Data Governance Vs. Data Management: Key Differences

Data, the lifeblood of any organization, drives strategic decisions and optimizes operations. However, unlocking its full potential requires an in-depth understanding of its two vital aspects: data governance and data management. Far from being just trendy industry terms, these form the heart of a solid data strategy.

Let’s explore the differences and similarities between data governance and data management in a straightforward manner so you can understand each aspect clearly to better safeguard your organization’s data assets.

Main takeaways from this article:

• Data governance and data management serve distinct yet complementary roles in an organization’s data strategy. Data governance outlines a strategic framework focusing on data quality, privacy, and compliance, setting policies and standards for data use and protection.
• Data management, on the other hand, involves the practical aspects of handling, storing, and ensuring the accessibility and reliability of data, executing the strategies laid out by data governance.
• Data governance is typically handled by data stewards who develop policies and standards for data usage and protection, while IT teams and data managers implement these policies through daily data management activities like quality checks and security. The effectiveness of data governance relies on the use of specific tools and technologies that ensure adherence to set policies.
• Data governance and data management are interdependent, with governance setting the rules and management implementing them to maintain data quality, availability, and integrity. This combination ensures that data remains accurate, secure, and accessible, supporting the organization’s goals and compliance with regulations.
• The principles of data governance and management are applied across various sectors, such as manufacturing, healthcare, financial services, and education, highlighting their universal importance. In each case, governance ensures compliance and protection of sensitive information while management optimizes data usage for operational efficiency and decision-making.
• Data quality is fundamental to both data governance and management, underpinning their effectiveness in supporting strategic decision-making and operational efficiency. High-quality data ensures confidence in decision-making, regulatory compliance, and operational efficiency, highlighting the critical role of data stewards in maintaining data standards and policies.

Data governance and data management are not the same

For leveraging data in any organization, understanding two closely interwoven aspects is essential: data governance and data management. While these terms are often used interchangeably, they have distinct roles in a company’s data strategy.

What is data governance?

Data governance is a strategic plan that focuses on upholding data quality, respecting privacy rules, and ensuring compliance. By overseeing the lifecycle of data from creation to disposal, organizations can effectively manage information as a valuable asset.

What is data management?

Data management is essentially the functional practice of collecting, handling, preserving, and effectively using data. It includes vital organizational functions such as administering data catalogs, metadata management, and operating data warehouses to ensure accessibility, reliability, and timely availability of data.

Key differences between data governance and data management

To understand data governance and data management better, it’s essential to explore their unique features and objectives.

Purpose and scope

Data governance constructs the strategic framework for how data is to be accessed, protected, and utilized across an enterprise.

On the other hand, data management is the hands-on aspect dedicated to the collection, storage, quality maintenance, and processing of data, essentially driving the practical deployment of the set governance strategies.

In simpler terms, data governance defines the data policy that guides how data should be handled, and data management, as the players on the field, implement those rules. Both are essential, distinct components that provide a comprehensive picture of an organization’s data environment.

Roles and responsibilities

In data governance, data stewards create policies to control how data is used in the organization.

On the other hand, IT teams are responsible for carrying out daily tasks related to data management, such as checking data quality and implementing data security measures.

In essence, while governance sets the rules, management acts upon them.

Data governance helps ensure the validity and reliability of data by setting up a framework of standards and processes, which helps teams across different departments in an organization to uniformly interpret the data while making decisions.

On the flip side, data management involves the storage, access, retrieval, and processing of data in compliance with preset data governance policies.

Policies and technology

Data governance establishes the rules and policies for how data can be used and kept private.

Data management enforces these rules by using specific tools and technologies, such as automated servers and encryption systems, to make sure all data follows the governance standards.

Data governance authorizes stewards and sets guidelines for data oversight, outlining procedures, resolving conflicts, and ensuring integration to enhance data consistency and quality.

Conversely, data management puts these standards into practice with tasks such as data cleaning, normalization, and backup to ensure data safety and usability.

Data quality metrics and standards

Data governance lays a robust foundation for quality standards and metrics deemed necessary for maintaining high-quality data. It involves creating specific measures for data quality and setting rules to achieve them.

Data management then implements these rules to keep the data accurate and in line with regulations. This process helps ensure that the data is reliable and follows both internal rules and external laws. This is important for making data-based decisions and achieving the organization’s goals.

Integrating data governance and management

Think of data governance and data management as two interlocking cogs in a well-oiled machine working together to ensure the smooth operation of your organization’s data. They use resources, practices, and strategic tasks to protect your organization’s database.

When they work well together, they keep your data safe, make it easy to access, and ensure it is always accurate.

Let’s explore their interdependence a little more in detail.

• Tool synchronization: Data governance tools are interdependent with data management tools. One cannot function optimally without the other. For example, a data steward (a role birthed by data governance) would need a data management software suite to comprehensively oversee and execute governance policies.
• Data protection: Data governance outlines the policies that define ‘who’ has access to ‘what’ data. These rules are then enforced through data management tools and practices, leading to comprehensive data protection.
• Data availability: The availability of data is regulated by data governance’s guidelines, which outline how data should be stored, managed, and accessed. With the help of data management practices, the data stays available and usable for those authorized to access it.
• Data integrity: Governance rules set the standards for data quality, and data management makes sure these standards are followed. Together, they help keep the data accurate and reliable, maintaining data integrity.

Practical examples of data governance and management across industries

Now, let’s approach these concepts with real-life examples from various sectors where data governance and management have been effectively deployed.

1. Manufacturing and pharma

In the manufacturing and pharmaceutical sectors, data governance plays an integral role in ensuring compliance with industry regulations like FDA guidelines and protecting intellectual property. This framework provides a safety net against regulatory traps and security risks, resulting in a safer, more compliant business environment.

On the other hand, data management boosts efficiency and sparks innovation by optimizing the use of supply chain and research data. By streamlining disparate data streams, companies are propelling faster decision-making and fostering a culture of progress and development.

2. Financial services and government

Data governance is crucial to financial services and government sectors where safeguarding sensitive information is paramount. By implementing robust governance protocols, compliance with firm financial regulations and strict privacy laws can be ensured, mitigating the risks of data breaches and financial penalties.

Concurrently, data management plays a vital supplementary role. It helps these sectors achieve operational efficiency through systematized data processing and promotes transparent reporting, thus enhancing public trust and stakeholder confidence.

3. Healthcare

In healthcare, data governance practices help establish robust privacy and security protocols to protect data such as sensitive patient information. It involves safeguarding data against theft and breaches and ensuring regulatory compliance.

Meanwhile, data management focuses on integrating patient data from varied sources like electronic medical records (EMRs), lab systems, and wearable devices. The organized and accessible data allows medical professionals to make informed decisions for improved patient care.

4. Utilities and education

In utilities like water, electricity, and gas, data governance embeds privacy and security practices to protect critical infrastructure data. Similarly, in education, sensitive student information is kept secure under vigilant data governance.

Meanwhile, data management gathers data from diverse sources like billing systems, network sensors, academic databases, and learning management systems, enhancing service delivery through informed decision-making and personalized learning experiences.

5. Retail

In the retail industry, data governance is quietly working in the background. What does it do? It keeps sensitive customer data safe and complies with laws that protect confidential information like credit card numbers.

Meanwhile, data management has a different but tangential role to play. It’s busy collecting and studying market, product, and customer data, information that helps store managers decide what products to stock, which sale strategies to use, and how to provide a personal touch for customers. This coordinated effort of data governance and data management goes a long way in making customers happy and growing the business with strategic insights.

The essential role of data quality in data governance and management

One of the defining aspects that bind data governance and data management, even as they operate differently within an organization’s data strategy, is data quality. As valuable as data is, it can offer true value only if its quality is maintained.

Let’s understand why data quality is not just a necessity but central to both governance and management and how it underpins the effectiveness of data strategies and decision-making processes.

Data quality and decision-making

The accuracy, consistency, and timeliness of your data can directly influence decision-making processes at all organizational levels. High-quality data allows for:

• Confidence: Trust in data allows stakeholders to make critical decisions without uncertainty or fear of risk.
• Precision: Accurate data promotes precise forecasts and strategies, minimizing errors and yielding more favorable outcomes.
• Efficiency: Consistent and available data reduces the time spent on data cleaning, validation, and alignment, increasing operational efficiency.

Data quality and compliance

Data governance standards heavily rely on data quality to ensure that the organization is compliant with internal and external regulatory standards. A few ways quality impacts compliance include:

• Regulatory requirements: Quality data assists in meeting strict regulatory standards by maintaining accurate records and providing evidence for compliance activities.
• Monitoring: High-quality data enables effective monitoring and auditing of data to detect and correct compliance issues promptly.

The role of data stewards

Data stewards play an essential role in maintaining data quality to align with the data governance standards. Their role is pivotal in:

• Establishing standards and policies: Data stewards help define data quality criteria, policies, and standards that align with the organization’s strategy and data governance framework.
• Maintenance and validation: They regularly review and validate data to ensure its quality, take corrective measures during data inconsistencies, and prevent future errors.

EncompaaS: A unified data strategy solution

Drowning in data? Struggling to keep it secure, organized, and accessible for smart decision-making? EncompaaS is the one-stop solution you’ve been waiting for.

Stop juggling separate data governance and management tools. EncompaaS seamlessly integrates both, providing a powerful platform to gain a unified view of your data, ensuring its accuracy, usability, and security.

The platform simplifies compliance by helping you navigate regulations with confidence, like GDPR. EncompaaS helps you stay on the right side of the law and also boosts efficiency by automating tasks and breaking down data silos, freeing your team to focus on what matters.

EncompaaS goes beyond basic data management by putting at your disposal:

• Advanced security: Iron-clad protection keeps your valuable data safe.
• Effective data governance: Enforce policies and establish a culture of data stewardship.
• Unmatched scalability: Handle diverse data sources with ease as your business grows.

The EncompaaS advantage?

• Improved data availability: Make the right information accessible to the right people at the right time.
• Streamlined business processes: Boost efficiency and reduce errors with automated processes to enhance data flow.
• Enhanced decision-making: Gain data-driven insights to make confident strategic choices.

So, stop just managing data; govern it effectively. Book a demo today and experience the EncompaaS difference.