Wherever we go, we are overwhelmed by MORE: more sales, more discounts, more fun, more excitement, more features – the list goes on and on!
What humans seem to be far less attuned to is reducing what we don’t need. Drive around any suburban neighborhood and see the many cars parked outside their garages!
Believe me—people in Chicago in the winter don’t prefer to leave their cars outside. Most of the garages are simply filled with other stuff. Probably items that are less important than closet clutter, but might have value…someday.
Data works the same way. We’re always adding new fields to tables or new calculations to our reports. People always ask for more. When’s the last time somebody in Finance called you to ask for a useless number to be removed?
It happens occasionally, but for the most part everything grows and grows until it fits the container, and usually until the container is bursting at the seams! Or another example, how often do your meetings end five minutes early versus how often do they run a little bit long?
You get the point: we are bad at getting rid of what we don’t need.
But in a data context, why is this such a big problem? Our systems can handle more and more, and as long as there is room on the page what harm is there in a little too much?
The reason is that while it seems innocuous, it is inefficient to have more than we need. People do not have unlimited brain capacity and every time we have to dig through more than necessary, we work a little harder than we need to.
Data Curation is all about removing the data that is not needed. Think of a museum gallery, where the exhibit is defined by what we see. There’s often a story underlying the artifacts and we are often moved by what we see. What we don’t see is all of the items that didn’t make the gallery floor. They are in storage in the basement or in the museum director’s office. But someone, somewhere, determined that the story that matters could be told slightly better by leaving some items out.
The goal of Data Curation is to remove anything unessential so that the important stuff shines through.
People are wired to come up with new ideas and pursue them. We like to have brainstorming sessions where we think of anything and everything that might expand our capabilities. But the rub is that we can’t do everything, and we shouldn’t always add more.
If we want to keep all of our junk that we will never need, it will be February and we will have to scrape ice off the car every morning. If we keep all the data, add everything possible to the dashboard, create every requested variation of every report—we will be causing more problems than we are solving?
Especially with things like Data Quality – if we are indecisive about what we are choosing to improve, then we will try to make everything perfect. And how will that work out? We will put too much effort into one area in a failed attempt to create perfection and then other areas will get no attention at all.
Turning things off, leaving things out, removing the unnecessary. All of these require having the courage to say “no.” This is actually key to any of the agile or iterative operating models out there. “Iteration” doesn’t mean keep adding everywhere—it means trying something, learning what doesn’t work, stop doing that part, and start doing something that has a chance of being better.
If we build data analysis tools that people don’t use, then we need to recalibrate the tools, provide more training, or get the right data into them. Data Curation leads us to observing the world around us, identifying the mistakes, and correcting our courses of action. We must have the courage to throw that project plan away and start again if we are heading down the wrong path. Even if that’s the right thing to do, any project manager would shudder at the immediate consequences of redefining projects all the time. It’s not just us as individuals, but our organizations themselves, struggling with this simple concept.
If you want to see it at its maximum, look at what is happening with the cloud. Adding real infrastructure (and associated costs) now happens with a couple of mouse-clicks. The ease by which new capabilities can be added is awesome and amazing! And even though we can turn the services off just as easily, it usually doesn’t happen so well. Many organizations find that even though the incremental cost of cloud technologies is compelling, their governance processes haven’t caught up. This results in wasted resources that can be at least as costly as their legacy infrastructure.
I guess it isn’t all that surprising that we are bad at turning things off, saying “no,” and abandoning bad ideas. They all have an element of conflict beneath the surface. But if we can find a way to overcome the friction, we will be on our way to making the most of the data and the limited resources we have. Think about how you can use Data Curation in your organization to free up resources and create more data value.
And until next time, go make an impact!
