Data is a valuable asset that helps companies conduct business, grow, and maintain a competitive edge in the modern marketplace. While data presents many opportunities, it also creates risks. A data governance framework establishes the processes and practices your company follows for managing and using data assets to mitigate this risk.

Data governance enables you to make decisions related to data. By defining and following companywide standards, creating accountability, and integrating data management processes into organizational changes, you help ensure the accuracy, availability, integrity, and confidentiality of your data. This, in turn, helps reduce security risks and protects data privacy.

A secure data governance framework also plays an important role in maintaining regulatory compliance. Although requirements vary by industry, laws such as the European Union’s General Data Protection Regulation (GDPR) and the U.S. based National Institute of Standards in Technology (NIST) affect all business sectors. By adopting a data governance framework and following best practices, your company not only improves compliance but also ensures that the people, processes, and technologies you use to manage data align with your business objectives.

Data governance vs. data management

Data governance and data management are sometimes used interchangeably. Although they are tightly related, they represent different functions—one is a strategy and the other is a practice.

In short, data governance is only one component of data management and is focused on establishing data-related rules, processes, and procedures. It answers questions such as who owns the data and who can access it.

Data management is about implementing the infrastructure, processes, and technologies for managing data through its entire lifecycle. Its focus is on logistics—everything from data storage and operations to data security.

What is a secure data governance framework?

The overall purpose of secure data governance framework is to ensure your company’s data is available, usable, accurate, consistent, and secure throughout its lifecycle, including:

  • Intake and ingestion
  • Sharing and backup
  • Removal and deletion

The framework’s guidelines and rules create a logical structure that helps your company make decisions about data. Think of it as a tool that helps you decide how to decide.

Your data governance framework depends on your company’s specific business needs, as well as maturity. Models range from reactive, where processes are largely informal or just emerging, to fully governed, where processes are controlled and optimized.

In a basic model, companies are more likely to use manual and inconsistent processes. In a mature model, companies have a sophisticated framework and strategy.

Challenges to address through the framework

Every company faces different data challenges. An effective data governance framework should consider and address these challenges. Challenges are often due to a lack of:

  • Leadership and clear governing structure—who controls what and who’s responsible for what
  • Understanding of data’s business value—how data helps the company grow and make a profit
  • Ownership and budget—the people, financial, and computational resources necessary for data governance activities
  • Stakeholder buy-in—the misconception that IT is the only one owning and managing data
  • Executive sponsorship—prioritized focus on data governance coming from the top

Five core elements of a data governance framework

A data governance framework comprises five main components that guide the data governance program:

  • Why: the mission and purpose
  • What: the goals of the program
  • Who: the people involved
  • When: the stages of the process
  • How: the rules and definitions

The mission

As with any solid strategy, creating a secure data governance framework starts by defining why you need a data governance program and what is its mission. By understanding and documenting both your business objectives and challenges in meeting those objectives, you can identify your data management gaps and focus your framework on areas that need improvement.

The goals

Closely tied with the program’s purpose and missions, goals specify what you want the program to achieve. Typical goals may include:

  • Minimize organizational risks
  • Maintain data security, privacy, and compliance
  • Improve employee productivity
  • Make better data-driven decisions
  • Get more value from the data

Two other components of “what” include the strategies that you’ll need to implement to reach the goals, as well as the metrics that will measure the success of those strategies. Strategies may range from adopting security-centric policies to implementing various platforms, tools, and technologies that support the process.

The stakeholders

Typically, a data governance council, board, or committee oversees the data governance process, developing policies, and coordinating and balancing the needs of the other stakeholders. This group’s main responsibilities are strategy and accountability.

The other people components include:

  • Data owners: Part of the governance committee and are responsible for ensuring that data is governed appropriately across different business units at a high level.
  • Data stewards: Responsible for day-to-day data management activities.
  • Data users: Those with access to data.

Companies choose different approaches and structures for these roles. Some designate a chief data officer to oversee the data management activities, while others may have an entire management team.

The processes

The processes—the “when” of the data governance framework—help you implement and maintain your data governance program. The steps may look like this:

  • Discover and analyze—assess the current state of data and processes
  • Create a roadmap—helps visualize the steps and milestones
  • Develop the plan—this should include funding considerations
  • Implement—operationalize the plan
  • Monitor, control, enforce, and measure—monitor, control, and enforce your framework, and measure both the quality of the program and the quality of the data, and report the results to the stakeholders

The definitions

The “how” element provides the context and definitions that will help make the data decisions. Definitions include data policies, standards, rules, and other structures and methods that need to be applied across the company.

10 steps for creating an effective strategy

While some of the steps for creating a data governance framework are evident from the framework’s components, documenting the creation process step by step helps keep the milestones on track and ensures you don’t miss important pieces.

Recommended steps include:

  1. Determine the maturity model for your data governance.
  2. Choose the focus areas that you want to improve and the goals for the program.
  3. Select a model and hierarchy for your data governance team.
  4. Define and create the roles and responsibilities for the key stakeholders.
  5. Identify the key decision makers.
  6. Choose the data owners and the data stewards.
  7. Create the policies and procedures.
  8. Determine metrics to measure the program’s effectiveness.
  9. Set up accountability mechanisms.
  10. Select the tools and technologies to support data governance.

Best practices for data governance frameworks

Although every company is different, you can use best practices as a general guideline. As you implement and evaluate your strategy—and gain a comprehensive understanding of how you use and manage your data—you can further customize those best practices to your use case.

Consider the following best practices as a starting point for your data governance framework:

Measure goals: Your framework’s goals need to be straightforward and quantifiable. Establish the milestones and the metrics that measure the program’s success.

Educate all stakeholders: Provide relevant context, definitions, and other content to help all the people involved in data governance understand the bigger picture, not just their specific roles.

Provide transparency: All the data governance participants—as well as auditors—need clarity about how and when data controls, decisions, and processes are created.

Ensure proper oversight: To create accountability, you need checks and balances among the different data participants, from those who collect and use the data, to those who create the standards and those who manage the data.

Map your data: Mapping provides a clear understanding of your data environment, including the scope, footprint, and flow.

Consider your risks: A risk-based approach to data governance helps you better mitigate your risks and ensure you’ve defined the right goals for your program.

Think security first: Although compliance is an important objective, your data governance framework should achieve more than simply checking off compliance requirements. A security-first approach helps you safeguard your data while also boosting compliance.

Leverage the right tools: Technology solutions designed for secure data collection, use, and management not only streamline your data governance processes but also ensure your processes meet your defined rules and guidelines, as well as regulatory requirements.

Final thoughts

Data governance ensures the quality and security of one of your most valuable resources—data. In today’s complex and fast-changing business environment, you need to balance your business objectives with the risks that data usage imposes. Regardless of where you are on your data governance maturity journey, a secure data governance framework is a critical part of implementing and managing a successful strategy.

The security and regulatory landscape can create challenges for data governance. Take advantage of tools and technologies that eliminate manual steps, reduce your governance costs, and ensure that you protect your data.

SailPoint File Access Manager helps you get visibility and control over you unstructured data. You can quickly and compliantly discover sensitive information, such as PII, PHI, and PCI data and classify and secure it in accordance with regulations such as GDPR, CCPA, and HIPAA. In addition, File Access Manger helps to identify and elect data owners and alert them to remediate risk exposures and manage data access requests and reviews.

To learn more, please see our File Access Manager webpage.

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