Data Governance Vs Data Management: What’s the Difference?

Data is information such as numbers and facts that are used to analyze and contribute to decision-making. It is considered to be a precious asset for organizations today, but it can also be a dangerous asset when it is managed in the wrong way. The way of managing and governing data may lead to a huge success or massive breakdown for the organization. Data is like a child, and its future solely depends on how it is nurtured. Data Governance and Data Management act as parental figures to data. In this blog, we will discuss in detail the difference between data governance and data management, and how dataZen, a part of the smart data platform offered by ChainSys helps to leverage data to its fullest potential.

Understanding Data Governance

Data governance refers to the set of policies, procedures, and standards that guide the management of data assets. It manages the actions and processes people must follow. It also monitors the creation of data dictionaries to make sure everyone has an understanding of the data and ensures that various departments across the organization use the data in a consistent way.

Key Elements of Data Governance:

1) Data Integration: Combining data from different sources into a unified view.

2) Data Stewardship: Assigning roles and responsibilities for data oversight.

3) Data Quality Management: ensuring the reliability, consistency, and accuracy of data.

4) Compliance and Security: Ensuring data practices comply with legal and regulatory requirements.

5) Data Catalog: Providing a comprehensive inventory of data assets and their metadata.

Why is Data Governance Necessary?

Many organizations today are expanding quickly, and every day, systems perform a huge number of transactions and generate enormous volumes of new data. There is always a possibility of physically or digitally entering wrong or duplicate data, which can result in a big data failure while decision-making. With the use of dataZen for data governance we can avoid these situations because its goal is to ensure that data is accurate, complete, and secure, and also verify whether it meets the needs of the organization. dataZen takes control of the overall management of data assets within an organization by defining the rules and regulations around data access, usage, and sharing.

Understanding Data Management

Data management refers to the processes and tools that are used to acquire, store, organize, maintain, and analyze data. Data Management ensures that the data is accurate and consistent, and available for use when needed. It also ensures that an organization is using the most updated form of data available.

Key Elements of Data Management:

1) Data Integration: Combining data from different sources into a unified view.

2) Data Storage: Efficiently storing data in databases, data warehouses, or data lakes

3) Data Security: Protecting data from unauthorized access and breaches.

4) Data Archiving: Preserving data for long-term storage and future reference.

5) Data Migration: Moving data between systems, applications, or storage environments.

Why is Data Management Necessary?

To develop effective business strategies every organization completely depends on data. An organization's progress is significantly influenced by relevant, accurate, and usable data. It can become useless if not well managed. But dataZen for data management can guarantee the accuracy, availability, and accessibility of data to be processed and analyzed, therefore helping in making better-educated business decisions and gaining an in-depth understanding of customer behavior, trends, and patterns. To get the most out of the data they have access to, it has become crucial for enterprises to adopt data management. The benefits of dataZen for data management are listed below

The Relationship Between Data Governance and Data Management

To get the most useful business insights from data, data governance, and data management must be used in tandem. Without data governance, data management is like a building without an architectural plan. Data governance, on the other hand, is just paperwork without management.

The difference between data management and data governance is

• Data governance is the overall management of data assets within an organization whereas data management refers to the operational activities involved in managing data.
• Data governance involves defining policies, procedures, and standards for how data is collected, stored, processed, and used while data management includes the processes and tools used to collect, store, process, and analyze data.
• Data governance ensures that data is consistent, reliable, and trustworthy while data management ensures that data is available and usable for the people who need it.
• Data governance verifies the data used is consistent and used across the organization whereas data management verifies that the data is available in the right format, at the right time, and in the right place.
• Data governance includes data dictionaries and data catalogs whereas data management is more concerned with data storage, processing, and exploration.

How can dataZen support you?

• dataZen is a master data management tool that enhances data quality and tightens security within the enterprise. It has over 7000+ master data templates, for over 200+ endpoints.
• Proper “System of Record” for master data, provides a Centralized data hub for consolidated reporting and querying of master data.
• It has preconfigured workflows supporting data governance and approval processes, and does data encryption and masking to keep data safe while at rest and in motion. This creates a single source of truth.

In conclusion, Data governance and data management are two distinct aspects of data management. data governance is focused on defining policies and establishing a framework for managing data, data management is focused on the day-to-day operational activities involved in managing data.

Even though both have different characteristics, both play a vital role in the effective management of organizational data, and they complement each other in ensuring that data is managed effectively throughout its lifecycle. With help of dataZen, you can fix fundamental issues with master data management such as duplicates, fragmentation, and inconsistency across systems, and also establishes master data governance rules to define a common data model, and master / transactional data creation using a workflow which creates a huge impact for a data.