Published On: October 24th, 2020Categories: Guides

5 essential elements of data governance.

What is data governance? Well, for one, it is a buzzword. And, with buzzwords, we often forget to slow down and investigate what they actually entail. This article is dedicated to exploring 5 essential elements of data governance – emphasizing the importance of implementing it from end to end.

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 business intelligence 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 BI solution in your data governance framework

However, this end-to-end perspective is often overlooked and it’s quite uncommon that BI solutions are included in the data governance framework. Companies are generally pretty good at data governance for 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 BI tools like Qlik, Power BI and Tableau 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 we are emphasizing the importance of end-to-end data governance – you need to include your visualization tools in your governance framework as well. And the fact that we do that with NodeGraph is quite unique. Find out more about implementing a data governance framework as part of your BI success.

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. That’s the key differentiator between any other solution and NodeGraph — the fact that everything in NodeGraph is automated. Explore the NodeGraph solution.

4. Thinking big, starting small, scaling fast

It is crucial to approach data governance step-by-step. We believe in a “think big, start small and scale fast” practical approach to data governance and the power of approaching it from an outside-in perspective, starting from the business perspective, ensuring data quality and data trust when it comes to your BI solution, especially if you use self-service BI.

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 BI?
  • Consolidate definitions for your KPIs?
  • Enable end-users to find easily 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. The key is not to have too many, not to get too ambitious.

By using NodeGraph, you can extend the reach of your data governance framework to include your business intelligence environment. We encourage customers to see NodeGraph as a toolbox that provides all the metadata you need, in an automated way and helps you see and analyze all the building blocks of your BI solution so you can drive the most business value from business intelligence.

NodeGraph Dependency Explorer showing full data lineage from end to end. Serves as a great starting point for developing a data governance framework.

5. Testing your solution to ensure continued alignment to the governance strategy

Despite the fact that the entire development environment considers it default to test their code and its results, BI tools have yet to adopt this. It’s not common practice to test BI data. This is probably rooted in the fact that BI is driven from the business-side — a sector that is not used to governing or testing their data processes.

We encourage BI 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 that NodeGraph’s Data Quality Manager provides.

NodeGraph data quality manager testing qlik

NodeGraph Data Quality Manager performs automated baseline testing to proactively ensure that your data solution is aligned with your governance efforts.

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