What Is Data Governance?
Data governance describes the roles, processes, and policies that organizations enact to ensure data accuracy, quality, and security. Its policies dictate the methods people can use to access and use the data.
Although data governance involves properly managing data across an organization, the framework also encompasses your data strategy and goals. When implemented correctly, data governance allows you to realize value from your data.
Data governance frameworks ensure that policies and processes align with internal and external factors, including local and national governments' data privacy laws and regulations. As governing bodies pass new laws, you must adjust your data governance frameworks to comply with them.
It's often challenging to implement data governance properly because it requires you to align departments and teams, which means you must clearly understand what data you have and how you use it.
When developing a data governance plan, you should consider the following:
- Data ownership: Who is responsible for managing each type of data?
- Data quality: How will data be checked for accuracy and completeness?
- Data security: How will data be protected from unauthorized access?
- Data archiving: How will data be stored for long-term preservation?
Data Governance Tools and Benefits
Data governance tools include any software, platform, or device used to access, use, store, create, or analyze data and the policies and procedures used to implement and maintain their functioning.
Data governance tools include:
- Data lakes, warehouses, and catalogs
- Data assets such as spreadsheets, images, PDFs, and more
- Software and platforms used to access, analyze, and manipulate data, such as automation platforms, single-point solutions, and visualization software
- Infrastructure used to transmit data, such as business and personal devices, servers, cameras, medical and monitoring equipment, sensors, and more
Implementing a data governance framework that best utilizes all these tools can benefit you and your organization by improving business value, security and compliance, data quality, and automation.
Data governance improves your decision-making process, helps you better manage risks, and increases your operational efficiency. Data governance tools can help you collect and organize data from various sources so you can make more informed decisions. Data governance can promote collaboration between different teams and departments by establishing a clear process for sharing data and information.
Security and Compliance
Data governance needs to encompass a shifting workforce, remote workers, and new compliance regulations. Your framework will need to include plans for onboarding, adding, and removing permissions for new, current, and former employees while maintaining compliance. This process will include reducing the risk of data breaches for sensitive information.
You can improve data quality by establishing rules and standards for data entry, access, storage, and usage. By ensuring that data is accurate, consistent, and complete, you can make better decisions and avoid costly mistakes. By improving communication and coordination, you can make better decisions and prevent duplicating work.
Automation can help you ensure compliance as people access and use data, including data collection, quality assurance, and reporting. You can reduce costs by reducing the need for manual data entry and storage. Automation can also help reduce the costs associated with data breaches and compliance violations. Properly implementing data governance can help you optimize your use of resources, improve efficiency, and free up resources so you can focus on more strategic tasks.
Data Governance Framework
A data governance framework helps you establish and maintain an effective data governance program. The framework provides a structured approach for defining roles and responsibilities, developing policies and procedures, and implementing tools and controls.
Organizations that have established data governance programs report several benefits, including improved decision making, reduced risks, and improved organizational performance. Data governance can also help organizations to comply with regulatory requirements.
The Data Governance Framework is divided into four main components:
- Data Governance Policy
- Data Governance Structure
- Data Governance Processes
- Data Governance Controls
Data Governance Policy
Your data governance policy outlines your approach to data governance. Senior management should approve it plus review it regularly to ensure it meets compliance regulations and best practices.
It should answer the following questions:
- What is your approach to data governance?
- What are the roles and responsibilities of those involved in data governance?
- What are your data governance objectives?
- What are your policies and procedures for managing data?
- What are your standards for quality data?
- How will you ensure that data is accurate and complete?
- How will you protect data confidentiality?
- How will you ensure that data is accessible to those who need it?
- How will you ensure that data is used consistently with your organization's policies and procedures?
- What are your plans for monitoring and auditing data governance?
Data Governance Structure
Your data governance structure defines the roles and responsibilities of each person involved in your governance framework. You should design it to ensure all governance initiatives and activities align with your business objectives.
Your data governance structure should include groups or committees that oversee and are responsible for:
- Setting the direction for your organization’s data governance program
- This group usually includes senior executives
- Implementing the data governance program
- This group usually includes representatives from each business unit
- Managing the data assets of their business unit
- This group should include anyone who has a deep understanding of the data and how it is used in their business unit
Data Governance Processes
Your data governance processes will define the activities that need to be carried out to implement and maintain an effective data governance program. These processes should be designed to meet the specific needs of the organization.
Your processes should include:
- Data Identification — Identify your organization's data assets, including customer data, financial data, employee data, and more.
- Data Classification — Classify your data based on its importance to the organization to help prioritize your data governance activities.
- Data Quality Management — Ensure that your data is accurate, complete, and consistent. Include processes for auditing data and corrective action if you find errors.
- Data Security — Protect your data from unauthorized access and use, including processes for managing access, encrypting, and backing up data.
- Data Access — Control who has access to data and include processes for granting and revoking access to data.
- Data Lifecycle Management — Manage your data throughout its lifecycle and include processes for archiving and deleting data that is no longer needed.
Data Governance Use Cases
Because data governance covers many aspects of data, there are many use cases where data governance can improve your organization, including delivering value from the data in your data warehouses, lakes, and catalogs.
Consolidating to a single repository ensures consistency and accuracy for all users who base their decisions on data.
- Bottom-Line Returns — Create a well-defined foundation that streamlines the processes of connecting data sources and extracting insights.
- Risk Reduction — Eliminate discrepancies from departments extracting the same data in different ways.
- Workforce Upskilling— Help your end users discover and share insights instead of wasting time redoing work or questioning results.
Metadata and Data Lineage
Data lineage and governance are crucial to ensuring reliable results and the trust of users.
- Top-Line Growth — Establish and communicate data lineage to enable teams to focus on results and make decisions faster.
- Bottom-Line Returns — Fully understand and communicate data lineage to reduce the investment needed to make changes.
- Customer Experience— Eliminate mistakes caused by bad data that affects your customers.
- Efficiency Gains — Eliminate redundant or manual data validation and reduce the need to double-check results.
- Risk Reduction — Gain visibility into data lineage to understand where data originates and what processes it passes through.
As more people access and use data in your organization, the importance of data governance grows.
- Risk Reduction — Scale self-service analytics and data access without losing control.
- Efficiency Gains — Implement a data governance strategy that doesn't become a bottleneck for analytic access.
- Workforce Upskilling — Create a culture of analytics understanding and data governance best practices
- Bottom-Line Returns — Stop reinventing the wheel and implement analytic access and data security across your organization.
- Customer Experience — Get the most out of customer data while ensuring data security
Data Governance Use Case Example
Data Governance Business Use Case Examples
A successfully implemented data governance strategy should deliver results across any analytical endeavor your organization undertakes. This includes reporting, advanced analytics, machine learning, data science, and more.
In analytical environments without data governance, processes are time-consuming, and inefficient. They can also be non-compliant due to how data is shared and used.
Here are examples of how analytical environments with and without data governance affect reporting for businesses.
Reporting in Analytical Environments Without Data Governance
- Organizations lose valuable time deciphering what information datasets, reports, and other analytical assets contain; potentially leading to rushed reports and less time for evaluating information for decision making
- Unorganized and unlabeled analytical assets increase the time it takes organizations to locate known datasets, reports, and workflows, which increases time to insight
- Hidden assets, or dark data, goes unused, increasing difficulty in evaluating past and on-going performance and informing new decisions
Data Quality Management
- Analytical assets containing incomplete and inaccurate information increase time required to complete manual and repetitive preparation
- Missing data degrades accuracy and reliability in insights, reducing confidence in results and decision making
- Data stored across the organization on company and personal devices makes encrypting and backing up asset impossible while sharing assets through non-compliant media increases risk of data breaches and unauthorized access
- No standard, company-wide policy or process for granting and revoking access to data slows time-to-insight and limits scalability
Data Lifecycle Management
- Data management teams can’t properly archive or delete unneeded data, increasing out-of-compliance risks for organizations
Reporting in Analytical Environments with Data Governance
- Organizations can easily determine which assets are valuable to analysis and can produce reports quickly
- Faster time-to-insight allows organizations to ask more questions of data and further evaluate ideas to increase confidence in decision making
- Centrally organized and labeled analytical assets enable organizations to quickly find the data they need, including previous insights and workflows they can leverage or use, shrinking time to insight
Data Quality Management
- Data catalogs provide data quality scores that allow organizations to determine the completeness and accuracy of analytical assets, reducing the amount of time needed to prepare data for analysis
- Trust and reliability in reports increases confidence in results and decision making
- Strategic data security processes allow data management teams to maintain governance and compliance of data, reducing risk
- Standardized policies and processes for granting and revoking data access increases onboarding and scalability process while reducing management costs and risk
Data Lifecycle Management
- Centralized storage enables organizations to quickly audit all analytical assets, simplifying the encrypting, backing up, and removal of data across the organization
Although the above use case example shows the process for reporting, the examples can also be extended for other business use cases, such as advanced analytics, machine learning, and data science.
Data governance is about more than data management. It also includes the policies and processes used to determine how data is used within an organization.
You should use data governance frameworks to align teams and departments, improve efficiency, and increase business value.
With the proper framework and tools, you can properly implement a data governance plan that helps you and your organization realize actionable results.