Data Governance: Definition, Framework Components, Best Practices & Benefits
Data is undoubtedly an organization’s most valuable asset, and it is growing exponentially. According to Statista, the volume of data created globally is expected to exceed 180 zettabytes by 2025. This means businesses can capitalize on a wealth of information to optimize operations, enhance customer experiences, and make strategic planning and decision-making easier and more efficient. However, like all valuable resources, data must be effectively managed and protected to reap the most benefits.
This is where data governance comes into play. In this article, we’ll explore the world of data governance, answering key questions and shedding light on its importance, benefits, challenges, best practices and how to reinforce your organization’s data governance strategy.
What is data governance?
Data governance is a set of policies and procedures that ensures holistic data management throughout its lifecycle, from creation to disposal. TechTarget defines data governance as “the process of managing the availability, usability, integrity and security of the data in enterprise systems, based on internal data standards and policies that also control data usage.”
Data governance covers all aspects of data management and protection — how data is generated, processed, used and shared, where it is stored, who has access to it, for how long the data needs to be retained and how to dispose of data appropriately and securely. Essentially, it is a blueprint outlining who can take what action based on what information, under what situations and using what methods. A well-established data governance strategy ensures information is effectively and judiciously used across an organization.
What is the purpose of data governance?
The main purpose of data governance is to ensure that data is managed in a way that is secure, compliant and aligned with an organization’s goals and values. It involves establishing policies and procedures for data usage, ensuring that data is consistent, reliable, usable and secure, and assigning responsibilities for data management to data stewards. The ultimate goal of data governance is maintaining data quality and enabling organizations to make informed decisions based on reliable, accurate data.
Well-planned data governance initiatives help businesses comply with constantly evolving data privacy and protection laws and other relevant regulations. Data governance ensures critical data is available at the right place and time, allowing businesses to extract maximum value from their data. It protects an organization’s sensitive information from loss, unauthorized access and corruption.
Why is data governance important?
According to Gartner analysts, through 2025, 80% of organizations attempting to expand their digital business will not succeed because they haven’t adopted a modern approach to data governance.
Today’s digitally fueled business landscape requires seamless and secure access to quality data. That’s why businesses must develop a well-defined data governance framework to ensure large volumes of data collected from various sources are clean, consistent, well-organized, appropriately utilized and adequately protected.
As per the Harvard Business Review Analytic Services survey sponsored by Profisee, 67% of respondents said data governance is important to achieving high-quality enterprise data. Data governance is critical for maintaining the quality of the data collected and stored because it impacts business outcomes in several ways. Even the success of advanced technologies like artificial intelligence (AI) and machine learning (ML) depends on the quality of the data. Therefore, data governance is vital for an organization’s digital transformation initiatives and meeting business objectives.
Good data governance sets the foundation for all data management activities by defining clear rules, responsibilities, standards and procedures for handling information assets. It helps maintain data accuracy and reliability, which are crucial for informed decision-making. A modern approach to data governance streamlines data processes, making them more efficient and cost-effective.
According to IBM’s Cost of a Data Breach Report 2023, the global average cost of a data breach in 2023 reached a staggering US$4.45 million, indicating a 15% increase over three years. It’s no surprise why many industries have strict regulations about how data is handled — for example, the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act of 1996 (HIPAA) and the California Consumer Privacy Act (CCPA). Data governance ensures an organization’s data is secure and compliant with stringent industry regulations. It not only helps avoid costly penalties but also protects the organization and its customers from data breaches and other security threats, preserving trust with customers and stakeholders.
How is data governance different from data management?
Although data governance and data management are closely related, they serve different purposes. Data governance focuses on data quality and reliability. It involves defining policies, procedures and rules for data utilization and protection. It’s about assigning responsibility for data management, documenting processes and ensuring that data aligns with the organization’s goals and values.
Data management deals with processes and tools — the practical aspects of handling data, including collecting, storing, organizing, controlling, safeguarding and processing it. It is the implementation of the policies and procedures set by data governance.
In short, data governance focuses on strategy and planning, while data management deals with implementation and execution.
Let’s consider the example of automotive manufacturing processes to better understand the concepts of data governance and data management. Think of data governance as designing, production planning, establishing safety guidelines and quality control standards. On the other hand, data management involves prototyping, assembling, welding, painting and testing.
What are the components of a data governance framework?
A data governance framework is a set of rules, standards, responsibilities, processes and procedures that lay the foundation for managing and securing data effectively. A purposeful framework is critical to driving data governance projects to ensure the accuracy, reliability, security, accessibility and usability of data in an organization. According to The Data Governance Institute, “a data governance framework is a logical structure for classifying, organizing and communicating complex activities involved in making decisions about and taking action on enterprise data.”
A data governance framework typically includes the following elements:
- Data policies: Rules and guidelines defining how data should be used and protected.
- Data standards: They define how data should be structured, formatted and stored.
- Data procedures: Outlining the processes for collecting, storing and handling data.
- Data stewardship: Identifying individuals responsible for data within an organization.
- Data quality: Continuously checking data to ensure it is accurate, complete, up to date and meets established standards.
- Data security: Protecting data from unauthorized access or breaches, use and disclosure.
What are the key pillars of data governance?
The key pillars of data governance are:
Businesses rely on high-quality data for insights and informed decision-making. Data quality involves maintaining the accuracy, reliability, completeness and consistency of data to enhance their strategic value. Data governance programs must include mechanisms to define data standards, establish data cleansing and validation and monitoring processes to maintain data quality.
This pillar defines the roles and responsibilities of data stewards, i.e., who is responsible for managing specific data sets. Data stewards are crucial for implementing data governance initiatives, identifying and resolving data issues, maintaining data quality and ensuring data governance objectives are achieved.
Data management is concerned with how data is handled throughout its life cycle, from collecting, storing, using, organizing and eventually disposing it. Data management aims to reduce data silos and security risks, enhance accessibility and usability, and optimize data for strategic decision-making. Data management encompasses many activities aimed at optimizing data usage cost-effectively, securely and efficiently.
Data security and compliance
Data security is non-negotiable for businesses of any size or across any vertical. A data breach, misuse or unauthorized access can cause irreparable damage to an organization’s image, financial health and relationship with its customers, and result in non-compliance with industry regulations. This pillar of data governance involves implementing security and privacy mechanisms to protect and ensure compliance with data protection laws and regulations.
What are the main data governance roles?
A strong team is required to correctly implement data governance rules, standards and procedures. The three main roles of data governance are listed below.
They hold authority over specific data sets and make decisions regarding their use. These individuals or groups are responsible for the overall management of a specific data set, for example, customer data, product information, financial records, etc. Data owners are often senior executives from different departments. They have the authority to make decisions and ensure they are implemented throughout the organization.
Data stewards or data governance ambassadors are responsible for maintaining data quality and ensuring compliance with data policies and standards. They are usually subject matter experts from their respective business units and not necessarily from the IT department. They manage and oversee specific data assets, optimize workflows, define access policies and establish security protocols.
Data governance committee
A data governance committee may include a master data governance manager, solution and data governance architect, data analyst, data strategist and compliance specialist. The committee does not create rules, policies or procedures but decides on them. They are responsible for overseeing the entire data governance program. The committee is also responsible for resolving escalated issues, such as disagreements over how data should be formatted.
What are data governance best practices?
Businesses rely heavily on data to make important, strategic decisions. Therefore, ensuring data quality, security, reliability and usability is critical for smoother, faster and more insightful decision-making. Here are some key data governance best practices to consider to safeguard your digital assets and maximize their value.
While people, processes and technology are critical elements of a data governance strategy, you must focus on one element at a time and gradually build up to create a well-balanced data governance policy. Begin with people, then focus on building the processes and finally implementing the right technologies.
Establish clear data ownership
Assigning clear data ownership roles within your organization is one of the most important aspects of data governance. It involves identifying who is responsible for specific data sets and who has the authority to make decisions about them. This helps to ensure data is managed consistently and reliably.
Define data quality standards
Establish criteria and implement data quality checks and validation processes for accuracy, completeness and consistency. Regularly monitor and cleanse data to eliminate duplicates, errors and inconsistencies. This helps ensure data is accurate and reliable, essential for making informed business decisions.
Implement data security measures
Data security is vital for any business. Implement robust security measures to protect data from unauthorized access, use, theft, loss and disclosure. Set up access controls, encrypt sensitive data and monitor data usage to detect and mitigate potential security threats.
Establish data retention policies
Data retention policies are a critical component of data governance. They ensure data is not deleted prematurely and is preserved or retained for the appropriate length of time. Establishing data retention policies ensures data is available for legal, regulatory and business purposes.
Establish data privacy and compliance policies
Data privacy and compliance policies are essential for protecting confidential information and compliance with data protection regulations like GDPR and HIPAA. They involve establishing rules and procedures for collecting, storing and sharing data, as well as ensuring that data is only used for authorized purposes.
Provide data governance training
Data governance training and awareness programs are critical for employees to better understand the policies and procedures. A clear understanding of the roles and responsibilities is crucial for successfully implementing data governance policies.
What are the benefits of data governance?
Good data governance offers numerous benefits to businesses of all sizes. Some key benefits are listed below.
Improved data quality
Data quality is critical for informed decision-making, generating accurate and insightful reports and meeting compliance requirements. Data governance strategies ensure data is accurate, complete, consistent and reliable.
Businesses often make decisions instantaneously or at lightning speed. The accuracy of data is crucial in such circumstances. Data governance ensures an organization’s data is accurate and trustworthy for faster and sound decision-making.
Meet regulatory compliance
With industry regulations getting stricter, meeting compliance requirements can be a challenge for businesses large and small. One of the key benefits of data governance is enabling businesses to comply with regulations effectively. Data governance ensures appropriate data security controls and processes are established to adhere to data protection regulations.
Data governance establishes rules, policies and standards to ensure data is managed, used, stored and shared in accordance with industry best practices and regulations. This means controlling access to data, encrypting sensitive data, monitoring data usage and implementing data retention policies. These data governance initiatives are crucial for safeguarding sensitive information from loss, breaches and other cybersecurity threats.
Improved customer experience
High-quality data provides actionable insights into customer behavior, enabling businesses to improve marketing efforts, accurately target audiences, respond to queries quickly and optimize customer experience.
What are data governance challenges?
Although data governance offers significant benefits, implementing robust data governance strategies is not without challenges. Some of the major challenges include:
An organization’s data is largely scattered among different departmental units, for example, research and development, sales and marketing, legal and finance. As such, there may be differences of opinion about specific data sets, such as around their definitions and formats. Therefore, getting buy-in from key stakeholders can be challenging due to perception. Cross-functional collaboration is vital for effective data governance. Businesses must streamline collaboration between stakeholders across various departments to implement data governance programs successfully.
Quantifying the value of data governance
Getting approvals and budgets for data governance programs from key stakeholders can be challenging if they don’t see the value. You must demonstrate the business value of improving data management processes and procedures on an ongoing basis through quantifiable metrics. These metrics may include the number of data issues fixed, duplicates removed, risks evaded, costs saved and revenue gained. Businesses must also set clear expectations, realistic goals and timelines that are achievable.
There’s a common misconception in many organizations that data and its governance is the IT team’s responsibility. Therefore, it’s important to establish clear roles and responsibilities for data management to prevent confusion and mismanagement. Well-defined data governance roles or dedicated data owners will ensure greater visibility and control of data assets.
Data fragmentation exists in almost every organization where information is stored in isolated systems or departments. Data silos happen due to businesses implementing new technologies or processes and often due to a lack of communication between different teams. Good data governance programs must break down data silos and foster team collaboration.
Support data governance with Spanning Backup
Businesses today gather and store vast amounts of data, including sensitive customer information and financial records. Cyberthreats pose a constant challenge to an organization’s mission-critical data, and navigating a complex web of rules and regulations can be cumbersome. Therefore, data governance is not an option but a must-have for every business, regardless of where the data is stored — on-premises or in the cloud.
Take your data governance efforts to the next level with Spanning Backup. Get end-to-end protection for your SaaS data and stay compliant with regulatory requirements.
Spanning Backup employs multiple layers of operational and physical security to ensure the integrity and safety of your data, including:
- SOC 2 compliance: Spanning is SOC 2 Type II certified, a rigorous evaluation of repeatable internal operational and technical controls, information technology processes and trust services principles.
- Application-level authentication: Spanning accesses SaaS systems using the OAuth 2.0 protocol rather than less secure service accounts and passwords.
- Strong encryption: Spanning Backup protects data at rest with 256-bit AES object-level encryption (one of the strongest block ciphers available) with unique, randomly generated encryption keys for every single object and a rotating master key protecting the unique keys. All data in transit is also protected with Transport Layer Security (TLS) encryption.
- Intrusion detection: Our systems constantly guard against intrusion with log analysis, file integrity checking, policy monitoring, rootkit detection, real-time alerting and active response.
- HIPAA compliance: Spanning’s service is hosted on HIPAA-compliant data centers.