In Part 1 of this series, we defined data governance as a framework – something an organization can implement in small pieces. Data management encompasses the disciplines included in the data governance framework. They include the following:
- Data quality and data profiling.
- Metadata (business, technical and operational).
- Data security.
- Data movement within the enterprise.
- Data movement/usage outside of the enterprise.
- Data stewardship or data ownership.
- Execution of architectures (including data warehousing and big data).
- Execution of policies and practices set forth in the data governance framework.
I'm sure there are a few more you could add, but this has become quite a large list.
Besides defining the data governance framework for your organization, and deciding on the first (or second) initiative, we need to define what data management disciplines will be required to further our success.
Data security is a favorite of mine. Some organizations protect data going outside of the enterprise to the highest degree, but they don't put much focus on where the data is going within the enterprise. Data redundancy happens – especially now, in our cloud and big data environments. Some organizations feel like any data should be able to reside in our big data environment. But I believe that managing and governing usage of that data is of prime importance. We need ways to monitor and report usage of that data so that we can maintain a level of compliance.
So – who does this, and who cares? There have to be resources (people and tools) to manage and monitor our ever-growing data empires. That said, knowing how the data was loaded into the big data environment – as well as who is using it – is required for most companies. We may need to consider limiting WHAT data can be loaded. All of this requires a data governance framework with working data management disciplines.
OK, I'm off my soapbox.