Data governance best practices: The core components of successful governance.

Explore data governance best practices, key governance challenges and core components to success in this guide snippet.

Data governance best practices

Ensure stakeholder buy-in by encouraging data owners to play a key role in the program. Often, owners may fear being ostracized for being “data police” when other staff are engaged who have no data ownership responsibilities. Governance is necessary and effective in any modern business, but employees on the ground are often reluctant to have more rules and restrictions imposed on them. Other best practices include:

●  The benefits of the program must be clear and regularly communicated to all staff. The focus should always be on how the program will improve the business.
●  Design robust data governance training, both for those involved in the program and for end-users.
●  Ensure internal teams agree and are made clear on who is responsible for data and who has access to data.
●   Data security and risk management must be a key consideration of the program.
●  Collaboration and widespread participation must be encouraged.

Several bodies promote data governance best practices including EWSolutions, The Data Governance Institute, DAMA International, and others. Engaging with these bodies can give organizations greater insight into best practices in this space.

KEY CHALLENGES

No new initiative is implemented without a hitch, and data governance is no exception. Initially, some employees may struggle to see the value in the program and be reluctant to engage. This can become a roadblock to getting the program approved and funded at all. The best way to combat this is to be vocal about the benefits but also be vocal about the risks of not having a data governance program.

Another key challenge is maintaining control over data as the benefits of analysis are shared with more teams. For example, contact center teams often want a live feed of KPI metric data displayed within the workspace. Real-time analytics has inherent problems when it comes to ensuring an accurate representation of data.

The core components of successful data governance

High-quality data

Improving the quality of data within the organization is a key driver of data governance programs. The benefits of the program can only be actualized with high-quality data. Organizations improve data quality by conducting data scrubbing and data cleansing activities. These activities will seek out and remove duplicate data. They will also fix errors and inconsistencies.

Effective data stewardship

Policies and rules can only be effective if they are understood and enforced. This is where data stewards shine. They help implement and enforce data governance policies and engage end-users to ensure compliance.

Master data management (MDM)

MDM is a data governance discipline focused on integrating all enterprise data of a certain type (for example, customer data), into a single point of reference. This single database is essentially the master data asset for the enterprise. This master list can be used to ensure that data is consistent across the business and that all teams use the same terms for products, customer segments, projects and so on.

Key data governance use cases

Data governance can underpin may activities in an enterprise. For example, successful data governance supports the business intelligence and data analytics teams. It plays a key role in digital transformation, a key focus of many businesses today. It helps to reduce security risks to the organization. It also helps with risk management, mergers and regulatory compliance.

Access the full guide

Data governance is a complex topic that spans a wide range of industries, departments and stakeholders. In order to fully understand what it means, and how to practice good data governance, we have compiled a detailed and comprehensive guide answering the following questions:

What is data governance?
Why is data governance important?
What are the aims and benefits of data governance?
● Who oversees data governance in an organization?
What does a data governance framework look like?
How can enterprises implement data governance?
What are some data governance best practices?
What can NodeGraph do to help?