Data management refers to the professional practice of constructing and maintaining a framework for ingesting, storing, mining, and archiving data integral to modern business. Data management is the spine that connects all segments of the information lifecycle.

Data management works symbiotically with process management, ensuring that the actions teams take are informed by the cleanest, most current data available-which, today, means tracking changes and trends in real-time. Below is a deeper look at the practice, its benefits and challenges, and the best practices for helping your organization get the most out of its business intelligence.

7 Types of Data Management


Master data management (MDM) is the process of ensuring the organization is always working with-and making decisions based on a single version of current, 'true' information. Ingesting data from all of your sources and presenting it as one constant reliable source, as well as repropagating data into different systems, requires the right tools.
The development team may work from one data set, the sales team from another, operations from another, and so on. Modern data management relies on access to all this information to develop modern business intelligence. Real-time data platform services help stream and share clean information between teams from a single, trusted source.
If a data steward is a kind of digital sheriff, a data quality manager might be thought of as his court clerk. Quality management is responsible for combing collected data for underlying problems like duplicate records, inconsistent versions, and more. Data quality managers support the defined data management system.
One of the most important aspects of data management today is security. Though emergent practices like DevSecOps incorporate security considerations at every level of application development and data exchange, security specialists are still tasked with encryption management, preventing unauthorized access, guarding against accidental movement or deletion, and other frontline concerns.
Data governance sets the law for an enterprise's state of information. A data governance framework is like a constitution that clearly outlines policies for the intake, flow, and protection of institutional information. Data governors oversee their network of stewards, quality management professionals, security teams, and other aspects of operations in pursuit of a governance policy that serves a master data management approach.
Big data is the catch-all term used to describe gathering, analyzing, and using massive amounts of digital information to improve operations. In broad terms, this area of data management specializes on intake, integrity, and storage of the tide of raw data that other management focuses use to improve operations and security, and inform business intelligence.
Information is the building block of modern business. The sheer volume of information presents an obvious challenge: What do we do with all these blocks? Data warehouse management provides and oversees the physical and/or cloud-based infrastructure to aggregate raw data and analyze it in depth to produce business insights.