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Home » Articles » Why data governance matters: Ensuring data quality and compliance

Why data governance matters: Ensuring data quality and compliance

Data has become the lifeblood of organizations, especially nowadays when the world is more interconnected than ever.

Because of this, ensuring data quality and compliance has become very critical. Data governance plays a pivotal role in this regard.

Mordor Intelligence estimates data governance’s market size values at USD $ 2.37 billion in 2023, and by 2028, its CAGR will grow to 19.72%.

This article explores the importance of data governance, its benefits, how it’s used, and the framework for implementing it effectively.

Defining data governance

Data governance refers to the overall management, policies, and processes surrounding data within an organization.

It involves establishing clear rules, roles, and responsibilities to ensure that throughout the data’s lifecycle, it’s:

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Data governance aims to enhance data quality, usability, and trustworthiness.

Data governance vs. Data management

While data governance and data management are closely related, they serve different purposes.

Data management focuses on the technical aspects of handling and storing information, including:

  • Data integration
  • Database administration
  • Data infrastructure

On the other hand, data governance focuses on the strategic and organizational aspects. It covers data policies, standards, and decision-making processes.

Data governance vs. Master data management

Master data management (MDM) are practices and technologies that manage master data across systems and applications within an enterprise.

Master data refers to critical business elements shared and used across various departments, systems, and processes. Examples of it include:

  • Customer data
  • Product information
  • Supplier details
  • Employee records
  • Financial reports

MDM focuses on ensuring data accuracy and consistency within specific domains. Meanwhile, data governance covers a broader scope by addressing all informational domains within a company.

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Data governance vs. Data stewardship

Data stewardship involves the operational implementation of data governance practices.

Data stewards are responsible for managing and governance specific data domains. They ensure data policy adherence, resolve quality issues, and act as custodians of information within their designated areas.

Defining data governance

What is data governance used for?

Data governance serves various purposes. Here are a few of its key use cases:

Data stewardship

Data governance comprises the operational implementation of its practices through data stewardship.

It involves establishing clear roles and responsibilities for data stewards. Data governance lets data stewards ensure information is accurate, consistent, accessible, and secure.

With effective data stewardship, data governance enhances the quality and value of information.

Data quality

Data governance improves data quality by establishing standards, validation rules, and quality metrics.

Governing data ensures its accuracy, completeness, consistency, and integrity. This means good data quality leads to better decision-making.

Data management

Data governance and data management work closely together. Governance of information ensures the management process aligns with organizational policies and standards. It establishes guidelines for various data management activities.

Data protection and privacy

Data governance is crucial in protecting and ensuring the privacy of sensitive information. It helps comply with GDPR, HIPAA, CCPA, GLBA, and LGPD regulations.

This is done through implementing appropriate security controls, access management policies, and privacy measures.

Data governance also minimizes the risk of data breaches, unauthorized access, and data loss. It safeguards the confidentiality and integrity of information.

Cloud data governance

Firms must extend their data governance practices to the cloud, especially with the increasing adoption of cloud technologies.

Cloud data governance focuses on governing information stored and processed in cloud environments. It ensures data privacy, security, and compliance with regulations. Even when information is stored in third-party cloud providers.

Benefits of data governance

Implementing effective data governance brings several advantages to businesses:

  • Improves data quality. Data governance ensures consistent standards, validation rules, and data cleansing processes. This results in improved data quality and reliability.
  • Better decision-making. By providing accurate and trustworthy data, its governance enables informed decision-making, leading to better business outcomes.
  • More efficient work. Well-governed data eliminates duplication, reduces redundancies, and improves availability. This makes organizational processes more efficient.
  • Compliance and risk management. Data governance helps meet regulatory requirements, mitigate breaches, and manage related risks.
  • Enhanced collaboration and insights. The process breaks down data silos and encourages cross-functional collaboration. It enables effective information sharing, integration, and interpretation. This leads to better insights, decision-making, and alignment across the organization.
  • Accountability and ownership. Data governance fosters a culture of data accountability and ownership within an organization. This ensures information is proactively managed, monitored, and continuously improved throughout its lifecycle.
Benefits of data governance

Data governance framework

Effective data governance follows a structured framework together with these key elements:

  • Data governance policies. Establish clear and concise policies outlining data management goals, principles, and responsibilities.
  • Data governance committee. Form a cross-functional committee comprising executives, data stewards, IT professionals, and business representatives. They should oversee data governance initiatives.
  • Data management processes. Define and implement data management processes. This includes classification, quality management, metadata management, and retention policies.
  • Data stewardship roles. Appoint data stewards responsible for governance activities. They should enact quality monitoring, resolve issues, and enforce policies.
  • Data governance tools. Utilize data governance tools and technologies to automate data management processes. They help provide visibility into related activities.
  • Continuous monitoring and improvement. Regularly monitor data governance initiatives. Assess their effectiveness and improve based on feedback and evolving business objectives.

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