Data governance creates policies and processes for data, whereas data management implements such policies and procedures to collect and use data for decision-making. To better comprehend these terms, let us understand data governance vs data management and how they work.
What is Data Governance?
Data governance is an important component of data management; the discipline of controlling how data is processed within an organization. It defines the processes and responsibilities that maintain the quality and security of data used within a company or organization. Data governance specifies who can take what actions, on which data, in what circumstances, and with what techniques.
You might be interested in knowing how to build a data governance strategy.
What is Data Management?
Data management is the design and implementation of technologies, procedures, and processes that manage an organization’s whole data lifecycle. It is vital to have certain rules and procedures in place to evaluate complicated, massive data. When data is regarded as a valuable company asset, it must be handled accordingly. Data management involves a wide range of data projects, including data governance. If so, then how are they different? Let us understand this in the data governance vs data management section below.
Distinctions Between Data Governance and Data Management
It is critical to recognize that data governance is a component of overall data management. Data governance without implementation is just paperwork. Enterprise data management supports the implementation and enforcement of data governance rules and processes. While data management and data governance have certain similarities, the beauty is in their distinctions and how they function together.
- Data governance establishes the norms and processes, while data management carries them out in order to assemble and use data for decision-making. There is an analogy that explains the difference between data governance vs data management- Data governance sets the blueprint for new building development, whereas data management is the process
- Data governance was historically seen to be a job of business and IT teams. As a result, adopting data governance would require the participation of business managers, domain data owners, and other business stakeholders. Meanwhile, data management would be all about execution; putting the governance structure in place, and affecting the organization’s business objectives. This included creating data storage policies to establish access rights and limits. As a result, only technical positions such as data engineer, architect, or database administrator(DBA) were necessary.
- As a result, data governance technologies are used to codify these rules and implement them throughout the organization’s data assets. This contains data dictionaries and glossaries, as well as data catalogues.
- Data management tools, on the other hand, are primarily focused on data storage, processing, and exploration.
- Data governance controls “how firms decide about using data”. As a result, the procedures involved can be: Implementing data quality checks, establishing data access policies, etc. Whereas, data management is all about “how businesses use data”. These processes adhere to the data governance framework’s criteria. Processes that may be involved include Data transformations used to keep data in consistent forms, data storage in warehouses, lakes, and other locations, etc.
Effective data management and governance are critical components of establishing IT systems that operate business applications and offer analytical information. This enables corporate executives, business managers, and other end users to drive operational decision-making and strategic planning. We at Intone take a people-first approach to data management. We are committed to providing you with the best data management service possible, tailored to your needs and preferences. We offer you:
- Knowledge graph for all data integrations done
- 600+ Data, and Application and device connectors
- A graphical no-code low-code platform.
- Distributed In-memory operations that give 10X speed in data operations.
- Attribute level lineage capturing at every data integration map
- Data encryption at every stage
- Centralized password and connection management
- Real-time, streaming & batch processing of data
- Supports unlimited heterogeneous data source combinations
- Eye-catching monitoring module that gives real-time updates
Contact us to learn more about how we can help you!