What is data governance?

Data governance is the set of processes, policies, and tools that enables organizations to manage and maintain their data estate, including its availability, usability, integrity, and security.

The purpose of a data governance program is to ensure that data is accurate, reliable, accessible, and trustworthy. These programs consistently manage data in a collaborative manner that ensures security and regulatory compliance.

Data governance has become a crucial element of every enterprise strategy due to the integral role that data plays in almost every aspect of the business. By ensuring data is collected, maintained, and used correctly, it is possible to draw value from that data in the form of operational efficiencies, hyper-relevant and timely insights, and enhanced decision-making.

Historically, data governance programs were driven by the need for companies to manage an ever-increasing amount of data and guide digital transformation programs with data-driven insights. However, as the regulatory and risk landscape has become more complex, data governance has also become a key element of security and compliance strategies. This means that data governance processes and tools have expanded to include a broader range of activities and stakeholders, which necessitates a more structured approach.

Key components of data governance

There are three key components to a modern data governance program:

1. Data governance framework
2. Data quality management
3. Data security

Data governance framework

The data governance framework outlines clear rules and guidelines for how data should be managed and used across the organization. It also defines who is responsible for overseeing data governance activities and the tools they can use to do so. In this section, we’ll explore three key components of the data governance framework:

1. Processes
2. People
3. Technologies

Processes

As part of a data governance framework, companies must establish clear rules for how they collect, classify, store, protect, share, use, and dispose of data as well as the processes for how those rules will be executed, enforced, and adapted.

Though regulatory requirements often guide these efforts, organizations should think beyond mere compliance and consider how their data strategy can ensure access to timely, accurate, reliable, and complete data to guide business operations.

People

Though many aspects of a data governance program can be automated, people still need to spearhead program development and oversight; people are also responsible for ensuring that their data governance strategies align with organizational goals.

There are several key roles and responsibilities within a data governance program, including:

• The steering committee: A data governance steering committee brings together the company’s data and IT leaders as well as various business leads. Together, this group establishes the policies and standards that guide the organization and help meet business objectives. This group also determines the roles and responsibilities for overseeing the program’s execution, enforcement of the rules, and the program’s overall evolution.
• Data owners: Data owners oversee the management and governance of a specific set of data. Ultimately, the data owner is accountable for every aspect of the dataset, including maintenance, compliance, appropriate use, and security.
• Data stewards: Data stewards implement and maintain data governance policies and processes set by the data owner.
• Data custodians: Data custodians are responsible for maintaining the technical environment and infrastructure needed to maintain the data governance program.

Technology

The final pillar of the data governance framework is technology. Given the explosion of data at the enterprise level, companies must rely on tools to gather, process, clean, standardize, classify, analyze, store, and secure their data. These tools also ensure adherence to internal policies and procedures as well as compliance with regulatory and legal requirements.

Most organizations rely on an integrated data platform to ensure that data activities are connected and consistent and that different business units have access to relevant data from across the enterprise.

Data quality management

The second component of the data governance program is data quality management.

Data quality management refers to the overarching process of ensuring the accuracy, completeness, reliability, timeliness, validity, and consistency of data throughout its entire life cycle.

There are several components of data quality management:

• Data quality metrics: Implementing specific key performance indicators (KPIs) to assess and measure data quality and the effectiveness of data quality initiatives.
• Data cleaning processes: Regularly identifying and correcting errors and inconsistencies in data to improve data quality. These processes may include things such as standardizing formats or identifying duplicates.
• Data enrichment: Incorporating relevant information from internal or third-party sources to provide additional context.
• Data profiling: Analyzing data to identify potential anomalies or quality issues.
• Data validation: Enforcing rules and performing checks to ensure data is accurate and that its integrity is maintained when it is in use, in motion, and at rest.

Data security

The final component of a data governance program is data security.

Data security is the practice of protecting digital data from unauthorized access, use, or disclosure in a manner consistent with an organization’s risk strategy. It also includes protecting data from disruption, modification, or destruction.

There are several key components of data security, including:

• Access controls: Access controls ensure that only authorized individuals have access to sensitive data. One best practice for IT teams is to follow the principle of least privilege (POLP), providing employees with the minimum access privileges necessary for them to perform their specific job or task.
• Data protection measures: Data protection measures are any steps, policies, technologies, or practices that organizations use to protect data from unauthorized access, breaches, and other threats.
• Data loss prevention (DLP): DLP is the process of detecting and preventing the loss, leakage, or misuse of an organization’s data at all stages of the development life cycle.

Benefits of data governance

A comprehensive data governance program offers organizations several important benefits. Here, we’ll explore some benefits across the categories of data quality, regulatory compliance, risk management, and operational efficiency.

Improved data quality

• Better decision-making: Stronger data means a stronger business. By ensuring that data used across the organization is consistent and accurate, companies make better, stronger decisions, identifying potential issues before they impact operations and creating more opportunities for collaboration.
• Enhanced trust: Clear data governance policies that are monitored and enforced consistently build trust among users and stakeholders. With a strong program in place, people are confident that they are using reliable and accurate data to make decisions.

Enhanced regulatory compliance

• Standards adherence: Data governance programs are created in a way that helps organizations comply with relevant industry regulations and standards, such as the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and California Consumer Privacy Act (CCPA).
• Audit readiness: A data governance program also includes clear documentation processes that allow companies to more easily respond to verification requests or comply with an audit.

Improved risk management

• Proactive security: A data governance program implements proactive data protection and data security measures to safeguard data against potential threats and vulnerabilities.
• Risk mitigation: A data governance program also provides reactive security support,  identifying and mitigating risks associated with data breaches, data loss, and noncompliance.

Greater operational efficiency

• Streamlined processes: A robust data governance program streamlines data management processes across the organization, helping teams capture important efficiency gains through standardization.
• Cost savings: Automating key aspects of the data governance program optimizes resources and reduces costs. These programs can also eliminate costs associated with manual processes, errors, and rework.

Implementing data governance

From fueling transformation initiatives to maintaining compliance, data governance plays an important part in virtually every aspect of modern business operations. Here, we’ll lay out the basic steps associated with implementing a data governance program.

Establish a data governance team

Though it’s true that adhering to data governance policies and processes is everyone’s responsibility, it is important to ensure that there is a specific team and executive sponsor who is accountable for the program.

To develop an effective program and ensure its adoption, IT leaders must bring together a cross section of functional leaders to set goals, define the strategy, develop processes and rules, select tools and technologies, and develop mechanisms to track and measure progress.

This team should also be tasked with overseeing the program’s operation and defining the key roles and responsibilities of the program. For example, the steering committee should specify which people in the organization will be accountable for maintaining different aspects of the program, including execution, infrastructure development, data maintenance, security, compliance, and more.

Develop a data governance strategy

One of the main tasks of the data governance steering committee is to develop the data governance strategy.

To begin, the group should conduct a thorough assessment of current data management practices and develop a detailed implementation plan based on the organization’s goals as well as best practices and regulatory requirements.

The strategy may include the following elements:

• Setting clear, achievable goals
• Identifying specific roles and responsibilities
• Developing the governance framework
• Implementing comprehensive data quality management protocols
• Defining strong data security and privacy policies
• Monitoring and reporting data governance program use and effectiveness
• Evaluating and selecting technologies and tools
• Training and educating staff
• Enabling a culture of continuous improvement

Tools and technologies

Most data governance programs are underpinned by a data governance platform. A data governance platform is an end-to-end software solution that supports the entire data governance strategy and helps teams automate core processes related to data governance.

When selecting a data governance platform, it is important to consider the following:

• Integration: Can the data governance platform support existing data management systems? Can it be integrated with other tools and technologies within the tech stack?
• Automation: Does the platform leverage the latest AI and machine learning (ML) capabilities to enable intelligent operations?
• Scalability: Can these tools scale over time to support the ever-increasing volume of data and an expanding set of data sources? Does it leverage the cloud to easily accommodate ebbs and flows?
• Usability: Does the tool provide access to key insights via an intuitive and customizable dashboard?

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Implementing a robust data governance framework is essential for organizations looking to leverage data as a strategic asset while ensuring compliance and minimizing risks. By establishing clear policies, roles, and processes, organizations can improve data quality, enhance security, and achieve regulatory compliance. As data continues to grow in volume and importance, effective data governance will remain a critical component of the organization’s overall health, performance, and success.

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