An Introduction to Data Governance and Management for Marketers

Data governance and data management are critical to the success of your business intelligence program and your entire organization. Here’s the first of a multi-part series to understand and optimize your approach to data governance and data management.

Welcome to the first post in our multi-part series all about data management and data governance. In this post, we define each approach and introduce some basic concepts. 

Additional posts will explain the importance of data management and data governance and help you start implementing this powerhouse pairing. Then we’ll demonstrate how one business intelligence platform enables and supports data management and data governance. Later on in the series, we will discuss using your BI platform to govern your BI platform and more about platform governance.

The Role of Data

Businesses collect, store, transform, and analyze more data than ever, and data is one of the most significant assets that an organization has.  As with all large assets, it is crucial to invest time, people, and financial resources into growth and maintenance. Enter data governance and data management.

What is Data Governance?

Data governance is the process of making the rules of the road. More technically speaking, Data governance sets the goals of policies and procedures within an organization for defining who has authority, control, access, and decision rights over data.

How Data Governance and Data Management Work Together

You can think of it this way: The Department of Transportation typically determines the roads’ speed limits. The police department enforces those speed limits. The Department of Transportation acts by setting a policy (data governance), while the police department acts by implementing the policy (data management).

Surprisingly,  the combination of data governance and data management is an often overlooked and/or under-utilized  area for many organizations that strive to be “data-driven.” That’s too bad.  A well-developed data governance program and a well-executed data management policy can lead to many benefits.

For instance, we have all heard the adage,  “garbage in, garbage out,” which could be the mantra for all data governance strategies. By ensuring that you have a strategy to validate data, you can establish that your data is trustworthy, leading to better analysis, better business decisions, and better business results. 

How They’re Alike

There are a lot of similarities between data governance and data management. Put simply, data governance is the ‘who does what’ of data management. They share the same goals: accountability, standardization, quality, and consistency; however, each achieves their goals through different means.

The data governance and data management funnel

How They’re Different

Even with all their similarities, there are still some differences between data governance and data management. While data governance sets the goals, policies, strategy, charter, and roles/responsibilities, data management carries out the task/implementation of data validation, policies/procedures, data architecture, data dictionary.

The Governance/Management Hierarchy

When starting a data governance/data management initiative, we typically introduce this hierarchy:The data governance and data management hierarchy

This hierarchy helps differentiate between which tasks are owned by data governance and which are owned by data management.  What is most important to call out is that all tasks from data governance flow into the main task of data strategy, a plan to use data with purpose.  Data strategy directly flows into the data management realm and subsequently drives all tasks therein.  You cannot have effective data management and governance without data strategy as the critical output of your data governance.

The role of data governance is most often a business strategy.  In many ways, data governance represents policies and procedures that must be aligned across the organization’s entirety.  However, data management roles are typically much more concentrated with the company’s IT and/or BI groups. With the abundance of data used for decision-making, Data management has moved into companies’ business units. Marketers are learning the benefits of data management to manage their data assets and BI Platforms better.

Team Member Roles

Many organizations do not have resources dedicated solely to data governance and data management, and instead, these duties are simply part of a person’s overall job. Since each person likely has multiple job duties within the organization, it is important to clearly outline what needs to be done within data governance and data management initiatives and who will fill the necessary roles.

In general, we recommend five roles for the execution of a data governance and data management strategy.

  1. Executive Sponsor – This person champions the initiative. They often provide funding and strategic direction for the program.  This person is aligned with the data governance tasks.
  2. Data Governance Council – This is the group that supports the development of the data strategy.  This group is aligned with data governance and data management tasks; however, their primary focus is to create a data strategy that the data management group can then execute.
  3. Chief Steward – This is the person that enforces the practice and adoption of the standards. This person is aligned with data governance and data management tasks and is a liaison between the two groups.
  4. Business Data Steward – This is the person who supports standards and policies from a business perspective. They are aligned with the data management tasks.
  5. Technical Data Steward – This is the person that provides standardized data definitions, system architecture, and data flows between systems. They are aligned with the data management tasks.

To take this one level deeper, we will typically leverage a RACI chart to show which tasks are associated with each role.  By having a clear definition of what each role is responsible for, it is easier to align resources to the appropriate roles and set expectations that align with their given tasks.

Screenshot 2021 03 11 at 21.36.05

Conclusion

Now that we have laid the groundwork around data governance and data management basics, please join us next month for our post about why these concepts are so important. In that post, we will cover specific reasons for a company to have data governance and data management and explain how those items can positively impact an organization.

Need help to educate your team about data governance, management, or strategy? Reach out here today.

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