Data governance is often presented as an arcane and complex topic, but at its heart, it’s quite simple. The purpose of data governance is to get data in the condition needed to support valuable action. This definition is important for what it says, but also for what it doesn’t say. It doesn’t say that the purpose of data governance is to create policies and procedures, establish “good” quality data, or implement data management practices. Those things are important, but only to the extent that they help data governance do what it needs to do – support valuable action.
Common pitfalls of Data Governance implementations
The first model positions data governance simply as a mechanism to provide free advice to various people and efforts across the organization when distributing data. The result is what many people in these organizations derisively call the “Wild West”. Sometimes starting with the name ‘data democratization’ or a similar well-meaning idea, every data and application development scientist the team is left alone to prepare data, manage quality, connect disparate data domains, secure data, and perform all kinds of data management tasks that take time and attention away from production of real value from analytics and applications.
These distributed efforts duplicate work by collecting data from the same sources over and over again, using different processes for different requirements, resulting in a bewildering and vulnerable situation. data landscape. This results in excessive costs, slow projects, and inconsistent and unreliable data.
Accelerating Self-Service with Data Governance
To aid each shared statistics deployment and dispensed innovation, it’s far regularly assumed that highly coordinated and proactive agency statistics governance have to be balanced with self-carrier statistics provisioning and analytics. But this isn’t the case. A carefully deliberate and applied statistics governance program hastens self-carrier with the aid of using supplying dependable center statistics, hence permitting analysts and alertness groups to focus their efforts on particular paintings wished for his or her use cases. This information will become more and more more vital as self-carrier or “statistics democratization” is similarly enabled with the aid of using friction-lowering cloud technology.
Data Governance Within a Framework for Enterprise Data Management
Data governance is best understood within two contexts:
- Drivers that motivate action and determine scope
- Data management activities that contribute value to those drivers
In this ebook, we’ll describe how to do that.
We’ll discuss first common pitfalls and the best way to avoid them. Then we’ll present a framework for enterprise data management that includes the drivers for data governance and the data management capabilities that data governance uses to support those drivers. Finally, we’ll present a simple but effective structure for a comprehensive data and analytics roadmap, and we’ll explain how to position data governance activity within the roadmap for maximum effect.