In the simplest terms, data governance establishes policies and procedures around data, while data management enacts those policies and procedures to compile and use that data for decision-making.
To unpack this idea further, it helps to understand what each of these concepts is to better understand how they operate together in practice.
What is data management?
Data management is the creation and implementation of architectures, policies, and procedures that manage the full data lifecycle needs of an organization. Having these policies and procedures in place is critical to analyze complex, big data. When data is treated as an important company asset, it needs to be managed as such.
Data management includes several different types of data projects, one of which is data governance. We’ll quickly review the other common elements of data management before focusing on how data governance and data management work together.
- Data preparation is the process of cleaning and transforming raw data to allow for accurate analysis. This critical first step sometimes gets missed in the rush for reporting and analysis, and organizations find themselves making bad decisions with bad data.
- Data pipelines are used to automatically transfer data from one system to another.
- Data extract, transform, load (ETL) means transforming data to load in an organization’s data warehouse. ETLs often are automated processes once they are built and usually require preparation and pipeline work.
- Data catalogs help create a complete picture of the data by managing metadata and also making data easier to find and track.
- Data warehouses provide a clear route to data analysis by consolidating all data sources.
- Data governance helps define policies and procedures for maintaining data security and compliance.
- Data architecture will be the formal structure for managing data flow.
- Data security consists of the processes put in place to protect your data from unauthorized access or corruption.
What is data governance?
Data governance is a key component of data management—the practice of managing how the data that is being managed is processed through the organization. Data governance helps answer questions like:
- Who has ownership of the data?
- Who can access what data?
- What security measures are in place to protect data and privacy?
- How much of our data is compliant with new regulations?
- Which data sources are approved to use?
We can think about these models into two groups worth governing, content and data. Here, content means the dashboards and analysis and stories that data is used to create. Within content and data, we can then work through areas of each, like content management, content authorization, data source management, and data security.
Governance models and practices won’t be the same across every organization, but these models are crucial pieces of the process.
- Data quality is a pillar of data-source management. If you don’t have quality data, then it doesn’t matter how robust your governance program is. Having data that is accurate, complete, and reliable is a cornerstone of any data-driven organization.
- Data security and compliance is the practice of defining and labeling data sources by their levels of risk and then creating secure access points, keeping a balance between user interaction and security.
- Data stewardship helps monitor how teams use data sources and stewards lead by example to ensure data access, security, and quality.
- Data transparency matters because every piece of the process and all of the procedures that you put in place should work within a model of transparency. Analysts and business users should be able to easily find out where their data comes from and know if there are any special considerations.
The differences between data management and data governance
It is key to understand that governance is part of the overall management of data. Data governance without execution is just documentation. Data governance puts all of the policies and procedures in place, and data management executes all of these pieces to compile and use the data for decision-making. Enterprise data management enables the execution and enforcement of policies and processes that data governance creates.
While there are some similarities between data management and data governance—primarily that they are both important to the organization and structure of how data is used in your organization—the magic is in their differences and how they work together.
To use an analogy, data governance designs and creates the blueprint for new construction on a building, and data management is the act of constructing the building. And while you can construct a building without a blueprint (data governance), it will be less efficient and less effective, with a greater likelihood of a failure in your data structure down the line.
Read more about Tableau Data Management and governance in Tableau to gain more insight into how these two processes must work together. Gaining this understanding will help your organization make the most of the data you have available to you and make strong, strategic business decisions.