What do Snow White’s Grumpy, Oscar the Grouch, and Gritty, the mascot of the NHL’s Philadelphia Flyers, have in common?
They represent the three G’s of data governance. (My Irish grandmother always said any good story starts with a joke, even a bad one. So I guess I have that one covered.)
Here’s what I mean: Nearly two decades ago, a client asked me why you needed good data. Somewhere in the outer reaches of my brain I spit out those three words — grumpiness, grouchiness, and grittiness — and there it stuck. (Giddiness I added later.)
Change is hard. People resist it. Even those who say they want good data may squirm and fight when you start making changes to deliver it.
So, What’s the Good News?
The good news? I’ve led that change before, and I’m here to tell you, there’s a light at the end of the tunnel. When you launch a governance initiative, prepare to witness the 4G’s of data governance:
That’s right, data just got emotional. But in both grieving and governance, there are emotional processes at play, which are universal and predictable.
Your path to good, trusted data, requires governance… which is a tough pill for many to swallow. You may feel grumpy. The people whose habits you’re working to change may feel grouchy. But if you dig in, and approach your change with grittiness and courage… good-data nirvana awaits!
In this article, I will discuss how good data governance teams can get beyond the grumpiness & grouchiness surrounding data by demonstrating grittiness to achieve giddiness. Let’s dive in.
Stage 1: Grumpiness
“Data is nothing but trouble” could be a rallying cry of nearly any business user who doesn’t trust their data. How many times have we heard:
All these comments come from real folks who are “grumpy” about the data and want to make it better so they can do their jobs. Without care and feeding of the data, trust erodes and use of the data becomes impossible.
We need to do things to make data better. Some of that is done automatically with tools, AI/ML, or just better processes, but much of it requires manual work somewhere by someone — and sometime soon!
Stage 2: Grouchiness
Now that we agree the data is bad (and needs to be fixed), there are seven dwarves — I mean seven things — we need to do with it:
I can already hear the grouchy replies:
Folks get grouchy when they have to do these basic tasks. Some of this goes with the territory; that is, some people balk at what they should do now — such as having good business definitions — to save time later. Other grouchiness is tied to not having the right processes in place, or existing processes badly in need of fixing.
So expect some grouchy people, especially those with the data who are always looking to improve their processes.
Stage 3: Grittiness
We’ve agreed that the data is bad, and we’ve agreed to not only clean it up in the short term, but also implement improvements to stop it from happening in the future. It’s now time to define OKRs or KPIs, establish processes, define and populate business definitions, and name an owner of the data and the process.
This requires grittiness. We need to dig deep, roll up our sleeves, and do the work, and get it done. Yes, I know we want to see it done automatically, but often it’s the hands-on work that’s required to make data right.
Working together with grit will give us clean data, solid and verified business definitions, robust business processes, and a clear understanding of what data could and should be used for.
The bottom line is…if we have grit to face the problem — and conquer it — all will benefit.
Stage 4: Giddiness
If we have the data right, we’ll be so gosh-darn happy about what we have that folks will jump for joy, trust will grow in the data, and everyone can stand around a bonfire and sing the camp songs of our youth. (All right … I can dream.)
In reality, once we get beyond being grumpy about bad data, grouchy because we have to do work to make the data right, and demonstrate the grit to get it right…we become giddy with all the great things we can do with all this great data.
Giddy up! We’re on our way. Until next time, happy data!