The Importance Of Data Management

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Data management is the process of controlling the information generated during a research project. Any research will require some level of data management, and funding agencies are increasingly requiring scholars to plan and execute good data management practices. Managing data is an integral part of the research process. It can be challenging particularly when studies involve several researchers and/or when studies are conducted from multiple locations. How data is managed depends on the types of data involved, how data is collected and stored, and how it is used - throughout the research lifecycle. The outcome of your research depends in part on how well you manage your data. Managing data helps you as a researcher organize research files…show more content…
Basically it organizes these files into a database for the storage, organization, manipulation, and retrieval by the computer's operating system. (DBMS) consists of software that operates databases, providing storage, access, security, backup and other facilities. This system can be categorized according to the database model and the type of computer that they support such as a server cluster or a mobile phone, the query language(s) that access the database, such as SQL or XQuery, performance trade-offs, such as maximum scale or maximum speed or others. Some DBMS cover more than one entry in these categories, e.g., supporting multiple query languages. Meanwhile database management systems are usually accessed in a client-server manner, where the database client and the server are located on different machines (in a local area)…show more content…
Data Security The data stored in the flat file(s) can be easily accessible and hence it is not secure. Example: Consider an online banking application where we store the account related information of all customers in flat files. A customer will have access only to his account related details. However from a flat file, it is difficult to put such constraints. It is a big security issue. 2. Data Redundancy In this storage model, the same information may get duplicated in two or more files. This may lead to to higher storage and access cost. it also may lead to data inconsistency. For Example, assume the same data is repeated in two or more files. If a change is made to data stored in one file, other files also needs to be change accordingly. Example: Assume employee details such as firstname, lastname, emailid are stored in employee_details file and employee_salary file. If a change needs to be made to emailid, both employee_details file and emplyee_salary file need to be updated otherwise it will lead to inconsistent data. However, it is possible to design file systems with minimal redundancy. Also note that Data redundancy is sometimes

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