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How to Build Your Data Governance Strategy

Here are the five key steps to take when creating a data governance framework in your organization.

Business is built on reliable data foundations. With the increasing volume of data gathered by businesses, establishing a robust foundation capable of managing and leveraging data can pose challenges. A well-executed data governance strategy can help you unlock the insights needed to steer decision-making, drive business innovation, and improve collaboration.

As you formulate a data governance strategy, it’s important to remember that data governance is not a technology; it’s an organizational commitment that involves people, processes, and technology. Here are five steps you can follow to build a data governance strategy that empowers your business to unlock the full potential of your data.

1. Design the preliminary framework and establish a team

At its root, data governance centers on the people who build and apply it. So, the first step is to establish a core team of stakeholders and data stewards to do the preliminary work in creating a data governance framework.

This process begins by conducting an audit to identify any issues with current data management policies and areas that need improvement. Subsequently, you can develop a business case for data governance, define guiding principles that pinpoint what’s important, and establish decision rights that identify what decisions need to be made, who will be involved in making them, and how they will be made. The core team can then form a governance council that includes executives responsible for the governed data and stakeholders from all organizational sectors to ensure that rules and procedures are cross-functional, which is a requirement for compliance. The council should clearly define roles and requirements:

  • Who will own the program or implementation?
  • Who will own certain types or areas of data?
  • Who will own the policies?
  • How will policies be enforced?

2. Define requirements and policies

The next step is to define the problems you’re looking to solve through data governance. Is it better regulatory compliance, increased data security, improved data quality, or all three? Within these overarching areas, what are you attempting to achieve with your data? Here are some examples:

  • Provide access to more users
  • Create more transparency about the flow of data through the organization
  • Increase the accuracy of external data
  • Determine what your rights are for using the data
  • Remove a data silo and integrate the information into other parts of the organization
  • Create a central source of truth for data
  • Restrict sensitive information
  • Track and categorize the sensitivity of data (non sensitive, sensitive, or extremely sensitive)
  • Establish a set of rules for deleting data
  • Revoke an employee’s access to data upon job termination
  • Institute a set of data rules for a government compliance audit

Once you've compiled your list, establish your priorities and  begin with those that are most critical. Identifying and restricting sensitive information is often a good place to start for compliance and security reasons.

3. Assess available tools and skills 

Does your organization have the skills and technology needed to execute its data governance program successfully? You’ll need individuals proficient in data modeling and architecture, business analysis, program and project management, database administration, and report development. You’ll also need tools for data modeling, data cataloging and management, data movement (for example, a data pipeline), data quality control, and data reporting and analysis.

4. Identify and address capabilities and gaps 

After assessing your current resources, you’ll need to identify and address any gaps. You can fill these gaps by purchasing tools and hiring in-house specialists or by using partner tools and third-party specialists.

5. Execute

You’re now ready to inventory your data. What data do you have and where does it reside? Who has access and how do they use it? Companies often start with data modeling, which involves the use of diagrams, symbols, and textual references to represent the way the data flows through a software application or the data architecture within an enterprise.

A centralized metadata repository, which stores descriptive information about the data such as its meaning, source, and relative quality, will help you locate and secure your most sensitive data. It will also help you understand and manage the lifecycle of your data so you can ensure its proper disposal at the end of life.

As data proliferates, iterate

As your company’s data grows, you will need to continue to establish rules and procedures around its governance. Data governance is not just a project you can launch and then wrap up in an 18-month period. It is a continuous organizational commitment that needs to grow and adapt to new compliance regulations, business needs, and security challenges. Building a permanent data governance team and structure to meet these challenges will help ensure the future success of your organization.

If you need help crafting a data governance strategy that lays the foundation for efficient data management and maximizes the value of your data assets, reach out to Concord. We're here to support your data governance journey and ensure your business thrives in the data-driven landscape.

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