Introduction to Data Manipulation

Imagine a life where we didn't sort our clothes or have them arranged in our closets. I bet finding that favourite dress of yours would be such a hassle. You'd have to sift through lots and lots of clothing to find it.

Organizing and sorting our clothes helps us find our stuff easily. It helps us see the clothes we have at a glance. Overall, it helps our closet look tidy. Similarly, data has to be organized in a way that makes it comprehensible. This is done by sorting the data alphabetically, numerically, or based on set criteria. This process is known as Data Manipulation.

Why Manipulate Data?

Data Manipulation is an important step in the data analysis process. Besides being able to comprehend your dataset at a glance, as hinted above, some other benefits of this include:

• Consistent layout: Having your data in an organized and orderly layout, helps with consistency and aids better decision-making.

• Reduces redundancy: This process helps with eliminating data points that are not useful at a certain point, and with identifying unique points in a dataset.

• Quick and insightful data access: It helps businesses refer to the direct information they need from a dataset, without sifting through tons of unnecessary data.

Data Manipulation Language (DML)

Performing data manipulation is usually done via a database language. A database is an organized collection of data stored electronically and accessed by a computer. Hence, a database language is a programming language or set of instructions written to access and/or manipulate data in a database. The most used database language is Structured Query Language (SQL). Some other ones include LINQ, GraphQL, SQL/XML, OQL, and XQuery.

Common DML Commands

Some commands used in the data manipulation process are:

• SELECT: This command lets you select or specify the data you want, and lets your database know where the data is stored.

• UPDATE: As the name suggests, this command lets you update existing data in the database, in your specified location.

• INSERT: With this command, you can add new data to the existing database.

• DELETE: You can remove existing data of your choosing, from the database, with this command.

Steps For Data Manipulation

To ensure efficient data manipulation, the following steps have to be carried out:

1. Without data, there cannot be data manipulation. That said, the first step in the process is to create a database with your data source.

2. Next, the data in this database should be cleaned before it is rearranged and restructured to suit your requirement.

3. After that, another database should be created to start working on your data.

4. Using data manipulation, you can filter out information to suit your needs.

5. Last, analyze the manipulated data to draw your insights.

Tips For Using Data Manipulation

Some helpful tips for data manipulation:

• Understand your needs or needs of the business before commencing.

• Determine the exact data you need for your specific business needs.

• Consider making use of automation tools.

• Understand mathematical functions and statistics and how they can help process your data.

• Filter your data to get specific information.

• Update your manipulation as required.

• Use data visualization tools to present your manipulated data.

Sources

• What is Data Manipulation - Javatpoint.

• Types of database languages - Indeed.

• Data Manipulation - Indeed.

• Data Manipulation - QuestionPro.

• Data Manipulation Tools - Astera.