modeling the data, and finally, verifying and governing the new discovery before operationalizing it back into the enterprise.
As an iterative exercise, discovery inherently places a premium on both the ability to quickly and agilely harness large amounts of a variety of data for exploration, as well as (equally as important) on speed. This isn’t just speed for the sake of speed, either: It’s a fundamental prerequisite of truly enabling the discovery culture in your enterprise. The quicker you can move through the discovery process, the quicker you can arrive at insights that add value back into the business. Finding a worthwhile discovery—especially the first time around—isn’t guaranteed.
This is the essence of fail-fast: If one discovery doesn’t work, toss it aside and keep looking. Sure, it may take only one attempt to discover a valuable nugget, but it also may take 99 iterations through the discovery cycle before one valuable insight is uncovered (if at all). The value of speed, then, can be found at the intersection of actionable time to insight and the ease—or, the “frictionless-ness”—of the discovery process.
Friction is caused by the incremental events that add time and complexity to discovery through activities that slow down the process, such as those that IT used to do for business users—many, if not all, of which can be reduced (or removed completely) with robust self-service, visualization, collaboration, and sharing capabilities. Friction, then, is a speed killer—it adds time to discovery and interrupts the “train of thought” ability to move quickly through the discovery process and earn insight. This is not unlike the old 80/20 concept wherein 80% of time is spent in data preparation, leaving only 20% of the time for data science and exploration. (Some have suggested this ratio is more closely aligned with a 90/10 split.) The more friction that is added into the process, the longer it takes to reach an insight in the data.
Think of it this way: Would data discovery be nearly as worthwhile if analysts had to budget an hour of waiting time for each of the five steps outlined above? And, what if it does take 99 iterations to find one meaningful insight? Do the math: that’s five steps at 1 hour each, 99 times, for a total of 495 hours or 62 days to value. Yikes! But if we pick up the speed—reduce the friction—and drop that hour of waiting time down to 1 minute per step, the time to value is significantly reduced: 1-minute steps, 99 times, is a mere 495 minutes—8.25 hours—to value. How much time would you prefer to spend—2 months or 1 day—before discovering an insight that adds value in your business?
Yes, sometimes there are unavoidable activities (such as governance) that will inevitably add friction to the discovery process. However, the goal is to reduce those as much as possible. As illustrated by the math above, every second counts in discovery. Remember the real value of discovery is to earn insights on data and facilitate interactive and immediate reaction by the business. Breaking down the activities that induce friction in each step of the discovery reduces the barriers to discovery and subsequently increases time to insight. Speed, then, is a function of friction; the less friction in the discovery process, the more value speed can deliver to the business. As friction decreases, time to insight increases—and the more valuable discovery becomes.
Ultimately, it’s a simple equation: Less friction equals faster time to insight. The role of friction in discovery then? As little as possible.
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