Manipulation of Data: Tools & Tips for Success

97% of organisations are investing in big data and AI to utilise their data. This figure shouldn’t be surprising because the need for manipulation of data to make it usable is irrefutable. By looking at data manipulation examples, we’ll see why data manipulation is so crucial.

In this article, we’ll look at the types of data manipulation and see how data manipulation tools provide you with exactly what you need to succeed.

Coming Up

What is Data Manipulation?

What are Data Manipulation Examples?

Why is Data Manipulation Important?

What is Data Manipulation Language?

What are the Steps in Data Manipulation?

How do you Manipulate Data in Excel?

What is the Difference Between Data Manipulation and Data Modification?

What are Data Manipulation Tools?

What is the Benefit of Data Manipulation Tools?

What are Best Practice Tips for Data Manipulation?

Closing Thoughts

What is Data Manipulation?

Data manipulation is the process of changing and reformatting data in order to make it readable and organised. For example, when you have a spreadsheet filled with names and you want to organise alphabetically, you will use the sort order function. This is data manipulation in its most basic way.

Along with making data more understandable, data manipulation is also used for forecasting purposes. A few types of data manipulation include: aggregation, row and column filtering, string concatenation, mathematical formulas, regression analysis, and classification.

What are Data Manipulation Examples?

Here is a look at a few more data manipulation examples:

Web data:

Companies may manipulate data in order to see what website page is the most popular. This way, they can drive more traffic to the page or use those insights to optimise other webpages on their website.

Stock market analysis:

Stock market analysts manipulate data continuously in order to forecast how stocks will perform in the future to know how to allocate their investments.

Pricing patterns:

Companies use data manipulation to assess how much their products cost and forecast pricing patterns.

Merge data:

If it’s the case that you have disparate data across spreadsheets, you may need to merge the sheets and connect your files in order to better interpret the information you have at hand.

An easier way to merge and collect data in a centralised location is to make use of finance automation software that will connect to your systems to pull and format data.

Why is Data Manipulation Important?

With the vast amount of data that your business has its hands on, it only makes sense to make that data usable for insights. Through these insights, you have what you need to make informed and better business decisions.

Data manipulation is just one step of the process in making data usable, but it’s often the first and most important one to get going. Data manipulation makes it possible to:

1. Review the past:

Data manipulation helps to understand prior accounting periods and business decisions that were made. This helps to better budget and set deadlines for projects, for example.

2. Enhance efficiency:

Data manipulation allows you to see the quality of your data in the first place. By doing so, data becomes more meaningful because you have what you need to remove any redundant or irrelevant data from the mix.

3. Create value:

When you clean, update, and convert data from your database, you are able to do more with it. This is the greatest advantage - being able to use your data to answer questions, resolve issues, and move forward with confidence!

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By performing data manipulation manually, you’ll spend a lot of time collecting, cleansing, and transforming data for your needs. Along with the time it takes, manual data manipulation is highly error-prone due to the nature of the work.

With automation software, you can streamline the data manipulation process, prevent errors, and maximise your productivity.

What is Data Manipulation Language?

Data manipulation language (DML) is a computer programming language designed for data manipulation, as the name implies. It is used to clean and map data.

For example, Structured Query Language (SQL) is a popular data manipulation language that is used to insert, select, and update records in a relational database.

You may be thinking, “I neither have the time nor the resources to learn how to use such a programming language.” Don’t worry - there are out-of-the-box, no-code software solutions that automate data manipulation needs so you don’t have to!

What are the Steps in Data Manipulation?

Whether you choose to use a data manipulation tool or manage your data manipulation desires by hand, you’ll run through a set of clearly outlined steps. These include:

1. Pulling together data from every data source (When done manually, you can imagine how long this can take, and you may overlook an important data source. That’s why it’s more efficient to use a software that connects all your data systems and pulls together your data).

2. Clean and organise data (In order to be usable, data has to be formatted and structured in the same way).

3. Build a database that you can work with

4. Merge information and remove any unnecessary data for the task or question at hand

5. Conduct data analysis to reap insights from the data

How do you Manipulate Data in Excel?

When conducting the manipulation of data in Excel, you’ll most likely rely on R and Python. While it could be a useful tool, it’s certainly not the most efficient in terms of your time.

Additionally, it requires that you know programming languages well. If you don’t, you’re relying on a key person (or persons) on your team to handle your data manipulation tasks.

Imagine if that person is out sick or leaves the company - then, you’ll be stuck with a lot of data without any way to use it. With Excel, you have to be keenly aware of formulas and functions.

Alternatively, if you’re looking to save time and prevent manual mistakes, then you could (and should) make use of data manipulation tools that automate the data manipulation process for you.

By doing so, you not only expedite the process, but you also prevent key person dependencies and bottlenecks from occurring.

What is the Difference Between Data Manipulation and Data Modification?

When working in the world of data manipulation, you’ll come across data modification, too. While it may feel like the two are interchangeable, they are actually two different things.

Data manipulation is when you organise data differently to make it more readable. Data modification changes the actual data values.

Data manipulation can be performed with formulas, functions, filtering, and autofill. You may end up removing duplicates or combining data. On the other hand, data modification will change the raw data itself- i.e. if x=5, then data modification may make it such that x=7.

What are Data Manipulation Tools?

In order to make data readable through data manipulation, there are tools you can use to get what you need. Data manipulation tools help to organise and order data without making big changes. According to your needs, the tools will adapt data for use.

Data manipulation tools can filter rows and columns or reclassify data. Automation tools connect to your existing data sources and pull in the data you need to either execute processes or perform analysis.

By using such tools, you can rest assured that your data is going to be stored safely, manipulated without mistakes, and cleansed to remove duplicates or missing records.

What is the Benefit of Data Manipulation Tools?

Given the immense importance of the manipulation of data in organisations, data manipulation tools are highly productive and efficient. These tools make it possible to rearrange your raw data in order to apply it into different target systems in a compatible manner.

By utilisting data manipulation tools, you get to reap the advantages of having:

1. Consistent Data

Having a consistent data format makes it possible to constantly and simply review, organise, and analyse your data. Since you have data coming into your organisation from different systems, it has to be formatted in the same way to make it readable.

2. Clean Data

Along with properly formatted data, data manipulation tools will help you to remove any redundant data or unwarranted information. This way, you will have clean data so you can apply it to whatever type of analysis you want to perform.

3. Value Generation

When you are able to manipulate data for use, it provides you with the major advantage of being able to gain real time and deeper insights from your data. This helps with making important business decisions that can help to make your business function more efficiently and effectively.

What are Best Practice Tips for Data Manipulation?

When utilising data manipulation tools to execute the manipulation of data, or even if you’re doing it manually, then there are some best practices to remember to make the most of it.

Keep in mind:

• Defining your needs before you get started
• Review data manipulation tool options and select what’s best for your business’ needs
• Understand mathematical functions
• Filter your data
• Revisit data manipulation tools as your business needs change and grow
• Make use of data visualisation tools to better understand insights
• Utilise automation tools to do the heavy lifting for you!

Closing Thoughts

With the manipulation of data, you can make smarter business decisions. Sure, it’s possible to conduct data manipulation in Excel or in other manual ways, but you’ll be all the wiser to make use of automation solutions for data manipulation purposes.

This way, you get to save time, prevent costly errors, and streamline your business processes to get the most from data analytics and glean insights from the raw data that is available to you.