How to identify Data Owners, where multiple areas of the organisation use the same data?
Nicola Askham
DataIQ 100 2022 | Award Winning Data Governance Training | Consultant | Coaching | Data Governance Expert | D.A.T.A Founding Committee
Identifying Data Owners
It is something I come across an awful lot when helping my clients do data governance.
and I'm sure you will too, because unless the data is only used by one area, it's often not clear-cut who the data owners should be.
If you have read any of my other articles about data ownership you will know that In my experience, it really is important that you only have one data owner per data set
I believe very strongly that you should have just one data owner per data set and, yes, it can be challenging if you have multiple people using the same data and even more challenging when they all want to own the data but there are a number of different ways of dealing with this.
My preferred way is to see if I can break down the data and identify different chunks of it that can be split across multiple data owners. For example, for one organisation, which was an insurer, we had a big debate over who owned the customer data and the head of underwriting believed quite strongly that they owned it, but the head of marketing also believed that they owned customer data and when we sat and talked to them we actually agreed that they owned different subsets of it.
We broke it down and we had customer risk details owned by the underwriting area
and customer contact details were owned by the marketing team and that worked very well… for a few months.
They got on and did everything we asked them to do as part of doing data governance
until we got to the day when somebody reported a data quality issue
and my heart sank because I had this horrible feeling that I knew what was going to happen.
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I was right, when we asked them both who owned the postcode, they both said they did, and they both believed it was them.
Interestingly, because we had split the data out, they’d both been doing the role and got their head around it for a few months. We got them back together and discussed it
and at that point, the head of marketing said “You know what, I actually don't think I am the data owner. I think I'm a key consumer of the data
The moral of that story is… splitting it sometimes works, and sometimes it doesn't.
Simply, you have to be flexible and understand that you may need to change it again
further down the line, but it doesn't stop you from trying.
The other way of doing it if splitting the data into subsets it isn't an option, is to look which area or team really dictates the standards by which that data is captured. Do you have somebody that is setting the rules and saying this is how we do this? Because in which case they should be the data owner.
If you've got a number of other teams elsewhere in your organisation that are allowed to set the rules around that data (and that is a very rare circumstance) I sometimes come up with a two-level data owner model, but I prefer to use that as an absolute last resort because adding any complexity to your data governance framework
Don't forget if you have any questions you’d like covered in future videos or articles please email me - [email protected]
Originally published on?www.nicolaaskham.com
Data Management Professional
1 年Hi Nicola, I thoroughly enjoyed reading your article! Currently, I'm in the process of establishing a data governance framework for my organization, and we've encountered a challenging question regarding data ownership. Specifically, we're uncertain about who should be designated as the data owner for blended data. For example, we have designated owners for customer data, employee data, financial data, operational data, and system data. But when it comes to a record that combines elements such as employee names, customer names, and financial transaction data, who should assume ownership of this blended record?
Chief Innovation Officer at Sprift Technologies
1 年Nicola Askham thought provoking as ever. I like to look at it from process and IDEF0 context diagram perspectives. Data is not a standalone product - it is obtained, managed and discarded as the result of activities in your organisation. If the data is an input to your process from elsewhere in the organisation then you are unlikely to be the data owner. If you are generating and managing the data to provide it as an output from your process then you likely are the data owner. In your example, marketing may be the data owner for “prospects” but once they succeed and the prospect becomes a “customer” then the data is usually enriched (with payment methods and orders at least) and becomes owned by the Customer Services team. As for whether someone is a data owner or data steward, depends on organisation - title is less important than someone taking accountability for ensuring the data meets appropriate quality requirements. And that’s a whole other topic!
Data Governance and MDM Expert | AI Entrepreneur | Engineer Bringing Value Through Governed Data??
1 年What do you mean by "owner". Ownership implies possession which does not require stewardship. Ownership in terms of usage, such as analytics, may have different governance requirements.
Data Management Architect
1 年Nikola, it's good to see posts from you bringing up the day to day challenges in governance.I would like to understand, could we not go with a ground rule for the data creators to hold the data ownership rights. However there could be multiple users of the data who can place demands and expectations from the data arising out of their usage ( as it's said that it may not be known at the time of data creation how that data could get used).?Also, the split of the data as achievable in the data model ( where my consideration is that different teams are actually creating data for the same entity in different contexts), I am assuming this segregation will be enabled more likely in the downstream data warehouse models.
Lead Data Governance and Metadata Management|2x AWS Certified | Informatica Data Governance Tools(Axon, EDC, DQ, DPM)|BigID, Collibra|AWS, Big Data Hadoop,Spark
1 年Thanks for sharing.This is common problem across Business sector