Data Manipulation Language(DML)

Data manipulation is the process of changing or altering data in order to make it more readable and organized. You can arrange data alphabetically to expedite the process of finding useful information. For example, it can be difficult to find information about any particular employee in an organisation if all the employees' information is not organized. Therefore, all employee's information could be organized in alphabetical order that makes it easier to find information easily of any individual employee. We use DML to accomplish this.

Data manipulation language is capable of adding, removing and altering databases, to something we can read. We can clean and map the data using DML, to make it digestible for expression.

SQL (Structured Query Language) is a popular data manipulation language which is used to retrieve and manipulate data in a relational database. Other forms of DML are those used by IMS/DLI, CODASYL databases, such as IDMS and others.

They are four functions or commands that direct databases where to find data and what to do with it, including:

SELECT: This command selects a section of the database (a few rows/ columns) to work on.

UPDATE: This command is required to make changes to existing data.

INSERT: This command is used to insert data in a different location or move data.

DELETE: This command is used to delete the redundant or duplicate data from the table.

For business operations and optimization data manipulation is key feature. You have to be able to deal with the data in the way you need it to use data properly and turn it into valuable information such as analyzing financial data, consumer behaviour and doing trend analysis. As such, data provides an organisation with many advantages like;

•Consistent data: It can be structured, read and better understood by providing data in consistent format.

•Usability: Data manipulation allows users to cleanse and organise data and use it more efficiently.

•Forecasting: Data manipulation enables business to understand historical data and help them prepare data analysis.

•Cleansing: Data manipulation helps clear unwanted data and keep information that matters.

How to Manipulate Data

Performing data manipulation would require you go through the following steps;

•Create a database from different data source.

•Cleanse, rearrange and restructure data.

• Import and build a database to work with.

•Combine, merge and remove information based on requirements.

•Acquire insights by conducting data analysis and use the derived information to make decisions.

Data manipulation Terminology provides an efficient way of doing it when it comes to operating inside existing data, whether it is to add, transfer or erase data. Data comes in several forms and is required to be able to make decisions for business leaders. Data is best used from marketing to sales, accounting to customer service, when it can be manipulated for some relevant reason. Proper data analysis depends on the ability to manipulate data including rearranging, sorting, editing and moving data around.