Data Governance. How is it different from Data Management?

Data Management is crucial for any organization that deals with data. But what is Data Governance? How is it different from Data Management? Many articles using these terms interchangeably but they have distinct differences. Let's try to understand more about them below.

Data Management focuses on technical aspects of data life cycle such as data ingestion, integration, transformations, processing, persistence (such as backup, archiving etc.). In contrast, data governance is about defining organizational policies, frameworks and tools to ensure that data-related requirements are aligned with the business strategy. This includes data accuracy, consistency, compliance with regulations, and internal organizational policies as well as data quality, security, privacy, auditing and risk management. Furthermore, data governance involves defining data ownership, roles and responsibilities, and enforcing policies and procedures throughout the organization. Data governance plays a significant role in leveraging data as a strategic asset while data management deals with operational aspect of delivering on that strategy.

Key Elements of Data Governance

Data Cataloging & Discovery

Data Cataloging is a centralized metadata repository for an organization’s data assets. Data Cataloging is the result of scanning & indexing of organizations structured and unstructured data repositories including metadata, location, user access details etc. It will allow organization members to discover, understand the data hence helps in enhance collaboration, reduce redundancy.

Data Quality

Data Quality is the evaluation of key data quality attributes such as

Improved data quality helps in improving business decisions and resource allocation.

Data Classification

Data Classification involves organizing and categorizing data based on its sensitivity, value and criticality. Data Classification helps in reduces risks and protection at scale.

Data security

Data Security include access controls that define which groups or individuals can access what data. These controls can be highly specific, down to the individual record or file. As data breaches and regulations such as GDPR and CCPA pose increased risks, businesses must establish clear governance policies that define who can access sensitive data sets and how to track any misuse. Unauthorized access to private or sensitive information should not occur, and implementing effective access management strategies is essential to safeguard data and maintain customer trust.

Data Lineage

Data lineage captures relevant metadata and events throughout the data’s lifecycle, providing an end-to-end view of how data flows across an organization’s data estate. It helps data teams perform root cause analysis of any errors, significantly reducing debugging time.

Data Sharing and Collaboration

Data exchanging is unavoidable between internal data teams, external partners and customers across multiple clouds, data platforms and regions. it is critical for organizations to securely exchange data while maintaining control and visibility over how their sensitive information is used.

Auditing data entitlements and access

By understanding who has access to what data and tracking recent access, organizations can proactively identify overentitled users or groups and adjust their access accordingly, minimizing the risk of data misuse. Without proper audit mechanisms in place, an organization may not be fully aware of their risk surface area, leaving them vulnerable to data breaches and regulatory noncompliance.

Data Governance Team:

• Data Governance Sponsor: This role is typically filled by an executive, such as a Chief Data Officer (CDO) or Chief Information Officer (CIO). Their main responsibility is to champion and advocate for the data governance initiative at the highest level of the organization. They provide the necessary support, resources, and authority to ensure the success of the program. The Data Governance Sponsor is usually business-focused but may also have technical knowledge.
• Data Governance Leader: This role is responsible for steering the overall data governance program to success. They provide leadership, strategic direction, and guidance for the initiative. The Data Governance Leader ensures that the program aligns with the organizational goals and objectives, and they coordinate and collaborate with various stakeholders to drive the implementation of data governance practices. This role is typically business-focused but may also require a good understanding of technical aspects.
• Data Owners: These individuals are responsible for the management and oversight of specific data domains or data sets within the organization. They have accountability for the quality, integrity, and availability of the data within their domain. Data Owners define data policies, standards, and procedures, and they work closely with Data Stewards to ensure compliance and adherence to these guidelines. Data Owners are usually business-focused, as they have a deep understanding of the data and its business context.
• Data Stewards: These individuals are responsible for executing the day-to-day data governance activities. They ensure that data is properly classified, documented, and protected according to the defined policies and standards. Data Stewards work closely with Data Owners, Data Users, and other stakeholders to resolve data issues, address data quality problems and support data-related initiatives. Data Stewards need to have a good understanding of both business and technical aspects to effectively carry out their responsibilities.

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