Blueprint for a Scalable Data Governance Team
#21 Or how to fast track towards high impact
Hello 😀
Once you’ll start, you’ll quickly realize that the scope is huge, there is a lot to do… To succeed, you’ll need a team. A scalable team.
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Agenda
The hard truth
Being pragmatic
A concrete example
The hard truth
Companies who start their data governance program NEVER do it with a million dollar invested right away.
Here’s what happens usually for the new Head of Data Governance :
What you think you’ll have : 5 full-time hires
What you usually get : 1-2 consultants for a few weeks
Why is this happening?
Because you need to prove value first. And you’ll have to do it with low resources. It may sound unfair but would you buy a house without knowing its market value?
I’ve seen mostly 2 cases when it comes to building a Data Governance team :
Case #1 - There is no Head of Data Governance
The Chief Data Officer will hire a consulting firm to get a first audit and recommendations. The first hiring will be a Data Governance Manager. This Data Governance Manager will keep consultants from the audit.
😒 Downside is that your team is not permanent, there is a high risk to lose knowledge.
Case #2 - There is a Head of Data Governance
The Head of Data Governance will defend the budget and start to hire asap according to the available amount :
A Data Governance manager,
Data Domain managers for each domain,
Sometimes Data stewards etc.
😒 From my experience it is really hard to hire these profiles.
For several reasons :
New field, not a lot of candidates
Hard to combine both data and business experience
Packages can be less attractive than other data jobs
And also for some roles like Data stewards, they already exist in your organization, you don’t need to hire them.
Being pragmatic
Whatever the path, here’s what you should target for a first core team :
A Head of Data Governance : can be the Chief Data Officer
A Data Governance Manager : the 5 legged sheep that I talked about
A Data Quality analyst : a super useful profile, that you need early to prove value. This analyst will showcase the bad data quality impact and take measures to improve it, implement alerts, etc. Skills are similar to a data analyst :
Of course, your team will partner with :
Data Engineers to implement policies & standards
Data Architects to ensure data models and flows align with governance policies
👉 With one rule only : reuse existing profiles - hire only when necessary !
A concrete example
Let’s talk about one of my client, we’ll call him Barnaby.
If like him you want my advice, you can book me here.
Barnaby is the head of IT. He is starting his data governance program from scratch, nobody really knows what it is. However, business teams have a pretty good data sensitivity : they need good data to calculate metrics daily, some of them even have started a small dictionary of indicators.
Here’s what I proposed in 3 steps :
1️⃣ Centralized Model (current stage)
🔹 Characteristics :
The single central data team within IT is made up of a Data Governance manager (myself at the moment), a Data project manager, Data Engineers and BI experts.
Data ownership is remaining primarily with IT, limiting business engagement.
🔹 What we’ll do :
Provide a core data governance framework and identify business owners and data stewards. We’ll do sessions with them to start engaging initiatives and raising autonomy (starting with the most mature ones who did the small dictionary).
2️⃣ Hybrid Model (transition phase)
🔹 Characteristics :
Shared responsibility between the central data team and domain-specific Business Owners and Data Stewards in business functions.
A Data Governance Board ensures alignment between governance policies and operational needs.
🔹 What we’ll do : Launch and animate the data governance board with all Business Owners. Provide tools like a data catalog and train Data Stewards. Coordinate local initiatives in business functions.
3️⃣ Federated Model (future state)
🔹 Characteristics :
Business domains have dedicated Business Owners and Data Stewards who own and manage their data within the governance framework.
The central team acts only as a facilitator, setting common standards and providing occasional support.
🔹 What we’ll do :
Provide content to scale the trainings on all business functions. Animate the data governance community. Act as a bridge between domains to ensure consistency in data definitions, metadata, and interoperability.
See you soon,
Charlotte
I'm Charlotte Ledoux, freelance in Data & AI Governance.
You can follow me on Linkedin !