Data Governance Vs. Data Management: What's the Difference?

Data Governance and Data Management are two of the most essential terminologies for an organization that deals with large volumes of data daily. Data governance is strategic planning conducted by management that sets out data usage and transfer rules. On the other hand, data management paves the way for physical access to data.

This article briefly discusses data governance vs data management and highlights how both terms are necessary for effective data utilization.

What is Data Governance?

Data Governance is a strategic principle that establishes a set of policies and roles to manage an organization’s data. It defines the core principle of how an organization can store, access, use, transfer, and delete data, ensuring consistency, availability, usability, integrity, and security across the organization.

As a subset of data management, data governance creates data harmony between different business units. It ensures that the data usage procedure complies with company policies without violating any rules.

Effective data governance requires three key elements: 

• People - Individuals from various departments, including business experts, data stewards, IT, leadership, and legal, come together to establish rules.‍
• Policies - Managers set policies according to data privacy and usage consent to perform different tasks with this data.‍
• Metrics - Metrics allow tracking technical and business aspects, such as data accuracy and sales cycle time.

Overall, data governance helps you to:

• Maintain data reliability and integrity
• Ensure compliance
• Align with business goals

What is the Importance of Data Governance?

Here’s why data governance is crucial in data-driven organizations:

• You can get the most out of the data by following data governance principles. These principles present definitions and standards for handling data, creating a shared understanding across any organization, and improving communication and collaboration.
• Data governance ensures data quality and consistency so that each individual can rely on accurate information. This leads to better data-driven decision-making based on reliable data.
• Established rules empower to streamline workflow while reducing redundancy, eventually leading to a better use of resources.

What is Data Management?

Data management is an integral part of every organization that maximizes the value of data by integrating, organizing, storing, protecting, and sharing it throughout its lifecycle. This encompasses the entire journey of data, from creation to use and deletion.‍‍

Data management empowers you to leverage big data effectively. It utilizes multiple architectures, policies, and techniques to achieve this. This encompasses aspects like data preparation, data catalogs, data warehousing, and more. 

By implementing a robust data management strategy and choosing the right data management tools, you can streamline operations, ensure data quality, reliability, security, and compliance with regulations. This enables you to make quick data-driven decisions for your organization.

What is the Importance of Data Management?

Here are a few ways in which data management can bring significant benefits:

• Data management mitigates the risk of data loss and corruption. It also ensures compliance with data privacy and security regulations, reducing legal issues.
• Organizing data with efficient management can save time and money, improve data accessibility, enhance productivity, and optimize resource usage.
• It can help organizations uncover new opportunities and make better decisions.

What are the Differences Between Data Governance Vs. Data Management?

Although data governance and data management may sound similar in some terms, they are significantly different when it comes to core values. Data governance explains the high-level strategy and demonstrates how an organization must handle data. At the same time, data management explains the day-to-day practices of handling data according to the data governance framework.

How is data governance different from data management? This table highlights the main differences between data governance vs data management.

This section mentions the significant differences between data governance and data management. Being different in multiple spaces, it becomes necessary for an organization to understand how data governance and data management can work together to produce better results.

How do Data Governance and Data Management Work Together?

Data governance and management are interdependent features that must work together to produce valuable insights for an organization. Around 88% of data scientists are considering adopting data governance solutions, and 73% of them are investing more in these solutions to improve data quality.

As we have seen above, data management focuses on the technical aspects of handling data, including its collection, storage, processing, and integration. On the other hand, data governance establishes rules and regulations for managing data. Let's discuss how data management and governance can work together to scale businesses.

‍Enhance Data Quality: Data governance and data management can work together to ensure the quality of your organization’s data. Data governance defines the standards for data accuracy and completeness. This includes establishing ownership and setting up policies for complying with data privacy regulations (e.g. CCPA and GDPR). Data management implements these practices that ensure data adheres to these standards through processes like data cleaning and validation. It also involves implementing these guidelines to ensure data collection, storage, and usage comply with the regulations defined by data governance. ‍

Streamlines Data Integration: Data governance sets out rules for efficient data integration practices, ensuring data is reliable for further analysis. Data management follows these rules and conducts assessments to avoid any inconsistencies and streamline the process of consolidating data.

By working together, data governance and data management empowers your organization to leverage high-quality, reliable data for better decision-making.

Perform Effective Data Integration with Airbyte

One of the critical tasks within data management is ensuring seamless data integration. Airbyte, a powerful and reliable data integration tool, can simplify this process. It offers a user-friendly interface and over 350 pre-built connectors, allowing you to replicate data from various sources.

Unique Features of Airbyte

• Airbyte’s Change Data Capture (CDC) allows you to replicate the changes made to the source in the destination system without copying the entire dataset.
• With Airbyte's Connector Development Kit, you can create custom connectors that are flexible to your requirements. 
• You can use Airbyte’s Python library PyAirbyte to extract data from sources supported by Airbyte within your Python environments.
• Airbyte allows you to integrate with dbt (data build tool), enabling you to perform robust data transformation techniques. This feature helps create an end-to-end data pipeline using complex SQL queries.
• It maintains compliance with renowned security benchmarks, including ISO, SOC 2, HIPAA, and GDPR, securing the confidentiality and reliability of your data.
• You can integrate Airbyte with the most prominent data stacks, such as Dagster, Prefect, and Airflow.

Conclusion

The discussions of data governance vs data management are necessary to understand how to utilize your data effectively. Data governance is essential for data management. It involves setting up policies and rules to manage data to ensure quality, availability, reliability, integrity, and security. Data management follows these rules by practically implementing them to manage data efficiently throughout its lifecycle.

Data governance and management can sometimes become complex as the data is present on a large scale. Robust solutions like Airbyte can enable you to automate parts of your data management tasks. It allows you to integrate data from multiple and load them into a destination of your choice.

Frequently Asked Questions (FAQs)

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

The critical difference between data governance and data management is that data governance enables you to set out the rules and regulations to manage the data. On the other hand, data management allows you to perform tasks in compliance with the data governance rules.

What are the three pillars of data governance?

The three pillars of data governance are: 1. Data quality ensures data accuracy, consistency, and completeness. It involves defining standards and processes to implement data quality monitoring and reporting mechanisms. 2. Data Stewardship is responsible for resolving disputes and ensuring the use of data in compliance with policies. 3. Data Security ensures the safety of sensitive data from unauthorized breaches and misuses, ensuring compliance with security standards like GDPR, HIPAA, and more.

Is GDPR a part of data governance?

Yes, GDPR can be considered part of data governance—a broader regulatory framework. General Data Protection Regulations (GDPR) focuses on data protection within the European Union.