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What is Data Governance? Your Definitive Guide

By Scott Moore, Director of Presales, Semarchy

Introduction to data governance – what does it mean?

In today’s interconnected world, organizations generate and store vast amounts of data that play a crucial role in enabling decision-making and driving business growth. However, with this abundance of data comes the need for proper management, control, and protection to ensure it remains accurate, consistent, and secure. This is where data governance comes in.

But what is data governance?

Data governance refers to the overall management framework, policies, processes, and strategies that govern how organizations collect, store, process, use, and share data. It comprises a collection of principles, standards, and practices that ensure your data is:

  • reliable and consistent,
  • accessible to those who need it,
  • aligns with business objectives,
  • meets ethical and privacy standards,
  • and complies with regulatory requirements.

Data governance provides organizations with a systematic approach to managing their data assets, enabling them to derive meaningful insights, inform decisions, and power digital transformation. By implementing a successful data governance program, organizations can achieve these objectives consistently and repeatedly.

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Understanding the importance of data governance

So, why is data governance important? It’s quite simple: data is a valuable asset that enables enterprise growth and customer conversion. However, gathering data isn’t enough; organizing and managing it effectively is necessary to maximize its value. This is especially true for large enterprises dealing with massive volumes of data. 

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By 2025, global data creation is projected to grow to more than 180 zettabytes. While collecting data is essential for success, proper data governance and management are prerequisites for leveraging its potential. Failing to do so renders the collected data meaningless despite having the potential to drive success.

In essence, data governance is essential for maintaining data integrity, ensuring compliance, managing risks, and leveraging data as a strategic asset for business success.

Best practices for data governance

Data governance best practices start with creating a cross-functional team that will oversee the creation of a data governance framework and implementation across the organization. The team must assign clear roles and responsibilities and appoint data stewards who manage, protect, and enhance data accuracy according to established policies and standards.

Another essential practice involves defining and implementing standards and procedures. This includes data definitions, quality standards, privacy, and security procedures. Having clearly defined parameters ensures the consistency and integrity of data across the organization. It is also vital to regularly measure and report on data quality to evaluate the effectiveness of data governance efforts and refine them as needed.

Lastly, change management and education are critical. Employees must feel empowered and equipped to manage data correctly. This best practice involves fostering a data-driven culture through training and incentives. Moreover, communication regarding the importance of data management to the company’s success is critical.

Building a strategy for data governance

A data governance strategy provides a high-level roadmap that outlines the objectives and direction for data governance initiatives within an organization. It guides decision-making and resource allocation – identifying, prioritizing, and aligning business objectives across the organization.

To establish a sound data governance strategy, introduce the following actions:

  • Define clear data ownership roles and responsibilities within the organization, assigning individuals or teams to manage specific sets of data.
  • Ensure data governance practices align with relevant data protection laws and regulations, such as GDPR, HIPAA, or other industry-specific standards.
  • Establish comprehensive data policies and standards that outline how data should be collected, stored, processed, and shared.
  • Implement robust security measures like encryption, access controls, and regular security audits to protect data from unauthorized access and breaches.
  • Integrate data from various sources using tools and platforms to ensure consistency and accuracy, creating a unified view.

Frameworks for data governance

There are several data governance frameworks that can be used, each providing a structured approach to managing and governing data within an organization. Some popular frameworks include the Data Management Body of Knowledge (DAMA-DMBOK), the Control Objectives for Information and Related Technologies (COBIT), the Zachman Framework, and the Data Management Capability Assessment Model (DCAM).

These frameworks provide guidelines, best practices, and methodologies to establish and implement effective data governance strategies. Organizations can select the data governance framework that best aligns with their specific needs, objectives, and industry requirements. It’s essential to tailor the framework to the unique characteristics and priorities of the organization to ensure the successful implementation of data governance initiatives.

Implementing data governance

Data governance implementation involves a series of steps and considerations to ensure that data is managed effectively, securely, and in compliance with regulations.

A successful implementation will typically involve the following actions:

  • Choose a suitable data framework that aligns with your organizational goals and specify roles, responsibilities, and decision-making processes within the framework.
  • Clearly define the goals and objectives of your data governance initiative, whether it’s to improve data quality, ensure compliance, or enable better decision-making.
  • Identify and classify all data assets based on sensitivity, importance, and regulatory requirements. Know where it resides and who owns it.
  • Develop and communicate clear and comprehensive data policies and standards covering quality, security, privacy, retention, and other relevant aspects.
  • Establish metrics and key performance indicators to measure and track quality improvement. 
  • Regularly gather feedback, analyze performance metrics, and make necessary adjustments to improve data governance processes continually.

Creating a policy for data governance

A data governance policy is a formal document that outlines the guidelines, rules, and procedures governing the management, protection, and usage of data within an organization. There are several common policies for data governance – ranging from data classification and privacy to data security and retention policies – to help ensure consistent and effective data governance practices. They provide a framework to guide decision-making, establish accountability, maintain data quality and security, and ensure compliance with applicable laws and regulations.

A well-defined data governance policy is essential for establishing a consistent and reliable approach to managing data assets, promoting data integrity, and building trust within the organization and with external stakeholders. It’s important to communicate and enforce these policies across the organization to promote a culture of responsible data management.

The benefits of robust data governance

Organizations with good data governance services benefit from more comprehensive support to leaders, workers, and automated systems, empowering their decision-making. It positively impacts efficiency, customer relations, risk management, and competitiveness.

Here’s a quick summary of the main benefits of data governance:

  • Enables true data democratization
  • Builds effective collaboration between teams
  • Protects data integrity and relevance
  • Helps comply with data protection laws and regulations
  • Provides a foundation for digital transformation initiatives
  • Allows organizations to respond more effectively to market changes and customer needs
  • Reduces costs associated with errors, redundant storage, and inefficient data handling

Recent advances in generative AI will further increase the popularity and importance of well-governed data and its advantages, helping businesses to improve and grow in the new economy.

The return on investment from data governance

In the aftermath of the coronavirus pandemic, the demand for data governance is increasing, happening at a time when resources are scarce. This underscores the crucial need for stakeholders to see clear data governance ROI. Each time you invest in governance, you’re essentially making a trade-off: allocating resources to prevent failures proactively and establishing checks to detect potential issues early on, thereby recovering time, effort, and, significantly, the labor costs associated with resolving issues.

Simultaneously, you’re reducing the uncertainty regarding the occurrence of potential failures, reducing the risk of yet undiscovered failures, and expanding the opportunities that naturally arise where users and stakeholders can benefit from smoothly operating systems. While quantifying the exact ROI of data governance can be challenging, the overall impact on informed decision-making, risk reduction, cost optimization, efficiency gains, data monetization, customer satisfaction, and competitive advantage makes data governance a valuable investment.

Understanding the challenges of data governance

With data collection and volume surging, developing a data strategy as soon as possible is more important than ever. Yet, it’s common for enterprises to eventually run into some data governance challenges.

Here’s a brief rundown of the top challenges you may face:

  • Limited resources: To avoid this obstacle, include data governance planning in your resource allocation each year or quarter, and leverage automation in your business processes to optimize data utilization and reduce employee burden.
  • Siloed data and fragmentation: As your organization gathers more and more data, be careful that it doesn’t become too siloed, as you won’t be able to draw meaningful conclusions.
  • No data leadership: Not all leaders are data literate, so set up a data governance team with a knowledgeable leader to articulate the need for data governance and keep top decision-makers informed.  
  • Lack of data control: This may result in non-compliance, so ensure you have the capability to monitor and control how data is used.
  • Poor data quality: Inaccurate, inconsistent, or incomplete data can compromise decision-making and undermine the achievement of data governance objectives.

Industry use cases for data governance

Data governance is not a one-size-fits-all concept – it needs to be tailored to the specific needs, regulations, and characteristics of the industry or product it serves.

Here are some examples of the various types of data governance:

  • Healthcare data governance: This often includes privacy policies to protect patient information, data standardization for interoperability between different health systems, and quality assurance.
  • Financial services, including finance data governance, banking data governance, and insurance data governance: In this sector, data governance includes policies and procedures to ensure data accuracy and completeness, especially for financial reporting and compliance with regulations like the Sarbanes-Oxley Act in the U.S. or GDPR in the EU.
  • Customer data governance: This includes tools for collecting and analyzing customer master data, ethical data acquisition frameworks, and security measures for storing and accessing this data.
  • HR data governance: The process of ensuring that your HR and employee master data is accurate, consistent, and compliant with legal and ethical standards, which is crucial for HR analytics.
  • Product data governance: An ongoing process that makes your product data clean, consistent, and coherent, a crucial process for any business that wants to succeed and grow in today’s digital age.
  • Reference data governance: Reference data carries significance by defining acceptable values, ensuring uniformity, and aligning internal data with external data or standards. Despite representing a relatively small portion of total data volume, reference data is essential to master data management.

Doing data governance in the cloud

Today’s organizations are rapidly embracing the cloud. Cloud data governance offers several benefits, including centralization of data, improved quality control, and availability across various platforms and devices. Cloud data governance solutions often come with built-in analytics tools and security measures, giving businesses a great advantage.

However, cloud data governance also brings unique challenges, especially with data security and privacy regulations. These can potentially vary across different regions and jurisdictions, making compliance more complex. Nonetheless, data governance can reside within cloud environments successfully with the correct planning, strategy, and choice of reliable cloud service provider.

What is the difference between data governance and …

  • Data analytics: Data governance and data analytics are two different aspects of data management. Data governance refers to the overall management of the availability, usability, integrity, and security of data used in an enterprise. Conversely, data analytics is the practice of analyzing raw data to extract valuable insights. It involves applying an algorithmic or mechanical process to derive insights, helping identify patterns, correlations, trends, and anomalies in datasets to predict outcomes.
  • Data management: The difference between data governance and data management is that data management is more tactical, with a sharper focus on the execution of the policies set by data governance. In other words, it’s the operational activities that carry out and enforce the strategies established by data governance to handle an enterprise’s data needs.
  • Data quality: The difference between data governance and data quality is that data quality aims to ensure that data is accurate, reliable, and fit for its intended purpose. While data governance sets up the framework and processes for managing data assets, data quality focuses on improving the actual data quality within that framework. Both are vital aspects of effective master data management.
  • Data security: Data governance security refers to the protection of data from unauthorized access, alteration, disclosure, and breaches throughout its entire lifecycle. This involves implementing measures, controls, and safeguards to ensure data confidentiality, integrity, and availability, focusing on aspects such as data encryption, authentication mechanisms, network security, backup and recovery, and more. 

Measuring data governance success

Establishing proper data governance metrics based on your business goals allows you to measure progress and adjust your data governance strategy as necessary to maximize success.

Here are five useful areas to look at when assessing the effectiveness of your data governance efforts:

  1. Data quality: These metrics evaluate the accuracy, completeness, consistency, and reliability of data.
  1. Data compliance: These metrics assess an organization’s adherence to data-related regulations and policies and may also include the number and severity of data breaches.
  1. Data governance adoption: As the name implies, these metrics measure the level of adoption and awareness of data governance, tracking aspects such as the participation rate or engagement of data stewards and owners.
  1. Data stewardship: These metrics evaluate the effectiveness of data stewardship activities, such as the number of data issues resolved, the response time for data issue resolution, or the overall satisfaction rating of data stewards.
  1. Data management: As the title indicates, these metrics assess the organization’s ability to manage data assets effectively, such as the percentage of data classified and tagged, or the success rate of data migration or integration projects. 

Tools to help with data governance

Organizations can use specialized tools for data governance to manage data quickly and effectively from many different access or storage points while meeting the needs of various end users.

Some commonly used tools include:

  • Data governance platforms: Data governance software provides a centralized system for managing all aspects of data governance, offering features like workflow automation, collaboration tools, and data governance dashboards.
  • Data catalogs: These tools help organizations manage and control their data assets by providing a centralized inventory of available data and associated metadata.
  • Metadata management tools: These tools help maintain data definitions, data lineage, and data documentation, which are crucial for effective data governance.
  • Data quality tools: These tools assist in ensuring data accuracy, completeness, and consistency by profiling, validating, and monitoring data quality
  • Data privacy and security tools: These tools offer features such as data masking, encryption, access control, and monitoring capabilities, helping organizations protect sensitive data and ensure compliance with data privacy regulations.
  • Data visualization tools: These tools help in presenting data governance metrics, reports, and dashboards in a visually appealing and easily interpretable manner. 

Data governance in action

Data governance ensures that the right people are assigned the right data responsibilities. Here are some data governance examples and the associated activities:

  • Master data management: This involves creating and enforcing data standards, defining data ownership and stewardship responsibilities, and implementing data synchronization and integration processes. For example, a data governance initiative may establish a master data management framework to ensure customer or product information remains consistent and reliable across various systems.
  • Metadata management: This entails capturing, storing, and managing metadata for data assets, including data definitions, data sources, and relationships. For example, a data governance program may utilize a metadata management system to establish a centralized repository, allowing users to discover and comprehend available data assets.
  • Data lineage and compliance: Data governance enables organizations to establish data lineage, which shows the origin and movement of data from its source to its end uses. For instance, data governance can help track data transformations and prove compliance with regulations like GDPR or HIPAA by documenting the processes and systems involved in data processing.

What does a data governance team look like?

A robust data governance team comprises specific data governance roles to oversee and implement data governance initiatives. These roles can vary depending on the organization and its particular data governance requirements.

Some organizations may combine or expand these roles, while others may have additional functions.

Here are some typical roles you may find in a data governance team:

  • Data governance manager or leader
  • Data owners
  • IT or data architects
  • Compliance and privacy officers
  • Data quality analysts
  • Metadata specialists
  • Data stewards

Data stewardship is vital within a data governance team, involving day-to-day data management, protection, and quality assurance. Overall, they act as guardians of data assets, ensuring their accuracy, integrity, and compliance. Data stewards play a crucial role in executing data governance initiatives and promoting a data-driven culture within the organization.

How data governance is regulated

Data governance is regulated through a combination of legal frameworks, industry standards, and organizational policies. Various laws and regulations govern how organizations manage and protect data. For example, the General Data Protection Regulation (GDPR) in Europe and the Health Insurance Portability and Accountability Act (HIPAA) in the United States impose specific requirements on handling personal and healthcare data, respectively.

Different industries may have specific data governance regulations that dictate how data should be managed within that sector. For instance, financial services, healthcare, and telecommunications often have their own set of regulations that organizations must adhere to in their data governance practices. Organizations also establish their own internal policies and standards to govern data. These can include guidelines on data quality, security measures, access controls, and other aspects of data governance.

International standards organizations, such as the International Organization for Standardization (ISO), develop and publish standards related to data governance. 

The risks of data governance – and how they can be managed

There are several risks associated with data governance, and organizations need to manage them effectively to ensure the success of their data governance initiatives. Here are some proactive strategies for data governance risk management:

  • Establish clear data governance policies and procedures by articulating the roles, responsibilities, and expectations for all individuals involved in handling and managing data.  
  •  Classify data based on its sensitivity and importance and implement access controls and encryption, ensuring only authorized personnel can access and modify critical information.
  • Conduct regular training sessions to educate employees about data governance policies, security measures, and best practices.
  • Stay abreast of data protection regulations relevant to your industry and location and ensure that your data governance practices comply with legal requirements.
  • Implement continuous monitoring mechanisms to detect and identify potential vulnerabilities and threats to data governance.
  • Implement processes for data validation and cleansing to minimize errors and inconsistencies that may pose risks to data governance.
  • Regularly back up critical data and establish reliable recovery procedures to mitigate the risk of data loss due to accidental deletion, corruption, or other unforeseen events.
  • Develop a robust incident response plan outlining steps to be taken in the event of a data breach or security incident.

Overall, data governance risk management requires a holistic approach involving people, processes, and technology. It requires a commitment from organizational leadership, clear policies and procedures, effective communication and training, and continuous monitoring and evaluation to ensure the success of data governance initiatives.

Future trends in data governance

Good data governance is the invisible backbone that sustains the integrity, quality, and usability of an organization’s data assets, turning them into valuable tools for decision-making, innovation, and growth.

Here are 10 trends that will drive the future of data governance:

  1. Increased adoption of AI and machine learning in data management.  
  1. Evolution of data quality management through activities such as data profiling, data cleansing, data validation, and ongoing monitoring.
  1. Improvements in data lineage and metadata management to provide a historical record of data and descriptive information about data, including its structure, content, and context.
  1. Increased integration of cloud-based data governance solutions.
  1. Use of decentralized data governance models to distribute data governance responsibilities across different departments, business units, or individuals within an organization to empower stakeholders to take ownership. 
  1. Real-time data governance which applies automated processes, controls, and monitoring mechanisms in real time to ensure that data is always accurate, consistent, and compliant with organizational policies and regulatory requirements.
  1. Data as a Service (DaaS) is a data management model where data is provided to users on-demand via a third-party service provider, usually through APIs or cloud-based platforms.
  1. Increased focus on data ethics to ensure data practices are aligned with ethical principles, legal requirements, and social norms. 
  1. Emergence of data lakes and data catalogs where data lakes are centralized repositories that store raw, unstructured, and structured data from various sources and catalogs are organized and searchable repositories that provide metadata information about available data assets.
  1. Emergence of data democratization, which involves removing barriers and empowering a broader range of users to access and leverage data for decision-making and insights, without the need for specialized technical skills.

By embracing these trends, organizations can strengthen their data management capabilities, comply with regulatory requirements, and leverage data as a strategic asset to drive business success.

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