Data Ownership
The combination of these two words strikes fear into the hearts of business domain executives/managers. Unfortunately for them, it’s a reality that’s becoming more and more prevalent in the data space, but fortunately for data people, this is a very good thing.
Let me explain…
A data owner is a person responsible and accountable for the data they produce in their business, or what we like to call their data domain. If you go back and read that sentence again, you will quickly see why we immediately get an allergic reaction to the words data ownership. In most cases we are talking to business people, not the tech folk.
We typically ask two questions when we start a new project:
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- Is the data available?
- Do you know your current data quality score?
For most, the first question is easy. A simple yes or no, but for the second one, it’s usually a cold, awkward stare back.
1. It’s priceless
But we are all friends here, no need to make people uncomfortable. We expect data owners to know, but unfortunately the world does not work that way. Data, for as long as I can remember, has always been treated as an afterthought, an “Oh yes, we need that!” It’s not their fault, to be honest, it’s become ours as data professionals. We have come late to the game and have much to prove, but that’s what we want, right? An opportunity to prove the true value of data and what it can do when in the right hands, at the right time with the right information.
2. Data is life, well at least I think so
We need to organize the environment around them, to empower and support them in their data ownership journey. More specifically, with people, process, technology, and data within their organization.
People
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- Surrounding business with the right tech/IT/CIO partners to help identify and fix data related issues
Process
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- Embedding data ownership and accountability within the business areas, so it’s clear who we need to reach out to first
Technology
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- Creating a platform that’s easy to use with a strong focus on improving the visibility of data quality
Data
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- Cultivating a culture of data quality improvement across all functions
If we expect them to know their data quality scores, we need to make very sure that they are enabled to do so.
3. Sound complicated?
You guessed right, it very much is. So how do we change their minds? From my experience, the fear of data ownership usually boils down to a lack of two things:
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- understanding and
- support.
Given that data can escalate quickly into a deeply complex matter, especially when we start talking about tech, then eye contact during those discussions seems to fade even faster. Keep things simple, including business terminology, and focus on the value that good quality data can bring for their business. Like the proverbial “the key here is trying to find the balance,” try “garbage in, garbage out.” This always seems to work when you want to land the message of having good quality data.
4. Not enough?
Try collaboration and partnerships with the tech teams. They take care of the systems that produce, store, and manage the data. They will know where to look if that boogeyman pops up. Tech runs the systems, business produces the data. This is where the chemistry lies, a fusion of business and tech focused on improving the oil that runs the machines – data. We have seen a direct correlation between improvement in data quality and collaboration of business and tech.
So, what have you got to lose? Well, lots if you don’t sort out your data quality! If I have to summarize a way to run data management programs, I always refer back to an African proverb: “If you want to go quickly, go alone. If you want to go far, go together.”
In my next blog, I will explore different data management operating models, specifically which ones we had set up and which ones work and the cold hard truth of what not to do.
Till the next one…
