Data Manipulation: Definition, Importance and Tips

Updated February 3, 2023

Performing the process of data manipulation effectively can improve the quality of your data and analysis.

In this article, we explain how data manipulation is performed and why it's an important process, and we discuss strategies and tips to use as a guide.

Key takeaways:

Data manipulation is the process of arranging a set of data to make it more organized and easier to interpret.
Data manipulation is used in various industries including accounting, finance, computer programming, banking, sales, marketing and real estate.
The steps of effective data manipulation include extracting data, cleaning the data, constructing a database, filtering information based on your requirements and analyzing the data.

What is data manipulation?

Data manipulation is the process of organizing or arranging data in order to make it easier to interpret. Data manipulation typically requires the use of a type of database language called data manipulation language (DML). DML is a type of coding language that allows you to reorganize data by modifying it within its database program. Common operations used for data manipulation include:

Aggregation
Classification
Mathematical formulas
Regression analysis
Row and column filtering
String concatenation

Examples of data manipulation

Data manipulation can be used to:

Arrange data alphabetically or by date to find individual entries
Manage web server logs where website owners can monitor most-viewed web pages and traffic sources
Create forecasts of stock market trends
Assess the expense of products, pricing patterns or future tax obligations
View online information in a more useful way to users based on code in a user-defined software program

What is data manipulation language?

Communicating with the database program to make these adjustments may require the use of data manipulation language to ensure none of the data gets lost in the database when it's being organized. DML provides operations that handle user requests, offering a way to access and manipulate the data that users store within a database. Its common functions include inserting, updating and retrieving data from the database.

Here are some common DML commands used in the data manipulation process:

Select: This command allows you to select the data you want to manipulate from the database. Specifically, it tells the database what data to select and where to find the data.
Update: This command allows you to update existing data within the database. Specifically, it can communicate with the database to tell it what data needs to be updated, where the new data should be input and whether it should add the data records one at a time or together.
Insert: This command allows you to relocate data within the database. Specifically, it tells the database where the data is currently located and where the data needs to be moved.
Delete: This command allows you to remove data from the database. Specifically, it tells the database what data to delete and where it can find the data.

SQL, which stands for Structured Query Language, is one of the most well-known and longest-running database languages that can be used for data manipulation.

Purpose of data manipulation

Data manipulation is important because it allows you to easily access the information that’s critical to your specific business and goals. This process can be tailored to identify different data sets as your company grows or makes adjustments due to market demand. Data manipulation is also a valuable tool for identifying and correcting data redundancies in reporting.

Companies may manipulate data to transform it into useful insights that they can use for stakeholder presentations, project or financial decisions and trend or success measurements. Here are four reasons companies may choose to implement data manipulation:

Consistent and organized data: Companies may manipulate their data because it can provide them with well-organized databases. Categorization can allow companies to group similar data, which may make it easier to search for information.
Insightful project data access: It can allow companies to archive project data and access it later if they want to use it as a reference while working on a new project or setting business goals. Businesses may also reference their previous data when examining finances and whether profits are increasing.
More valuable data: Companies can tailor their results to provide specific insights. For example, if a business were interested in learning more about its website traffic and wanted to track the number of visitors over a certain period of time, it might manipulate its website traffic data to provide those results.
Reduces unnecessary data points: Sometimes, the data received might not provide useful insights or may be inaccurate. With data manipulation, companies can remove non-useful data insights and clean inaccurate data to provide accurate results.

How to use data manipulation effectively

Strategies for effectively using data manipulation can incorporate the use of multiple steps. Here are some common strategic steps you may use when manipulating data:

1. Create a database with information from different sources

A common initial strategic step is to create a database with information and data from different sources. This might be a database you create or an automated software you choose to use. If you choose to create a database yourself, you may use data modeling tools like Microsoft Excel or Google Analytics and Google Data Studio.

2. Restructure and cleanse data content

Another common strategic step is restructuring and cleansing data content to ensure it's accurate and organized. If you use automated software, it may complete this process for you. This can include making sure you properly link all data and analytics in organized patterns.

3. Combine information and remove redundancies

Once you've organized your data in the database, the next strategic step usually includes combining your information to check for redundancies. This can help you remove overlapping data and further organize your database. This might also include the combination of data in formulas to provide extensive niche data to meet business needs.

4. Analyze the data to find useful information

Typically, the final strategic step is to analyze the comprehensive data results to find useful information. This useful information may include consumer purchasing trends, profit insights or digital brand engagement. Depending on an individual company's needs, the useful information they find and analyze can vary.