5 Top Tips for Data Manipulation

Data Analysis
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Regardless of your industry, data is changing the way organisations function. Structured data, or the type of information that is only readable to machines, must have a uniform structure to work correctly. To be usable by humans, the data has to be translated and manipulated so that it is cleansed and mapped so that it can provide useful insights. With an increasing amount of data being used and stored, the necessity for data manipulation becomes even more critical.

As such, we will take a look at the ins and outs of data manipulation, as well as some of the top tips of how you and your software can better organise data to extract useful insights.

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Table of Contents

1. What is Data Manipulation?

2. What is the Difference between Data Manipulation and Data Modification?

3. Purpose of Data Manipulation

4. What are Techniques for Data Manipulation?

5. What are Tips for Manipulating Data?

6. What are Data Manipulation Examples?

7. What are Data Manipulation Tools?

8. Why Use Data Manipulation Tools?

9. How to Manipulate Data?

10. What are the Steps for Manipulating Data?

11. How to Improve Data Manipulation?

12. Bottom Line

What is Data Manipulation?

Data manipulation refers to the process of adjusting data to make it organised and easier to read.

Data manipulation language, or DML, is a programming language that adjusts data by inserting, deleting and modifying data in a database such as to cleanse or map the data. SQL, or Structured Query Language, is a language that communicates with databases. When using SQL- data change statements for data manipulation, four functions can occur, namely:

  • Select
  • Update
  • Insert
  • Delete

These commands tell the database where to select data from and what to do with it.

Here’s how it works:

  • SELECT: The select statement allows users to pull a selection from the database to work with. You tell the computer what to SELECT and FROM where.
  • UPDATE: To change data that already exists, you will use the UPDATE statement. You can tell the database to update certain sets of information and the new information that should be input, either with single records or multiple records at a time.
  • INSERT: You can move data from one location to another by using the INSERT statement.
  • DELETE: To get rid of existing records within a table, you use the DELETE statement. You tell the system where to delete from and what files to get rid of.

Since SQL does not allow you to import or export data from outside sources, some providers can store data and give you the tools to manipulate data for your business needs.

What is the Difference between Data Manipulation and Data Modification?

Data manipulation and data modification are commonly used interchangeably. With respect to data processing, the two are mutually exclusive. Here’s why:

Data modification is when a computer’s saved value is changed to a different value. Thus, data is modified when it is stored in the same location.

Data manipulation is the process of pulling information from data by applying logic to generate a new set of data.

This example helps to clarify:

  • Data modification would be if you change the value in a spreadsheet’s cell from 100 to 150.
  • Data modification would be if you create a formula in Column B and apply it to the data in Column A to reap new data as a result in Column C.

Purpose of Data Manipulation

Data manipulation is a crucial function for business operations and optimisation. To properly use data and transform it into useful insights like analysing financial data, customer behaviour and performing trend analysis, you have to be able to work with the data in the way you need it. As such, data manipulation provides many benefits to a business, including:

  • Consistent data: Having data in a consistent format allows it to be organised, read and better understood. When you take data from different sources, you may not have a unified view, but with data manipulation and commands, you can make sure that your data is consistently organised and stored.
  • Project data: Being able to use historical data to project the future and provide more in-depth analysis is paramount for businesses, especially when it comes to finances. Data manipulation makes this function possible.
  • Create more value from the data: Overall, being able to transform, edit, delete and insert data into a database means that you can do more with your data. By having information that stays static, it becomes useless. But, when you know how to use data to your benefit, you can have clear insights to make better business decisions.
  • Remove or ignore unneeded data: Frequently, there is data that is unusable and can interfere with what matters. Unnecessary or inaccurate data should be cleaned and deleted. With data manipulation, you can quickly cleanse your records so that you can work with the information that matters.

What are Techniques for Data Manipulation?

Data manipulation techniques often follow the same flow. Whether you choose to use a data manipulation tool or manage it manually (which is time-consuming and error-prone), here’s what to expect:

Data collection

Create a database by pulling data from multiple sources. Data can exist within a software system, Google Analytics, Excel, etc.

Data organisation

Structure and clean the data to ensure it is accurate. Everything you glean from the data will depend on this starting point. During this process, you’ll combine and eliminate redundancies within your database.

Apply data analysis

The last step is to reap the insights from the data. With an automation solution like SolveXia, advanced analytics are automated at your fingertips with the click of a button.

What are Tips for Manipulating Data?

The best tip there is for data manipulation is to utilise data manipulation tools that automate it for you. These software cleanse, map, aggregate, transform, and store data to make it usable. Along with the use of tools, it’s best to:

  • Understand your needs before starting
  • Locate and collect the data you need to accomplish your goals
  • Understand how mathematical functions could work in your favour (if you choose to work manually)
  • Filter data properly
  • Use data visualisation tools to easily represent manipulated data

What are Data Manipulation Examples?

Data manipulation serves a variety of purposes. By looking at data manipulation examples, it becomes easier to understand its importance. So, let’s consider this:

Accountants may use data manipulation to assess product expenses or future tax obligations. To expand, data analytics and manipulation is performed so that tax information is known in advance before taxes are due. It can be used to make more informed business decisions at the time, serving as a strategic advantage for executive-level personnel.

Data manipulation is also required for account reconciliation. To exemplify, when you connect data from disparate sources, as you do when it’s time to perform account reconciliation, you’ll need to ensure it’s all formatted properly and consistent for its transaction matching application. Data manipulation tools handle this for you.

Short stock analysts need to understand patterns in the constantly changing stock market. As such, they may use data manipulation to create forecasts and make smarter decisions with regard to their trades.

Along with all of these financial applications, computers themselves use data manipulation to present information realistically to users. This type of data manipulation is based on the code inherent in a user-defined software program or web page.

What are Data Manipulation Tools?

Data manipulation takes raw data and makes it usable. Data manipulation tools handle the heavy lifting for you by making it easier to modify existing data to organize, read, and use it.

Tools identify patterns by sorting, moving, and rearranging data. Data manipulation doesn’t require data to be changed, but rather it can mean a method of reorganization to elicit trends or insights that may have otherwise been overlooked.

It’s about altering the relationship of data, either logically or physically. As such, tools that help to do this include filters, aggregators, string manipulation, regression, and other mathematical formulas.

There are also automated data manipulation tools, so rather than having to use a spreadsheet to carry out such functions, these functions can occur behind-the-scenes and result in the presentation of more understandable reports and dashboards.

Why Use Data Manipulation Tools?

Data manipulation is critical for process efficiency and optimisation. Beyond processes, data manipulation has a direct impact on what kind of business decisions get made because it provides a way to see data more clearly within a big picture.

Data manipulation tools provide value to organisations in multiple ways. Data manipulation tools deliver:

1. Data consistency

With data in a consistent format and structured, it becomes easier to analyse, interpret, and read data. With data automation solutions businesses pull data from multiple and often disparate sources, so the ability to format it in a unified way will add value for reporting.

2. Data removal

Since your business sources data from different locations, there’s a chance that you have redundant data. Data manipulation tools will spot and remove redundancies so that your data analysis is not negatively impacted.

3. Data projection

Data is at the heat of business intelligence, providing insights you need to make informed decisions. When you can make use of historical data for future projections and forecasts, you can use the past to your advantage.

4. Data interpretation

Complex data in a variety of formats poses a challenge for interpretation. With data manipulation tools, the complexity is removed since data can be formatted accordingly. Once it’s structured, it can be transformed into a visual experience so that it can actually be of value to the person reviewing the data.

5. Historical Review

With data manipulation tools, you also gain access to the history of your previous decisions and how they impacted your organisation. This way, you can always reference back when making decisions surrounding project deadlines, budget allocation, and the like.

6. Improved Efficiency

When you manipulate data, you are able to gain valuable information efficiently. Without data manipulation, you may come to less than optimal decisions based on redundant values or missing information. Data manipulate ensures accurate data, and thus, accurate insights.

How to Manipulate Data?

To get started with data manipulation, you’ll want to understand the general steps and order of operations.

  1. To begin, you’ll need a database, which is created from your data sources.
  2. You then need to cleanse your data, with data manipulation, you can clean, rearrange and restructure data.
  3. Next, import and build a database that you will work from.
  4. You can combine, merge and delete information
  5. Then analyse the data, to make all of this information come to life, and glean useful insights.

What are the Steps for Manipulating Data?

Some many essential tips and tricks allow you to get the most out of your data, even when it’s in Microsoft Excel, for example. Some of these include:

1. Functions and formulas: You can use essential math functions to make your numbers mean more. By merely writing essential math functions into the bar in Excel, you can add, subtract, multiply and divide data to see immediate results.

2. Autofill function: In the same vein, if you want to run an equation across multiple cells, but don’t want to keep retyping it, you can drag your mouse to the bottom right corner of the cell and drag it downwards to apply the same formula to multiple rows at a time.

3. Filter and sorting: With large datasets, it’s useful to be able to filter and sort information based on your needs. You can use this feature to save time in analysing data.

4. Remove duplicates: Duplicate data can affect your analysis. As such, you can remove duplicates by utilising the “remove duplicate” function on a spreadsheet once you’ve selected the data you want to work with.

5. Merging, Separating, creating and combining columns: To further organise data, you can connect, merge or separate columns and sheets of data.

How to Improve Data Manipulation?

The best and most efficient way to manage data manipulation is through software programs that offer advanced and automatic data manipulation features. Data automation tools like Solvexia offer benefits like automatically cleanse, map, validate, calculate, and store data with a live feed so you can say goodbye to manual data entry and low-value repetitive tasks. Additionally, with automation, reports can be generated and sent to specific people with no human interference. These reports help to run analysis, predict trends and create forecast models efficiently. Furthermore, with a robust system, all data is securely stored and allows for audit trails for governance and accessible data for collaboration.

Data manipulation within the finance industry can save a ton of time. Rather than having to copy-paste data from invoices or expense reports, software systems can handle data migration and reduce the level of human error, as a primary example.

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The Bottom Line

Data comes in many forms and is needed for business leaders to be able to make decisions. From marketing to sales, accounting to customer service, data is best utilised when it can be manipulated for any relevant purpose. Proper data analysis relies on the ability to perform data manipulation, which involves rearranging, sorting, editing and moving data around.

There are many different ways to execute data manipulation, from basic operations in Microsoft Excel spreadsheets to SQL to software programs like SolveXia, that can do the work for you by executing commands. Starting with data collection to data organisation, you’ll want to be able to take data from various sources and combine it to get the insights you need.

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