Data is the lifeblood of modern organizations. Failing to effectively harness the power of data to make informed decisions and drive business growth bodes poorly for an enterprise’s future. To stay on top of all this powerful data are two fundamental concepts that play a pivotal role: data management and data governance. While often used interchangeably, these terms represent distinct aspects of data handling within an organization. This blog aims to clarify the differences between data management and data governance while highlighting the crucial role of data lineage in effective data governance strategies.
What Is Data Management
Data management refers to the comprehensive processes, practices, and strategies involved in acquiring, storing, organizing, processing, and delivering data throughout its lifecycle. It focuses on the technical and operational aspects of handling data efficiently and accurately.
The objective of data management is to improve the overall operational efficiency of how data is handled in an organization. Enhancing accessibility, streamlining workflows related to data, and ensuring accuracy as well as consistency are some of the focuses of effective data management.
Activities related to data management include:
- Data storage and retrieval
- Data integration and transformation
- Data quality and cleansing
- Master data management
- Data architecture and modeling
What Is Data Governance
Data governance is the strategic framework and processes that ensure data is managed, controlled, and utilized effectively to meet business objectives. This involves policies, procedures, roles, and responsibilities for data-related decisions.
Activities related to data governance include:
- Formulation and implementation of data policies and standards
- Frameworks for data stewardship and ownership
- Data compliance and security
- Data strategy and alignment with business goals
The objectives of data governance are to ensure data quality and reliability, establish data ownership and accountability, align data usage with regulatory requirements, and facilitate better decision-making based on accurate data.
Fundamental Differences: Data Management vs. Data Governance.
It can be challenging to pull apart data management and governance. The two are unavoidably intertwined, but the key differences are in their objectives and scope.
Where data management is the overarching, operational handling of data, data governance could be said to be a specific subset with a strong focus on security, compliance, and stewardship towards business goals or reducing organizational risk.
Key Principles of Data Governance
Exactly how many principles of data governance there are varies depending on who you ask. According to the Data Governance Institute, there are eight principles at the center of effective data governance and stewardship programs. Onetrust proposes a simplified set of 4 top data governance principles to base your foundation on. However you choose to slice and dice your approach to data governance, these are the core concepts that should be kept in mind.
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Data Ownership and Accountability: Assigning clear ownership and accountability for data within an organization. This ensures that individuals or teams are responsible for managing and maintaining data quality, security, and compliance.
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Data Quality and Integrity: Focusing on maintaining high data quality and integrity standards. This involves processes for data validation, cleansing, and ensuring that data is accurate, consistent, and reliable for decision-making.
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Data Privacy and Security: Implementing robust data privacy and security measures to protect sensitive information. This includes defining access controls, encryption, and compliance with relevant data protection regulations such as GDPR or HIPAA, depending on the industry and jurisdiction.
The Role of Data Lineage in Effective Data Governance
Data governance is what comes up most often in the field of risk management and cybersecurity. It is a vast domain with numerous tools and tactics that organizations can choose to leverage toward the ultimate goal of ensuring sustainable, continuous business execution.
One part of the data governance and risk reduction puzzle is getting a clear overview of how data flows, is transformed, and where and by whom in the enterprise. This is where Data Lineage comes in. Data Lineage leverages Enterprise Architecture techniques to represent how data moves through an organization.
Why is Data Lineage Important?
Organizations cannot effectively manage risks they don’t know about. Data Lineage is one way to make the unknown known, providing insight into where data originates, is modified, and ultimately ends up.
With Data Lineage, organizations can:
Identify High-Risk Areas - See where data inconsistencies, inaccuracies, and potential bottlenecks can occur and understand where critical data is used in the business.
Map and Track Data Owners, Users, and Locations - Understand where data originates, is transformed, and will end up in the course of usual business.
Ease Due Diligence for Compliance - Know where the most critical data is stored, transformed, and used allows data governance teams the visibility and ability to continuously track changes and ensure compliance, minimizing the risk of costly penalties.
How Data Lineage Aids Data Governance
While Data Lineage leverages Enterprise Architect know-how and techniques, the benefits actually go deeper with risk, security, and governance teams within the organization. The level of accountability, transparency, and traceability that Data Lineage offers allows the organization to:
- Ensure Data Quality:
Data Lineage identifies issues at each stage of data processing, highlighting who the teams are and what they need to rectify to ensure the accuracy and quality of data. This also creates a common understanding of data processes and improves cross-organizational collaboration to achieve good data practices and compliance.
- Speed Up Time to Audit:
Data Lineage supports regulatory compliance by showing how data is handled, stored, and processed, making audits more transparent. When conducted with a dynamic, data-driven Enterprise Architecture tool, stakeholders and auditors can quickly get this overview with a few clicks of a button. This is in stark contrast to typical, manual approaches that rely on out-of-date models or documentation, providing more reliable, up-to-date insights on demand.
- Conduct More Comprehensive Impact Analysis:
With the insights from Data Lineage, organizations can better assess the potential impact of changes or updates to systems and processes on data. This informs more holistic strategies on data management and governance, enhanced with insight into data ownership and accountability down to the individual or team level.
The Step in the Path to Data-Driven Success
Data management and data governance are both critical to the operational and ongoing success of an organization's data-driven endeavors. By recognizing their distinctions and embracing the insights provided by data lineage, businesses can establish a solid foundation for efficient data handling, improved decision-making, and regulatory compliance. In the ever-evolving data landscape, a well-rounded approach that encompasses both management and governance is essential for achieving lasting success. It is a vital part of data governance, empowering risk teams with the overview they need to quickly take action and plug security gaps.
Quick Takeaways on Data Management vs. Data Governance:
- Data management and data governance have substantial overlap, but data governance focuses more on security, stewardship, and compliance in an enterprise.
- Data management has a broader scope of all things related to data in an organization on an operational level.
- Keeping in mind key principles of data governance ensures an organization has covered the critical bases needed to ensure strong governance systems and processes are in place.
- Data Lineage leverages Enterprise Architecture techniques to allow enterprises to better understand where their data is being created, used, stored, and transformed at a more granular level.
- The insights from Data Lineage improve traceability and transparency, aiding risk management and security processes, and expediting compliance.
- Maintaining good control over the enterprise’s data is vital for ensuring sustainable, continuous business execution.
Learn more about how a dynamic data-driven EA tool like Ardoq can help with other areas of cybersecurity, such as risk assessment and risk mitigation.
