Data governance is like a traffic cop overseeing your organization’s data: It directs you data where it’s meant to go while ensuring its cleanliness, organization, and security. Effective data governance helps you make better decisions, control costs, comply with regulations, and gain the trust of your customers and suppliers. Here’s what you need to know.
What is data governance?
Data governance is the process of managing and protecting an organization’s data assets—such as customer, financial, or operational data—throughout its lifecycle. This involves developing and implementing policies and processes that ensure data availability, integrity, privacy, and security. The goal is to establish a framework that defines who has the authority to make decisions regarding data, how to manage the data, and how to manage data-related risks. It must also reflect the regulatory requirements and industry protocols that apply to your business.
In today’s data-driven landscape, the volume and complexity of data are growing exponentially, meaning effective data governance is a vital practice. Data governance responsibilities typically fall to the chief data officer (CDO) or another designated data governance manager. This role will determine and oversee the software used to manage data quality and security.
Data governance vs. data management
While data management is concerned with the operational aspects of data, such as storage, backup, and retrieval, data governance focuses on the policies and procedures for data handling, ownership, quality, and security. Data governance teams set the standards and procedures for data management, while data management teams carry out those policies.
What your data governance strategy needs to cover
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Data policies and standards
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Data quality control
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Data security and privacy
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Data management
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Metrics and oversight
As you begin to work with big data, a comprehensive data governance strategy ensures the proper use, management, and protection of your data assets. Here are the five components of a robust data governance strategy:
1. Data policies and standards
The data policies and standards must outline what data is being governed and how it is being governed. This requires determining the scope of the data, such as whether it’s master data, transactional data, operational data, analytical data, big data, or a different category.
2. Data quality control
Organizations must consider data integrity in their procedures, from data creation to management to protection. Data integrity refers to the assurance that the data is accurate, consistent, and reliable throughout its lifecycle. It’s mainly concerned with maintaining data’s completeness, validity, and reliability by preventing unauthorized access, data corruption, or loss.
3. Data security and privacy
Data privacy and security involves identifying and safeguarding sensitive customer data, such as addresses or payment information, and controlling access to it. You may establish data ownership policies, which define who is accountable for what data, or establish data stewardship policies, which outline responsibilities for managing and protecting the data.
4. Data management
Effective data management practices must account for the following:
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Data architecture. Data architecture refers to the data’s overall structure, design, and organization. It involves defining data models, data storage mechanisms, integration approaches, and flow across systems.
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Metadata management. Metadata refers to the descriptive information about data. Therefore, metadata management involves capturing, organizing, and maintaining “data about data,” such as its characteristics, definitions, and relationships to other data.
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Master data management (MDM). Master data refers to the core data entities within an organization that are essential for its operations, such as customers, products, or suppliers. It involves establishing processes, standards, and technologies to ensure the consistency, accuracy, and integrity of master data across different systems and applications.
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Data catalogs. Data catalogs are centralized repositories that document and index available data assets within your organization. They’re a comprehensive inventory of datasets, including descriptions, data sources, quality metrics, and usage information.
5. Metrics and oversight
To measure the success of your data governance strategy, establish specific parameters or metrics to track. These might include data quality, accuracy, accessibility, compliance with privacy regulations, and alignment with business objectives.
A dedicated quality control team typically conducts regular checks to ensure procedural compliance and adherence to these established parameters. They might be tasked with reviewing data governance processes, evaluating data quality controls, verifying compliance with policies and regulations, and identifying areas for improvement.
Why it’s important to have a data governance strategy
Data governance is essential if you want to protect your data quality, security, and compliance efforts. Only 33% of customers believe brands use their data responsibly, and regional governments are stepping up to protect their citizens.
Without proper data governance, inconsistencies in different systems across your organization—such as discrepancies and conflicting information—may go unresolved, leading to data integrity issues and impacting the accuracy of business intelligence, enterprise reporting, and analytics applications.
Organizations that lack data governance may also fail to comply with data privacy and protection laws, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the US, resulting in potential penalties and reputational damage.
Benefits of implementing a data governance strategy
Here are six key benefits of implementing a data governance strategy:
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Improved data accuracy. Effective data governance programs ensure data is used consistently and accurately across applications, ultimately reducing errors.
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Enhanced data management practices. Data governance breaks down data silos and promotes harmonization across different systems, enabling better collaboration and data-sharing within the organization.
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Strengthened data security. Data governance programs establish policies and procedures to monitor data usage and enforce security measures, mitigating the risk of unauthorized access or security breaches.
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Enhanced regulatory compliance. Data governance ensures adherence to internal and external mandates, allowing you to meet regulatory requirements such as GDPR and CCPA, protecting customer privacy, and avoiding legal and compliance issues.
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Improved decision-making. With reliable and trustworthy data, leadership can make more informed decisions.
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Competitive advantages. Data governance allows organizations to leverage their data assets effectively, improving operational efficiency, cost controls, and better business performance.
Consequences of not implementing a data governance strategy
Organizations that lack proper data governance face several disadvantages, including:
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Poor data quality. Without data governance, inconsistent data across systems can lead to errors and inaccuracies, undermining the reliability of business intelligence and analytics applications.
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Increased costs. Ineffective data management practices, lack of standardized procedures, and the absence of data governance can result in higher data management costs and operational inefficiencies.
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Legal and regulatory risks. Non-compliance with data privacy and protection laws, such as GDPR and CCPA, exposes you to legal and regulatory consequences, including potential penalties and reputational damage.
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Decreased customer trust. Inadequate data governance practices can erode customer trust and loyalty, particularly regarding the handling and protecting of their personal information.
How to implement a data governance strategy
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Identify custodians
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Develop rules
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Create controls and audit procedures
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Document data assets and flows
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Embed data governance within your organization
By following these steps, organizations can establish an effective data governance program that promotes data accuracy, improved data management practices, and enhanced data security.
1. Identify custodians
Identify the owners (or custodians) of different data assets and involve them in the governance program. This ensures accountability and ownership over specific data sets.
2. Develop rules
Develop rules that clearly define authorized personnel who can use data. These should outline access controls, usage guidelines, and any restrictions or requirements for data handling.
3. Create controls and audit procedures
Create controls and audit procedures to ensure ongoing compliance with data governance policies and standards. Regular audits and reviews help monitor adherence to the established rules and identify areas for improvement.
4. Document data assets and flows
Create documentation that outlines your data assets and how data flows through your organization by conducting data mapping and classification. This process helps classify data sets based on sensitivity, enabling the appropriate application of data governance policies.
5. Embed data governance within your organization
Finally, integrate data governance practices into existing workflows, procedures, and systems to ensure consistent and standardized data handling practices.
Learn more: What Is Data Profiling? Types and Why It Matters
Data governance FAQ
Who is responsible for data governance?
The responsibility of data governance falls on the chief data officer (CDO) or a designated data governance manager. They must identify the data assets and their respective owners or custodians, then create a governance framework and structure, staff the data governance team, and formalize the governance committee.
What are the challenges in implementing data governance?
The challenges in implementing data governance include identifying the data assets and their owners, getting buy-in from stakeholders, defining policies and standards, developing controls and audit procedures, and ensuring ongoing compliance with internal policies and external regulations.
What are the best practices for implementing data governance?
Best practices for implementing data governance include:
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Outline a robust framework that details your data management practices, critical data assets, and data owners.
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Identify and appoint data stewards who are responsible for ensuring data accuracy, integrity, and security.
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Increase the data literacy and skills of end-users and establish a shared vocabulary for business data to aid governance efforts.
