By Rachel Lee - August 27, 2021
Today, far more members of a given organization are interfacing with data and using business intelligence (BI) tools than just a few years ago. It would be great if all that access translated to actionable info – and better long-term business outcomes – but for many companies, it doesn’t. The problem isn’t that there’s “too much data” (I’d argue there’s no such thing). And it’s not that businesses don’t want to be more data-driven (they do). What’s happening is better described as a human bottleneck. Simply put, the challenge for most businesses is that a large number of the people to whom data is now accessible don’t have the time or skillset to analyze and put it to use.
Most current BI tools and SaaS platforms only tackle the “what” of data: what is available, what is unavailable, and what has changed. Many of them are great at doing that, but there’s something missing — the why behind those changes.
To understand the importance of why, let’s use consumer credit as an analogy. When something changes with your credit, users are often alerted with an explanation of not just what changed and when it changed, but why. That why provides the basis for actionable steps, like a decision to refinance, consolidate, or pay down debts. Better yet, the actionable insights that both alerts and explains the why behind a change can also generate helpful advice for the question of what now. If you’re using too much credit, it might suggest budgeting. If your credit history is sparse, it will recommend you open a new line of credit. The reason that’s so valuable in this scenario – and even more valuable in the context of a large enterprise – is that the why is often genuinely difficult to determine. If it weren’t, most businesses would spend a lot more time discussing it.
Pay attention during your next meeting, and there’s a good chance you’ll hear a whole lot of what being discussed instead. Because why is so much trickier, most organizations tend to only ask it when there’s a hiccup or as a follow-up to the hours of analysis needed to answer what changed.
This is a big problem. It can take hours, days, or weeks (time that companies don’t have) and a massive collaborative effort just for an analyst to come back to the table with a slide deck that maybe answers the question of why. In all likelihood, the window of opportunity to act on the insights hidden in the data passed during that time.
When you can’t answer both what changed and why, there are a number of implications for your business. The first is financial. Nothing is free, or so the saying goes, and that certainly applies to the process of defining metrics, outlining possible actions, and putting other operations on hold just to answer a single why. Not to mention, curating all those dashboards isn’t cheap. When the data ends up underutilized, it represents a poor return on investment.
On top of that, there’s the decision/time cost — again, did you miss your opportunity window while stepping out to recruit a data analyst and perform a cross-disciplinary analysis? And given the pressure to answer why as quickly as possible, what are the chances that the decision is based on biased interpretations of the data?
And, there’s more — all that work you just did doesn’t translate to value-add. Just because you figured out today’s why doesn’t mean you can use that answer next week, because the data itself will change.
Finally, failing to answer why has a negative impact on the big-picture company culture that you must consider. With all the above happening, it’s hard to blame executives or team members who resort to old-school, gut instinct decision-making. The more that proliferates, the higher the costs, as scenarios repeatedly unfold in which data could have better informed difficult decisions but the gut-instinct decision was the only route available when the decision needed to be made.
It’s not that dashboards are incapable of offering insight into the important whys of a business. A good dashboard is capable of highlighting any number of pattern matches, after all — but as many organizations have learned the hard way, correlation does not equal causation. Things aren’t always as they seem, and staring at a static dashboard week by week can obscure the valuable insights within your data.
Then, how do you scale the level of BI capabilities needed to find answers in your data in a way that communicates those answers? The first thing is to not overlook the need for data itself. If all analysts are using aggregated, simplified data for analysis they’re going to have an impossible time answering any important why questions, no matter how advanced your stack is. Before you can run you have to crawl, and in this case, crawling means compiling data that is granular enough to actually provide interesting insights.
Next, you need the right tools capable of making sense of that granular data. These tools don’t look like more analysts or more dashboards, but improved ways of interfacing with the data you already have. To get the most out of your data, you have to move past descriptive BI tools and reactive slicing and dicing. You need to adopt tools that transform the analytics workflow and automatically perform analysis of key drivers, giving team members – regardless of data know-how – the ability to understand why at a glance.
The benefits of this approach (which you might hear referred to as “augmented analytics”) extend to virtually all parts of a business. Analysts are freed up from mundane tasks, decision-makers and strategists are empowered with the best their data has to offer, and leadership can authentically claim the mantle of being data-driven. Early adopters of augmented analytics already see those results, and are positioning themselves as tomorrow’s market leaders.