If I got $1 for every time I heard “we need to be more data-led,” I would have enough to retire. Today most organizations acknowledge that data is anywhere from somewhat important to absolutely critical. Without a tailored Data Management capability, most of these organizations might be looking into the dark.
Question is, will they believe what they see or are they too scared to look?
Well, what exactly is Data Management? It’s a broad topic, but I’ll try to simplify it. In essence it’s about instilling, managing, and measuring the right level of capability, engagement, and execution to ultimately improve Data Quality. Remember that old data classic saying, “Garbage in, garbage out?”
Data quality is key. Let me explain…
Without a good understanding of the data quality in the organization, it’s like trying to prove that the Loch Ness Monster exists. Poor quality photos, bad recordings, unvalidated interviews make the story lukewarm at best.
The more organizations obsess with data quality, the better. So you get the point, but what about the Boogeyman? Well, this is where things get interesting…
Data Management has a split personality.
On the Loch Ness side, if we don’t know what quality of data we’re dealing with, our insights and decisions will probably yield wild assumptions. On the Boogeyman side, if we ignore data quality, do we really know what’s under the bed or in the dark corners of the organization? If we keep reusing poor quality data, we feed and morph this unknown creature into something we won’t be able to find or contain, and the scary thing is that we will never know when it will strike. Imagine trying to mitigate organizational risk without knowing all the facts.
Did I hit a nerve yet?
Fortunately, there are a plethora of professional businesses focused on helping organizations trying to validate if the Loch Ness Monster exists or capture the Boogeyman. This is great news, but there is a catch…
Organizations are unique in their:
- Data maturity
As a core principle, we need to acknowledge the chemistry of the organization with data to brew up a fluid approach to executing Data Management. There will be elements that don’t mix well — some could even blow up — but with resilience, the team must just keep trying to find the right bonding agent.
Photo by Steve Halama on Unsplash
I am lucky enough to have experienced and transitioned through three phases of Data Management execution models in a large organization. This gave me the advantage of the ability to learn and adapt, because if I didn’t, then shame on me!
In my next blog, I will go into more detail about the three phases, their characteristics, and things to look out for. I’m sure everyone reading this blog will be able to relate and hopefully learn something new.