Data management encompasses most aspects of managing data as a valuable powerful resource. It includes developing procedures for getting, collect, store, enhance and safeguard data — all while using the goal of delivering high-quality organization outcomes that could be trusted.
Thinking about managing data as a reference dates back for the first blooming of information technology, when IT pros recognized that computers reached incorrect data when they were fed erroneous or not enough data. After some time, mainframe-based hierarchical sources helped to formalize the data control, which is now thought of an important a part of a firm’s overall THAT infrastructure.
A number of criteria may be used to measure info quality, depending on the industry through which an organization works and the position that info plays in its goals. A few examples include completeness, consistency and uniqueness. Completeness measures if all essential values can be obtained — for example , if your group needs a customer’s last name to be sure avg usa emailing is dealt with correctly, the repository must consist of that little bit of data. Steadiness ensures that info values remain the same as that they move between applications and networks, when uniqueness guaruntees duplicate info items are not really stored two times in different spots.
Companies that excel at data management own a well-defined set of data processes that help them recognize, analyze and interpret business problems and opportunities in a timely style – so they can take action quickly and with certainty. In addition to improving decision-making, data management may reduce risk and help businesses meet regulating requirements.