DMG Full Form
DMG stand for the Data management. Data management refers to the professional practice of building and maintaining a framework for ingesting, storing, mining and archiving integrated data for a modern business. Data management is the backbone that links all segments of the information lifecycle. Data management works symbiotically with process management, ensuring that the actions the team takes have the cleanest, most current data available – meaning, tracking changes and trends in real time. Below is an in-depth look at the practice, its benefits and challenges, and best practices to help your organization make the most of its business intelligence.
7 types of data management
Master Data Management: Master data management (MDM) is the process of ensuring that the organization is always operating on - and making decisions based on - a version of current, "true" information. Ingesting data from all of its sources and presenting it as a stable, reliable source, as well as retransmitting the data across different systems, requires the right tools.
Data Stewardship: A data steward does not develop information management policies, but deploys and enforces them throughout the enterprise. As the name implies, a data steward oversees enterprise data collection and movement policies, ensuring that practices are implemented and regulations are enforced.
Data Quality Management: If the data steward is a type of digital sheriff, the data quality manager may be considered his or her court clerk. Quality Management is responsible for linking aggregated data to underlying issues such as duplicate records, inconsistent versions, and more. Data quality managers support a defined data management system.
Data Security: One of the most important aspects of data management today is security. Although emerging practices such as DevSecOps include security considerations at every level of application development and data exchange, security experts are tasked with managing encryption, preventing unauthorized access, preventing accidental movement or deletion, and other frontline concerns.
Data Governance: Data governance sets the law for the state of information of an enterprise. 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 others, and data management processes following a governance policy that serves a master data management approach.
Big Data Management: Big Data is a catch-all term used to describe the collection, analysis and use of large amounts of digital information to improve operations. Broadly speaking, this area of data management specializes in the intake, integrity, and storage of a tidal stream of raw data that other management teams use to improve operations and security or inform business intelligence.
Data Warehousing: Information is the building block of modern business. The vast amount 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 used to collect raw data and analyze it in depth to generate business insights.