Getting off the data treadmill

Companies too often think that getting value from data is all about the latest buzzwords: big data, machine learning, real-time. Turns out it's much simpler than that

man on treadmill

Most companies start their data journey the same way: with Excel. People who are deeply familiar with the business start collecting some basic data, slicing and dicing it, and trying to get a handle on what's happening.

The next place they go, especially now, with the advent of SaaS tools that aid in everything from resource planning to sales tracking to email marketing, is into the analytic tools that come packaged with their SaaS tools.

These tools provide basic analytic functions, and can give a window into what's happening in at least one slice of the business. But drawing connections between those slices (joining finance data with marketing data, or sales with customer service) is where the real value lies. And that's exactly where these department-specific tools fall down.

So when you talk to people in that second phase, understandably, they're looking forward to the day when all of their data automatically flows into one place.. No more manual, laborious hours spent combining data. Just one place to look and see exactly what's happening in the business.

Except...

Once you give people a taste of the data and they can see what's happening, naturally, their very next question is, "Well, why did that happen?"

How things usually work

And that's where things break down. For most of the history of business intelligence, the way you answered "why" questions was to extract the relevant data from that beautiful centralized tool and send it off to an analyst. They would load the data back into a workbook, start from scratch on a new report, and you'd wait.

By the time you got your answer, it was usually too late to use that knowledge in making your decision.

The whole thing is kind of silly, though -- you'd successfully gotten rid of a manual, laborious process and replaced it with one that is, well, manual and laborious. You thought you were moving forward, but it turns out you were just on a treadmill.

To sketch it out, here's what that looks like:

img1 Daniel Mintz

Another path

Recently though, more and more businesses are realizing that there's another way: With the right tools, you can put the means to answer why questions in the hands of the people who can (and will) take action based on those answers.

In the old world, you'd find out in February that January leads were down, and wait until March for the analysis that reveals that -- d'oh! -- the webform wasn't working on mobile. In the new world, you can get an automated alert about the drop-off in the first week of the year. You can drill into the relevant data immediately by device type, realize that the drop-off only affects mobile, surface the bug, and get it fixed that afternoon.

That's the real value that most businesses aren't realizing from their data. It's much less about incorporating the latest machine learning algorithm that delivers a 3% improvement in behavioral prediction, and more about the seemingly simple task of putting the right information in front of the right person at the right time.

The task isn't simple (especially considering the mountains of data most companies are sitting on). But the good news is that it is achievable and it doesn't take a room full of Ph.D's or millions of dollars in specialized software.

What it does take is focus, and a commitment to being data-driven.

Luckily, it's worth it. The payoff of facilitating this kind of exploration is enormous. It can be the difference between making the right decision and the wrong one -- hundreds of times a month -- all across your company.

img2 Daniel Mintz

So if you find yourself stuck on the treadmill, try stepping off. I think you'll like where the path takes you.

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