Data Management is the development, implementation and management of plans, policies, procedures, practices and programs that control, protect, deliver and augment the value of data and information assets in an effective manner.
Various approaches are there in data management. One such approach is Master Data Management or MDM, which is a comprehensive method of facilitating an organization to connect all of its important data to one file, which is called as a master file. Generally, the master file provides a common point of reference.
Nowadays, businesses are subject to a lot of compliance regulations and hence the effective management of business data has grown in importance. Moreover, the complete volume of data or information that should be managed by corporate has increased so markedly, which is sometimes called as big data.
However, organizing, administrating and governing huge volume of both structured as well as unstructured data is called as Big data management. The big data management is generally employed by big corporate, government agencies and other organizations to manage their fast growing pools of data and a different variety of data types.
Today, we are in a data deluge and it can keep on growing in intensity as the quantity, frequency and resolution of data sources increases, and the dependence on data also increases rapidly.
The benefits of data management are as follows:
Improved Efficiency:
Generally, the data are easy to handle when they are obtained and hence proper data management can seem to be not necessary. But, on the other hand, over time, data accumulates, many changes are made in data, some data are intentionally discarded, some data may be lost unfortunately, or subsets of the data may be copied or shared with others. Therefore, if data are not properly managed, then an increasing amount of time has to be spent on corrective activity. So, to reduce the amount of time spent on dealing with data-related problems, it is necessary to assign explicit responsibility to take care of data, documenting and implementing procedures for handling data, and making sure that the data is properly classified, stored and backed up.
Protection from Data-related Risks:
Data security is a very important part of data management. To ensure that your data is never permanently lost, you need to procure backup and recovery procedures and also data handling protocols. You can secure your data by controlling its malicious access and inappropriate release. Good data management practices ensure data security by protecting a project and its members from breaking privacy laws and loss of reputation and prestige.
Improved Research Quality:
In particular, digital data could be effectively harmed or contaminated. By protecting the long-term quality of data, data management guarantees the authenticity of later investigation and underpins the validity of research findings.
Enhanced Reputation and Prestige:
There is a growing society of open-data over numerous research communities. New administrations and foundation are constantly made to support the long term preservation of data sets and their re-use by others long after the definitive research is completed. Distribution of data, separated from research findings, is turned into an additional source of eminence for researchers. New norms for citing existing data sets and accumulations are constantly improved and data citations are liable to turn into an accepted additional measure of academic performance.
Good data management is required to make sure data is able to be protected at the closure of a task or project, and made accessible for people in the public .
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