In today’s business world, data is changing how companies operate. Data plays a crucial role in everything from day-to-day operations to highly important and sensitive business decisions. Data transformation makes all of this possible, especially when dealing with large amounts of data from different sources. It is here that data manipulation tools come into play. Translating data into the required format facilitates cleaning and mapping for insight extraction.
A detailed explanation of the data manipulation concept will be presented in this blog, along with an in-depth exploration of the need for businesses to have data manipulation tools. The data manipulation process can also be simplified with our tips and steps.
What Is Data Manipulation?
In data manipulation, data is organized in a way that makes it easier to read, or that makes it more visually appealing, or that makes it more structured. Data collections can be organized alphabetically to make them easier to understand.
In contrast, unorganized employee information can make it difficult to find information about an individual employee in an organization. Consequently, it would be easier for employees to find information about themselves if all their information was arranged alphabetically.
By manipulating data, website owners can track traffic sources and popular pages on their sites. Therefore, web server logs frequently use it.
Contrary to popular belief, hacker attacks on IT infrastructure are not the only forms of cybercrime. Digital content theft, fraudulent data manipulation, and sabotage are among the most prevalent forms of cybercrime. Attackers typically target retailers and shopping platforms.
The attackers falsify the data analysis of operators with targeted bot requests, causing them to make decisions that are not based on reality.
In terms of technology, the fraudulent methods described rely on bots making machine requests. It is estimated that such requests account for 20 percent of the average website traffic. Therefore, controlling the traffic on online services is essential for operators.
Why Do We Need Data Manipulation?
Through the manipulation of data, businesses are able to predict trends, understand customer behavior, increase productivity, reduce costs, and increase profits. There are also a number of other advantages, including the following:
- Format consistency: To make better business decisions, it is necessary for users to have access to unified, organized data.
- Historical overview: It’s important to have access to the data of previous projects so as to make quick decisions about deadline projections, team productivity, allocation of budgets, etc., within an organization.
- Improved efficiency: Data can be organized more effectively over the course of a business to isolate external variables and even reduce these variables for the business to be more efficient.
Data Manipulation Language
Data manipulation language (DML) is a programming language that is used to manipulate data in a manner that makes it more organized and readable. In a database, this programming data manipulation language is used to add, delete, and update information in a database by inserting, omitting, and updating the data. The data can easily be cleansed and mapped to be used for further analysis using this method.
Structured Query Language, or SQL, is one of relational databases’ most commonly used data manipulation languages. By using Insert, Select, and Update statements, SQL is used to update and retrieve the data in the database.
What are the Various Data Manipulation Tools?
The use of data manipulation tools enables you to simplify the reading and organizing of data. By using these tools, you can identify patterns in your data that might not be obvious otherwise. In order to make finding discrete entries easier, a data manipulation tool can be used to arrange a log in alphabetical order.
In order to manipulate data effectively, the following data analytics tools for beginners can be used:
- Tableau: Tableau is a Salesforce tool used for data manipulation. Raw data is simplified easily to a user-friendly format and is mostly used for Business Intelligence. Also, it is commonly referred to as a reporting tool. Data can be explored, visualized, and reports can be prepared for the same data using it. Data connectors or parsers can be used to connect or store heterogeneous data from diverse sources.
- RapidMiner: The RapidMiner company developed RapidMiner, a data manipulation tool. Java is used in its development. This tool can be used for the analysis of predictive data, business applications, research, education, and commercial applications. It accelerates delivery because it adheres to a template framework. As a result, delivery times go faster, and transformation errors get less frequent.
- Excel: A variety of functions and data management can be automated with Excel. Data can be collected in Excel in the form of rows and columns, and that data can be presented as well. The data can be sorted alphabetically, numerically, graphically, and visually. Using an Excel application, it may also be added, deleted, edited, linked, and moved.
- KNIME: Known as the Lego of Analytics, KNIME is a tool for data manipulation that integrates a wide variety of learning and mining components. In order to blend different sources of data, it uses JDBC and a graphical user interface.
- SAS: SAS provides business intelligence and analytics solutions through its Statistical Analysis System. SAS Institute developed this software. Data manipulation is frequently performed with this tool. Machine Learning algorithms and functions (cleaning, transformation, preprocessing, filtering) enable users to create predictive analyses. In addition to its powerful visualization tools, it has self-organizing maps and three-dimensional graphs. A flexible file operator is used for data input and output for tree modeling, and XML is used to describe tree modeling.
- Apache spark: Using Apache Spark, you can manipulate data quickly. The application’s processing speed is increased by its memory cluster computing. In addition to batch applications, Spark has iterative algorithms, streaming, and collaborative queries. Furthermore, this workload can be handled within a single system, thus reducing the management burden of providing separate resources.
Why Do You Need Data Manipulation Tools?
Process optimization requires data manipulation. Analyzing financial data, customer behavior, and trend analysis are just some examples of how it transforms data into a form that can be used for further analysis.
The use of data manipulation tools often complicates the integration of data. For example, raw data obtained from vendors and marketing are often manipulated in accounting to understand current product prices, sales trends, and potential tax implications. Data can also be leveraged by stock market professionals for forecasting market trends, thus enabling them to manage their investment portfolios accordingly.
Data manipulation can be used for a variety of purposes. Manipulation can also benefit organizations in the following ways:
- Data Projection: In terms of business intelligence (BI), you cannot ignore the importance of data. When it comes to investing, companies need to conduct an exhaustive data analysis. Data from the past is used by every business to plan for the future. Financial sectors, especially those relying on past investment results for future planning, use data manipulation tools to create projections.
- Data Interpretation: Getting the most out of complex data is next to impossible without manipulating it. To make data valuable and comprehensible, you need to be able to visualize it. In this case, a data manipulation tool would be able to solve this problem by converting the data to the desired format and integrating it with a variety of tools to enhance the visual experience. Data can be comprehended and consumed more easily this way.
- Value Generation: A database can be manipulated by updating, modifying, deleting, and adding data. Thus, data can be leveraged to make better business decisions based on in-depth insights.
- Data Consistency: Organizing, reading, and analyzing data is easier when the data format is consistent. It is necessary to transform and manipulate data when it comes from disparate sources to create a unified format. When the format is standardized, data can be easily written into the enterprise system or utilized for reporting purposes.
- Redundant Data Removal: Oftentimes, source systems provide data that is erroneous, redundant, or unwanted. The information essential to your company can only be extracted by running this data through quality checks and applying cleansing filters. In order to filter out the records that matter, you can use data manipulation to clean your data swiftly.
Tips for Data Manipulation
When manipulating data, it is best to use tools that are built-in, automated tools and provide other functions such as data cleansing, mapping, aggregating and storing. Data entry and repetitive tasks can be performed easily and efficiently with these tools. Furthermore, these tools provide automation features that make generating and delivering reports seamless.
Five key tips are involved in effective data manipulation:
- Extraction of data from sources is the first step
- After you’ve cleaned the data in the source system(s), reorganize and restructure it as needed
- Create a staging area by importing and constructing a database
- Based on your business needs, combine or filter information
- Lastly, uncover valuable insights based on manipulated data
Steps For Data Manipulation
You should consider the following steps when you want to begin manipulating data:
- Data manipulation is only feasible if you have data to manipulate. In this case, data sources need to be used to generate a database.
- It is necessary to reorganize and restructure this knowledge. Information can be cleansed through the manipulation of data.
- You can work on the database once it has been imported.
- Information can be combined, erased, or merged through the manipulation of data.
- Manipulating data simplifies data analysis.
Data manipulation can be done in various ways in Data Science. The purpose of this is to make it easier to understand data or structure it more effectively. In marketing, sales, accounting, and customer service, data is best used when it can be manipulated. In order to conduct proper data analysis, the data should be rearranged, sorted, modified, and shifted.
Last but not least, data manipulation is helpful to people and organizations in making their data more useful. Using these techniques, you can accomplish this.
If you’re willing to learn more about the professional use of data manipulation tools and techniques, then you must check out IIM Indore’s Integrated Program In Business Analytics in collaboration with UNext.