The Definition:

Data Management is the comprehensive series of procedures to be followed and have developed and maintained the quality data, using the technology and available resources. It can also be defined that it is the execution of architectures under certain predefined policies and procedures to manage the full data lifecycle of a company or organization. It is comprised of all the disciplines related to data management resources.

Following are the key stages or procedures or disciplines of data management:

1.   Database Management system
   

2.   Database Administration
   

3.   Data warehousing
   

4.   Data modeling
   

5.   Data quality assurance
   

6.   Data Security
   

7.   Data movement
   

8.   Data Architectures
   

9.   Data analysis
   

10. Data Mining

11.   Database Management system:
    

It is one of the computer software from various types and brands available these days. These software are designed for specifically for the purpose of data management. These are few of these; Ms Access, Ms SQL, Oracle, MySql, etc. The selection of any one of these depends upon the company policy, expertise and administration.

2.   Database Administration:
   

Data administration is group of experts who are responsible for all aspects of data management. The roles and responsibilities of this team depends upon the company’s over all policy towards the database management. They implement the systems using protocols of software and procedures, to maintain following properties:

a. Development and testing database,

b. Security of database,

c. Backups of database,

d. Integrity of database, and its software,

e. Performance of database,

f. Ensuring maximum availability of database

3.   Data warehousing
   

Data warehousing, in other words is the system of organization of historical data, its storage capability etc. Actually this system contains the raw material for the management of query support systems. That raw material is such that the analysts can retrieve any type of historical data in any form, like trends, time stamped data, complex queries and analysis. These reports are essential for any company to review their investments, or business trends which in turn will be used for future planning.

The data warehousing are based on following terms:

a. The databases are organized so that all the data elements relating to the same events are linked together,

b. All changes to the databases are recorded, for future reports,

c. Any data in databases is not deleted or over written, the data is static, readable only,

d. The data is consistent and contains all organizational information.

4.   Data modeling
   

Data modeling is the process of creating a data model by applying and model theory to create data model instance. The data modeling is actually, defining, structuring and organizing the data using predefined protocol. Then the theses structures are implemented in data management system. In addition, it also will impose certain limitation on the database with in the structure.

5.   Data quality assurance
   

Data quality assurance is the procedure to be implemented in data management systems, to remove anomalies and inconsistencies in the databases. This also performs cleansing of databases to improve the quality of databases.

6.   Data Security
   

It is also called as data protection, this is system or protocol which is implemented with in the system to ensuring that the databases are kept fully safe and no one can corrupt by access controlling. The data security, on other hand, also provides the privacy and protection to the personal data. Many companies and governments of the world have created law to protect the personal data.

7.   Data movement
   

It is one term broadly related to the data warehousing that is ETL (Extract, Transform and Load). ETL is process involved in data warehousing and is very important as it is the way data is loaded into the warehouse.

8.   Data Architectures
   

This is most important part of the data management system; it is the procedure of planning and defining the target states of the data. It is, realizing the target state, describing that how the data is processed, stored and utilized in any given system. It created criterion to processes the operation to make it possible to design data flows and controls the flow of data in any given system.

Basically, data architecture is responsible for defining the target states and alignment during the initial development and then maintained by implementations of minor follow-ups. During the defining of the states, data architecture breaks into minor sub levels and parts and then brought up to the desired form. Those levels can be created under the three traditional data architectural processes:

a. Conceptual, which represents all business entities

b. Logical means the how these business entities are related.

c. Physical, is the realization of the data mechanism for specific function of database.

From above statements, we can define that the data architecture includes complete analysis of the relationship between functions, data types and the technology.

9.   Data analysis
   

Data analysis is the series of procedures which is used to extract required information and produce conclusion reports. Depending upon the type of the data and the query, this might include application of statistical methods, trending, selecting or discarding certain subsets of data based on specific criteria. Actually, data analysis is the verification or disapproval of an existing data model, or to the extract the necessary parameters to achieve theoretical model over realty.

10. Data Mining

Data mining is the procedure to extract unknown but useful parameters of data. It also can be defined that it is the series of procedures to extract the useful and desired information from large databases. Data mining is the principle of sorting the large through the large amount of data and selected the relevant and required information for any specific purposes.