Nowadays, data professionals are capable of building incredibly powerful systems. Near real-time data warehouses that integrate data from multiple data sources from all over the organization. Even external data like data from partner organizations, open data, data from third party data providers, … can be seamlessly combined into one integrated enterprise data warehouse. This allows unprecedented insights into customer journeys and customer behaviour and enables -to name one- 360 degree views on your customer. The value of these data warehouses is huge, right?
Potentially yes. Although reality is too often very different. I have once witnessed an entire data warehouse that was ultimately only used to produce some static pdf reports. Once per month they were generated and sent to customers. No interactive analyses, no intelligence to understand customer behaviour, just plain and simple reports merely to comply with service level agreements.
So why is that? Why is it so hard to use this untapped potential of already integrated data? Why do companies invest so much in enterprise data warehousing and fail to reap its benefits?
There’s one key ingredient that’s often missing and definitely underestimated, and that’s culture. In order to move to a “data driven” culture and really integrate data into everyday’s processes, people need to know what data is available, how to use it and understand it’s value.
That’s where data governance can be a true game changer. When embarking on a data governance track, the business people are getting more and more involved in data. They get the hang of it. The data owner and data stewards start to become ambassadors of data within their domain. And that’s exactly what’s needed to make data programs in general more successful. Business people acting as ambassadors within the organizations and explaining the value of data in their own words. That’s when the magic happens. The key is not to rush things, and to progress slowly and steadily. Culture doesn’t change overnight.
So what does a successful data governance track look like?
It’s crucial to define a high level semantic data model of the organization. Important here is to define it in the language of the business, and to avoid introducing levels of abstraction that business does not relate to. The model should read as a story. The next step is to assign each entity to an information domain. Per information domain we assign a data owner and a data steward. The exact roles and responsibilities of a data owner and data steward will be tailored to your organization, but roughly the data owner is higher in the organization and will for example approve the definition of a business term that’s proposed by a data steward.
Once that’s established, all data stewards gather typically every two weeks for two hours. There’s a lot of value in these meetings and very interesting discussions take place when definitions need to be defined for business terms. The broader a business term is used in an organization, the more lively discussions will get. And ultimately everyone will have a better understanding of the business in other domains. So what happens if two departments have important differences in the definition of the same business term? Well, that’s a good indicator you probably need two different business terms. The mere fact you’re now aware of the differences in understanding is valuable in itself.
Below is an example of a data governance track, where we gradually increase the data maturity of the organization.
Obviously, the above needs to be part of an overall enterprise data strategy.
Oh, and finally, while you evolve in getting definitions straight, be prepared to rework the namings that have been used so far in your reports and dashboards across the organization. There will be probably be some misalignment 🙂 On the bright side, numbers will now be interpreted correctly.
Don’t hestitate to contact me on firstname.lastname@example.org to find out how we can make your organization more data driven.