From Business Strategy to Data Governance
#20 Or how to turn vision into action
Hello 😀
Let’s come back to basics. I always say that Data Governance must be linked to the corporate strategy. But how do we do it?
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Agenda
Translate business goals
Map & prioritize
Be Data ready
Translate business goals
Every strategic decision your organization makes depends on accurate, accessible, and reliable data. But without the right governance in place, even the best business strategies can fall short.
Data Governance supports business goals. Repeat this sentence. Several times.
Start by identifying your organization’s core objectives.
It could be :
A specific X € target for revenues
An increase of customer satisfaction
And from a data standpoint…
To increase the revenues, the Sales team have asked you to improve the customer experience and provide :
Customer preferences to personalize offers and services (e.g., targeted promotions, customized journeys).
Promotions allocation optimization during the year according to preferences
On the satisfaction, they are asking for :
Real-time data insights to adapt to customer needs during their experience (e.g., live recommendations, notifications).
A seamless customer journey by integrating data across all touchpoints (online, mobile app, services, etc).
👉 This is the first translation level of the business goals.
Map & prioritize
Trace the flow of data necessary to provide what the Sales team asked.
This mapping exercise identifies the critical data points that directly impact the company’s objectives.
Prioritize data that drives value
Recently someone asked me on my online training :
“Should business teams flag themselves critical data? If so, isn't there a risk that they will flag everything as critical data?”
🤯 Good point, not all data is equally important. You need to focus your governance efforts on high-value data elements.
Coming back to our example, key data elements to reach objectives are :
Customer data : personal information (name, age, contact details), history, loyalty program
Journey data : preferences (favorite products), ticketing and reservation details
Retail data : point-of-sale data, promotions, inventory
We could imagine a list of questions and a scale to determine which data elements are critical :
For each data element :
Rate against all criteria, assigning a score from 1 to 5 for each
Calculate the total score by summing up the scores of each criteria
Define criticality thresholds based on the total score :
1–6 : Low criticality
7–12 : Moderate criticality
13–15 : Critical
👉 This is the second translation level of the business goals.
Be “Data ready”
Great, so now you can define what you need to deliver these critical data elements.
You need 3 foundations :
1️⃣ PEOPLE : Data Culture and Literacy
Data is only as valuable as the people who use it. You have to create “data consciousness”, meaning building awareness of how data impacts decisions, the importance of accuracy, and the risks of poor-quality data.
2️⃣ PROCESS : Data Governance
Without clear processes, data can quickly become chaotic. This leads to compliance risks, accessibility challenges, and poor data quality. Data governance provides the structure needed to ensure data is reliable, secure, and usable. Once these data elements are defined, you can group them into data domains and define governance policies to ensure their accuracy, security, and accessibility.
3️⃣ TECHNOLOGY : Modern Data Infrastructure
Technology is necessary to store, process, and analyze data at scale. A modern infrastructure ensures scalability, supports advanced analytics, and provides the flexibility needed to adapt to changing business needs.
These 3 strategic pillars are the prerequisites for leveraging data. That’s how you show the necessity of your pillar Data Governance 😀
👉 Once ready, you can think about doing use cases using analytics or even AI (e.g. recommendations, targeted promotions - all stuff requested by the Sales team).
Your ultimate quest
On paper, this looks logical : you translate business goals into actions on data. It’s easy to understand. Or at least for us, data people…
I feel like I’ve never seen a company where this line of reasoning is acknowledged by all on the first time. There are always those who are reluctant. They’ll look at you with a defiant look.
The list of their arguments usually goes like this :
🤫 Things are working pretty well now, we’re reaching our objectives.
🤔 What for? We’ll manage problems when they happen.
💸 You need to show me ROI of your data governance before you start.
People don’t need to understand your data things, you’ll do it for them.
This is especially true for large companies, who are making profit since many years without changing too much. Or for businesses that are not based on data.
What you could answer to these arguments :
1️⃣ I like the idea that it’s like a state : without governance, people can do whatever they want - including stupid stuff like sharing passwords with whoever, sending datasets outside the company, etc… Usually this kind of argument works.
2️⃣ Or you can take it back to the ground : ask 3 different people how they calculate the revenue or other KPI - find out that there are at least 3 different answers. That’s not so good for the Top management dashboards which are probably not very accurate.
Make it unavoidable
If Top management think they’ll get ROI in the short term → it’s a problem. Make the Top management understand that it’s an investment. It’s true : it will not be free.
You need to convince that it’s a long term change. Nothing happens over night.
👉 But in a few years, companies who will have taken care of their data assets will have a huge competitive advantage.
My advice is : be patient.
Talk to different people and repeat your story about the foundations required to reach business goals.
With this story, you’re linking Data Governance to business objectives but also to other key data foundations !
At some point they’ll come to you to share it like it was their idea : that’s when you’ll know you’ve won the battle !
See you soon,
Charlotte
I'm Charlotte Ledoux, freelance in Data & AI Governance.
You can follow me on Linkedin !
Thank you, great post :)
Very clearly articulated . thank you Charlotte.