What is data management?

The definition

Data management is the comprehensive set of procedures to follow and that develop and maintain quality data using the technology and available resources. It can also be defined as the implementation of architectures according to certain predefined policies and procedures to manage the entire life cycle of a company or organization. It consists of all disciplines related to data management resources.

The following are the main stages or procedures or disciplines of data management:

1. Database management system

2. Database management

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

1. Database management system:

It is one of the computer software of different types and brands available today. This software is specially designed for data management. These are just a few; Ms Access, MsSQL, Oracle, My Sql, etc. The choice of any of these depends on company policy, expertise and administration.

2. Database management:

Data management is a group of experts responsible for all aspects of data management. The roles and responsibilities of this team depend on the company over all database management policies. They implement the systems using software protocols and procedures to maintain the following properties:

a. Development and testing database,

b. Database security,

c. Database backups,

d. Integrity of the database and associated software,

e. Database performance,

f. Ensure maximum database availability

3. Data warehousing

Data warehousing, in other words, is the system of organization of historical data, storage capacity, etc. This system actually contains the raw material for the management of query-supporting systems. This raw material is such that the analysts can retrieve any type of historical data in any form, such as trends, time stamps, complex queries and analyzes. These reports are essential for any business to view their investments or business trends that will in turn be used for future planning.

The data warehousing is based on the following conditions:

a. The databases are organized in such a way that all data elements related to the same events are linked,

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

c. All data in databases is not deleted or overwritten, the data is static, only readable,

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

4. Data modeling

Data modeling is the process of creating a data model through application and model theory to create a data model instance. The data modeling is basically defining, structuring and organizing the data using a predefined protocol. The thesis structures are then implemented in the data management system. In addition, it will also impose a 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 ensures database cleanup to improve database quality.

6. Data security

Also referred to as data protection, this is a system or protocol implemented in the system to ensure that the databases are kept completely secure and that no one can corrupt through access control. Data security, on the other hand, also offers the privacy and protection of personal data. Many companies and governments of the world have laws in place to protect personal data.

7. Data movement

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

8. Data architectures

This is the most important part of the data management system; it is the procedure for planning and defining the target states of the data. It is, realizing the target state, that describes how the data is processed, stored and used in a given system. It created criterion to process the operation to allow to design data streams and control the data stream in a given system.

In fact, data architecture is responsible for defining the target states and alignment during initial development, and is then maintained through implementations of small follow-ups. While defining the states, the data architecture breaks down into smaller sub-levels and parts and is then reshaped. Those levels can be created under the three traditional data architectural processes:

a. Conceptually, representing all business entities

b. Logical means the relationship between these business entities.

c. Physically, it is the realization of the data mechanism for a specific function of the database.

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

9. Data analysis

Data analysis is the set of procedures used to extract the required information and prepare final reports. Depending on the type of data and the query, this may include the application of statistical methods, trend determination, selection or deletion of certain subsets of data based on specific criteria. Data analysis is actually the verification or disapproval of an existing data model, or to extract the necessary parameters to achieve a theoretical model about realty.

10. Data mining

Data mining is the procedure for extracting unknown but useful data parameters. It can also be defined as the set of procedures for extracting the useful and desired information from large databases. Data mining is the principle of sorting the large by the large amount of data and selecting the relevant and required information for specific purposes.



Source by Nawaz Ali Lakho