1. Applying an End-to-end Perspective
Successful data governance needs to be implemented from end to end, meaning that it encapsulates your entire data landscape from data warehouse to analytics solution. It’s like any process: If it’s not governed all the way, then you cannot control the end result. On the whole, data governance is about making sure that the KPIs on which you are basing your business decisions are correct – having a process in place that ensures that secure data is delivered to end-users.
2. Including the Analytics Solution in Your Data Governance Framework
However, this end-to-end perspective is often overlooked, and it’s quite uncommon that analytics solutions are included in the data governance framework. Companies are generally pretty good at data governance from the data warehouse side, because they believe that, after the data leaves their data warehouse, nothing will alter that data. In reality, this is not the case, largely because of modern analytics tools that enable users to modify data directly inside the tools.
Basically, even if you have world-class data governance for your data warehouse, it doesn’t matter. That’s why it’s important to have end-to-end data governance – you need to include your analytic and visualization tools in your governance framework as well. In fact, some analysts are now explicitly saying “Data & Analytics Governance” instead of “Data Governance.”
3. Leveraging Automation
If you’re relying on people to perform manual processes in order to achieve a properly governed data landscape, you will never have 100% coverage. Data governance processes need to be automated. If you manage to achieve 90% effective governance, that’s good, but you still have that 10% uncertainty looming over all your decisions. And, if you can’t trust the data, nothing else really matters.
Additionally, because the world is changing so fast, the only way for BI tools to keep up is through leveraging automation.
4. Thinking Big, Starting Small, Scaling Fast
It is crucial to approach data governance step-by-step. It’s important to have a “think big, start small and scale fast” practical approach to data governance and the power of approaching it from an outside-in perspective, especially if you use self-service analytics.
Basically, this means starting your data governance efforts with an overview of your entire data landscape, identifying which inconsistencies, objectives and errors are most important, and building your efforts from there.
All in all, this needs to be aligned with the overarching objectives you have as an organization. Are you trying to:
- Make it easier for self-service analytics?
- Consolidate definitions for your KPIs?
- Enable end-users to easily find reports or KPIs they need?
- Solve a compliance issue that requires correct documentation?
There are so many different objectives that you can take into consideration, and these are just some examples. The most important thing is that you initially focus your governance efforts on your main business objective. Other issues, gaps and targets will follow.
5. Testing Your Solution to Ensure Continued Alignment to the Governance Strategy
Despite the fact that the entire development environment considers it a default practice to test their code and its results, analytics tools have yet to adopt this. It’s not common to test analytics data. This is probably rooted in the fact that analytics is driven from the business-side — a sector that is not used to governing or testing their data processes.
We encourage analytics users to test their entire solution so that they know that all their data is correct and is aligned with their overall governance framework. Every now and then, technical issues might arise, and it is crucial to be able to act on these proactively. Such issues are hard to spot manually but very easy to test automatically with baseline testing.