Data Management vs. Data Governance: What’s the difference?

For many companies, data is part of their critical path to success. So the effective handling of data becomes even more important for organizations seeking to thrive in an increasingly competitive landscape. Data, despite its role as an invaluable asset, can quickly become an unwieldy liability. So it’s no surprise that businesses, from small startups to global enterprises, are dedicating substantial resources to harness its potential.

Two crucial components of this data-centric journey are data management and data governance, each playing distinct yet interrelated roles in the pursuit of data excellence.

The explosion of data over the past few decades has reshaped industries across the globe. Consider this: the International Data Corporation (IDC) predicts that the global datasphere will reach a staggering 175 zettabytes by 2025. To put that into perspective, a single zettabyte is equivalent to a trillion gigabytes. With such exponential growth, the value of data in decision-making, innovation, and customer engagement should become the de facto standard. However, this abundance is also data’s greatest challenge. As the world’s data grows, so does your company’s data, presenting challenges in terms of data quality, security, and compliance.

Enter data management. This is a process of organizing, storing, retrieving, and processing data to ensure its availability, reliability, and usability. Historically, data management can be traced back to the era of punch cards and magnetic tapes, where time was tracked manually and people were heavily involved in ensuring data quality. It has since evolved through the phases of database management systems and enterprise data warehouses. Today, it encompasses a wide spectrum of technologies and practices, including data integration, data architecture, data modeling, and more. The primary goal of data management is to enable organizations to efficiently store and retrieve data for their operational and analytical needs.

In contrast, data governance focuses on the strategic aspects of data, encompassing policies, procedures, and frameworks that define how data should be collected, managed, protected, and utilized. This discipline gained prominence in response to growing concerns about data privacy, regulatory compliance, and data quality. As organizations accumulate vast volumes of data, the need to establish clear guidelines for data stewardship has become imperative. Data governance is the guardian of data, ensuring its accuracy, security, and ethical usage. It is not only a response to the data challenges of the digital age but a proactive step towards responsible data management.

In the following sections, we will dive into the specifics of data management and data governance, highlighting their key differences and how they complement each other in the pursuit of data-driven success. Understanding these concepts is essential for data analysts, IT professionals, and anyone involved in the data ecosystem, as they underpin the foundation of a well-structured, compliant, and valuable data landscape.

Data Management vs. Data Governance

While data management and data governance serve distinct purposes, they are closely intertwined. Data governance provides the guiding principles and rules for data management to follow. In other words, data governance sets the standards for how data should be handled, and data management implements these standards in practice. This collaboration ensures that data is not only efficiently managed but also aligns with the strategic goals and compliance requirements of the organization.

What is data management?

Data management is the comprehensive set of practices, processes, and technologies employed to effectively handle an organization’s data assets. It encompasses the entire data lifecycle, from data creation and ingestion to storage, processing, and distribution. Data management aims to ensure that data is accurate, available, and secure, while also optimizing its use for operational and analytical purposes. It involves tasks such as data integration, data architecture design, database administration, data quality control, and data warehousing.

For organizations wondering if setting up a structured data management process is worth it, consider the following benefits for businesses who invest in proper data management practices:

• Data-Driven Decision-Making: Effective data management ensures that accurate and timely data is available for decision-makers. When data is well-managed, businesses can make informed decisions based on reliable information, leading to better strategic choices and improved performance.
• Operational Efficiency: Well-managed data streamlines business processes from sales organizations and marketing operations to enhanced customer insights. It reduces the time and effort required to access and analyze data, enhancing operational efficiency. This leads to cost savings and improved productivity.
• Compliance and Risk Mitigation: Data management ensures that sensitive data is protected and complies with relevant regulations, such as GDPR or HIPAA. Proper data management reduces the risk of data breaches and potential legal consequences.
• Quality Assurance: Effective data management practices result in higher data quality, reducing errors and inconsistencies in business processes. This enhances the reliability and trustworthiness of data used throughout the organization.
• Resource Allocation: Efficient data management helps businesses allocate resources more effectively. It ensures that data is readily available to the teams and departments that need it, avoiding redundancy and waste.
• Business Continuity: Data management strategies can include data backup and disaster recovery plans, safeguarding data against loss due to unforeseen events. This ensures business continuity even in the face of disasters.
• Strategic Planning: Data management is crucial for long-term strategic planning. It provides the historical data and insights necessary to set future goals and make informed choices regarding business growth and expansion.

What is data governance?

Data governance is the strategic management of data as a valuable organizational asset. It is a framework that defines the policies, standards, roles, and responsibilities related to data. Data governance ensures data compliance with regulatory requirements, maintains data quality, and enforces data security and privacy. It aims to establish a structure for decision-making and accountability regarding data and often involves the creation of data stewardship committees and the development of data policies and procedures.

Sometimes data governance can be confused as a blocker or something that adds layers of difficulty to accessing the data. But that is the opposite of true. Data governance actually frees up data so business users can access data when and where needed, confident it’s accurate and applicable. Here are some additional benefits for businesses implementing data governance:

Businesses should care about data governance for several important reasons:

• Data Security: Data governance establishes data security protocols and policies, protecting sensitive information from unauthorized access or breaches. This safeguards both the business’s reputation and the trust of its customers.
  Accountability and Responsibility: It assigns clear roles and responsibilities for data management within the organization. This ensures that individuals are held accountable for data quality and security, reducing ambiguity and enhancing data-related decision-making.
• Data Accessibility: It ensures that the right people have access to the right data. This prevents data silos and promotes collaboration by providing employees with the information they need to perform their roles effectively.
• Risk Mitigation: By identifying and mitigating data-related risks, data governance helps the business avoid costly errors, compliance violations, and reputational damage that can result from poor data management.
• Improved Collaboration: Data governance promotes collaboration and communication between different departments and teams within the organization. When everyone follows the same data governance principles, it fosters a shared understanding of data and its importance.

The Differences and Similarities of Data Management and Data Governances

Difference between data governance and data management

Data governance and data management stand as the dual pillars of efficient data handling within any organization, playing vital but distinct roles in the management and utilization of data assets.

At the core of data governance lies the strategic oversight and rule-making that set the stage for responsible data use. Data governance is the architect of the data landscape, defining the rules, policies, and standards that data must adhere to. It ensures data compliance with regulatory requirements, upholds data quality, and champions data security and privacy. This element is all about the “what” and “why” of data management – it establishes the guidelines and standards that govern how data should be used, protected, and handled. Through data governance, organizations create a structured framework for decision-making, instilling a sense of accountability when it comes to data usage and stewardship.

On the flip side, data management takes a more practical and hands-on approach. It encompasses the nuts and bolts of data operations, handling the day-to-day tasks required to make data accessible, reliable, and usable for operational and analytical purposes. Data management is the implementer, responsible for transforming data governance’s strategic vision into actionable processes. It focuses on the “how” of data management, optimizing data infrastructure, ensuring efficient data integration, maintaining data quality, and overseeing the practical implementation of data-related processes and technologies.

How data management and data governance work together

These two components, while inherently different in their focus, are inextricably linked within your data. They form a symbiotic relationship where data governance provides the overarching vision and data management executes it. This harmonious collaboration ensures that data is not only well-managed but also aligns with the organization’s goals and compliance requirements. Together, they construct a robust and compliant data ecosystem that maximizes data’s potential while mitigating potential risks and challenges.

The synergy between data governance and data management is best described as a collaboration between an architect and a builder. Data governance designs the blueprint, setting the high-level vision and structure for responsible data management. In contrast, data management steps in as the builder who takes the design and converts it into a functional structure. Just as a building’s success relies on close cooperation between its architect and builder, data governance and data management must work hand in hand to construct a robust and compliant data ecosystem that optimizes data’s potential while mitigating potential risks and challenges. This synergy ensures that data serves as a valuable asset to organizations, rather than becoming a liability or a source of inefficiency.

Here’s a simple chart that highlights the key differences and similarities between data governance and data management:

Finding the tools for data governance and data management

Data management and data governance are pivotal components of a successful data strategy. While data governance defines the rules, policies, and standards, ensuring data’s ethical and compliant use, data management handles the practical aspects, making data accessible, reliable, and optimized for operational and analytical purposes. Together, they form a seamless partnership, setting the path for responsible and effective data handling within organizations.

As organizations seek to harness the power of their data, platforms like Domo come into play, offering robust solutions for both data management and data governance. Domo’s platform provides tools and features for data integration, storage, analysis, and visualization, addressing many data management needs. And, it was built from the ground up with data governance in mind, offering a governance framework that helps organizations establish data policies, track data lineage, and ensure compliance. By integrating data management and data governance on a unified platform, organizations can streamline their data operations, achieve greater data-driven insights, and stay ahead in today’s data-centric business landscape.

Data Management vs. Data Governance FAQs

Have additional questions about data management vs. data governance or
how to choose the best data management and data governance platform? We’re here with answers.

How data governance and data management work together?

Data governance and data management are closely related but serve different purposes. Data governance involves defining policies, roles, and responsibilities for data, ensuring data quality, and regulatory compliance. Data management, on the other hand, focuses on the technical aspects of handling data, such as storage, integration, and maintenance. They work together by having data governance provide the framework and rules for data management to implement, ensuring that data is well-managed and aligned with organizational goals.

What is the difference between master data governance and master data management?

Master data governance and master data management both deal with critical data elements in an organization, but they have distinct roles. Master data governance is about defining the rules and policies for creating, updating, and maintaining master data (e.g., customer or product data). Master data management, on the other hand, focuses on the processes and technology used to manage and maintain master data, including data consolidation, storage, and distribution.

What is the difference between data governance and data management?

Data governance is primarily concerned with defining the policies, procedures, and rules related to data, including data quality, security, and compliance. It sets the framework for how data should be handled within an organization. Data management, however, deals with the practical aspects of storing, processing, and ensuring the accessibility and usability of data. While data governance defines the “what” and “why,” data management handles the “how.”

Which comes first, data governance or data management?

In most cases, data governance should come before data management. Data governance establishes the policies and rules that guide data management activities. It provides the framework for how data should be handled, including defining data quality standards and regulatory compliance. Once data governance is in place, data management can be implemented to execute the policies and procedures defined by data governance. This sequence ensures that data is managed in a consistent and compliant manner.