Data is transforming the way companies operate today. From business decision-making to day-to-day operations, everything is dependent on data. And none of this is possible without transforming raw data into useful information, especially when a large amount of data and disparate sources are involved.
This is where data manipulation comes into play. It translates data into the required format so it can be easily cleaned and mapped for extracting insights.
This blog will illustrate the concept of data manipulation in detail and further explore the necessity of data manipulations tools for businesses. We’ll also share some tips and steps to help manipulate your data effectively.
What is Data Manipulation?
Data manipulation is defined as the process of changing or altering data in order to make it more readable and organized. For example, data can be arranged alphabetically to help the owner quickly find useful information. Another example of data manipulation is websites. Website owners can use web server logs to locate the most viewed web pages, traffic sources, and much more. Similarly, stockbrokers and analysts use data manipulation to forecast stock market trends.
Purpose of Data Manipulation: Why Use Data Manipulation?
Manipulation of data allows businesses to efficiently use the data for predicting trends, understanding customer behavior, increasing productivity, reducing costs, etc. Some of the benefits of data manipulation for a business are:
- Ensures format consistency: Data organized in a unified, orderly manner help business users make better decisions.
- Provides historical overview: Being able to quickly access data of previous projects can help an organization make decisions regarding deadline projection, team productivity, budget allocation, etc.
- Improves efficiency: By having more organized data, a business can isolate and even reduce external variables to contribute to business overall efficiency.
Data Manipulation Language
DML or data manipulation language is used to make data more organized or readable. DML is a computer programming language that is used for inserting, omitting, and altering data in a database. It makes the data easy to cleanse and map for further analysis. A commonly used data manipulation language is Structured Query Language (SQL). SQL is used to update and retrieve data in a relational database.
What are Data Manipulation Tools?
Data manipulation tools allow you to modify data to make it easier to read or organize. These tools help identify patterns in your data that may otherwise not be obvious. For instance, you can organize a data log in alphabetical order using a data manipulation tool so that discrete entries are easier to find.
People often confuse data manipulation with ETL and other transformation techniques. However, data manipulation involves sorting, rearranging, and moving data without essentially changing it. It includes operations to adapt data in the form that is needed to display information, or feed and train an analytics model.
The key purpose of data manipulation is to alter the relationship (either logical or physical) that one data item has with another, not the data itself. Common operations used to manipulate data include row and column filtering, aggregation, join and concatenation, string manipulation, classification, regression, and mathematical formulas.
ETL, on the other hand, serves a different purpose. It involves extracting data from the source system and making it compatible with the destination system before writing into it.
Why Do You Need Data Manipulation Tools?
Data manipulation is a critical task in process optimization, as it transforms data into a usable form that can be further utilized to generate insights, such as analyzing financial data, customer behavior, and carrying out trend analysis.
The process is widely used during integration to make data compatible with the target system. For example, users associated with accounting often manipulate raw data acquired from vendors and marketing to comprehend product prices, sales trends, or prospective tax requirements. Similarly, stock market experts manipulate data to forecast market trends so that they can manage their investment portfolio accordingly.
These are just a few use-cases of data manipulation. Some of the other ways in which manipulation can be beneficial for organizations include:
- Data Consistency
A consistent data format makes it easier to organize, read, and analyze data. When data comes from disparate sources, it has to be transformed and manipulated to create a unified format. After standardizing the format, it is easier to write data into the enterprise system or utilize it for reporting.
- Data Projection
As a business, you can’t deny the importance of data when it comes to business intelligence (BI). Creating an exhaustive analysis is vital for companies, particularly when it comes to investments. Every business makes use of past data to plan for the future. Manipulating data makes it easier to create projections, especially in the financial sector, where you are dependent on the results of your past investments for future considerations.
- Value Generation
Data manipulation allows you to update, modify, delete, and input data into a database. This means that you can leverage data to obtain in-depth insights and make better business decisions.
- Redundant Data Removal
Often, data coming from source systems include redundant, erroneous, or unwanted information. Making this data useful requires running it through quality checks and applying cleansing filters to extract the information essential for your company. Using data manipulation, you can swiftly clean your data so that you can filter out the records that matter.
- Data Interpretation
When dealing with complex data that involves multiple formats and business conditions, making sense of it without manipulation is next to impossible. You need to have the capability to visualize data and alter it into useful and comprehensible information. A data manipulation tool might resolve this problem by converting data into the desired format and integrate it with different tools to enhance the visual experience. This makes it easier for users to comprehend and consume data.
Tips and Steps to Manipulate Data
The most efficient way to manipulate data is via tools that offer built-in, automated data manipulation functions, such as data cleaning, mapping, aggregating, or storing. These tools save you the trouble of entering data manually and performing low-value repetitive tasks. Moreover, the automation features supported by these tools support report generation and delivery without any human intervention.
Here are the five key steps that are used to manipulate data effectively:
- The first step is to have a database that’s formed from your data sources.
- Next, clean the data obtained from the source system before rearranging and restructuring it.
- Import and construct a database that serves as the staging area.
- Combine or filter out information based on your business requirements.
- Lastly, leverage the manipulated data to garner valuable insights.
Enterprise-grade tools like Astera Centerprise simplify data manipulation by offering built-in transformations, and source and destination connectors. You can easily manipulate data without writing a single line of code. Moreover, you can schedule jobs using the solution’s automation and workflow orchestration capabilities.
Download the trial version of Astera Centerprise and experience first-hand how the software can help you manipulate data.