Published on

November 24, 2023


The Power of AI Data Transformation for Excel

Are you an avid Excel user? Find out how to use Excel to transform your data, and whether it’s the best tool for the job.
Craig Wisneski
Co-Founder & Head of G&A, Akkio

The rapid pace of data growth and the ever-increasing complexity of business analysis are daunting challenges for today's Excel users. In fact, more than 500 different functions have been added to Excel, for organizing, filtering, and manipulating data.

Furthermore, the size of the Excel Community—with over 40,000 user-generated discussions on best practices—highlights the platform's popularity and the wealth of experience users have with the tool.

For businesses using data analytics and machine learning to understand their customers, position their products, and make better decisions, Excel is often the platform of choice for storing and "prepping" data. Data transformation is a crucial step in the data analysis process, and Excel provides many ways to transform data—including some that are not immediately obvious.

In this article, we'll explore the concept of data transformation for Excel, how it can help your business, and understand the different ways you can transform your data using default Excel and other tools.

What is data transformation?

Data transformation is the process of editing, processing, and reformatting existing data into a more effective and useful form. Data transformation in Excel uses various functions and tools to sort, clean, organize, or structure the data into a more relevant form for further use.

Data transformation can be used to:

  • Convert data from one format to another (e.g., from text to number)
  • Extract specific information from a larger dataset (e.g., identifying all records that contain a given word or phrase)
  • Restructure data for easier analysis (e.g., transposing rows and columns)
  • Combine multiple datasets into a single file

Data transformation is a crucial step in the data analysis process because it allows you to take data that may be in an unusable format or that contains errors and convert it into a form that can be used for further analysis.

For example, imagine you have a customer list in an Excel spreadsheet, but there are some missing customer names. Data transformation can be used to fill in the missing customer names by using a function like VLOOKUP (which looks up a value in one column and returns the corresponding value from another column).

Or, there may be missing sales data - one could impute the missing data by using the AVERAGE function to calculate the average sales for that product across all regions, or use a more complex, and potentially more accurate, forecasting technique.

Examples of data transformation in Excel

The most common transformations are simple formatting options like changing a date to another format like MM/DD/YY or deleting a row or column. 

Virtually every real-world dataset will require some level of data transformation to make it ready for analysis. The process of data transformation can be simple or complex, depending on the nature of the dataset and the desired outcome.

In this section, we'll share some common data transformations that are performed in Excel. We'll also provide some tips on how to go about performing these transformations.

Simple Data Transformations

Using Excel, you can easily apply a transformation to a range of data using formatting options, such as changing the date format or deleting a row or column. The raw data is usually imported into Excel through a CSV, another Excel File, or by connecting to external data sources in Excel Power Query. You can also use Power Pivot, VBA, and SQL queries to perform more complex transformations. 

To get started with simple transformations in Excel, begin by accessing the Data tab on the Home ribbon. Here, you'll find commands for getting & transforming data (Power Query) and working with pivot tables and other types of analysis. In the Power Query Editor, you'll have access to various transformation functions like add column, append, pivot or unpivot, and more to help you transform your data.

Additionally, there are built-in dialog boxes that allow users to easily add columns and rows of data from an external source into an Excel table. 

One of the most common data transformation tasks is to format your data columns into a consistent date type. This is often necessary when working with data that comes from different sources. For example, you might have a column of dates that are in DD/MM/YYYY format, but you need them to be in MM/DD/YYYY format for analysis.

To change the date format in Excel, select the column of dates that you want to change. Then, go to the 'Format' menu and select 'Cells'. In the 'Number' tab, select the 'Date' category and then choose the desired format.

Another common data transformation is to delete rows or columns that are not needed for analysis. For example, you might have a dataset that includes information on multiple years, but you only need data from the current year. In this case, you can delete unwanted rows or columns.

To delete a row or column in Excel, right-click on the header of the row or column that you want to delete. Then, select 'Delete' from the menu.

Complex Data Transformations

Some data transformation tasks are more complex, such as calculating a mean, finding the customer lifetime value, or matching data from two columns. These transformations often require the use of formulas or functions.

To calculate the mean in Excel, select the cells that you want to include in the calculation. Then, go to the 'Insert' menu and select 'Function'. In the 'Select a function' box, select 'AVERAGE'. Click 'OK' to insert the function.

To find the customer lifetime value, you could create a formula that uses the 'FV' function. This function calculates the future value of an asset, which could include customers. A more accurate approach would be to create a customer lifetime value model that incorporates key factors such as purchase frequency, average ticket size, churn rate, and other variables.

To match data from two columns, you can use the 'VLOOKUP' function. This function looks up a value in one column and returns the corresponding value from another column.

What are Excel functions? How do they help with data transformation?

Excel functions are preset formulas that can perform various mathematical, statistical, and logical operations. In many cases, these functions can help streamline data transformation processes.

Some of the most commonly used Excel functions for data transformation include SUM, VLOOKUP, IF, MATCH, and CHOOSE. Each of these functions can be extremely helpful in different ways.

For example, the SUM function can be used to quickly add up a column or row of numbers. The VLOOKUP function can be used to find specific values in large data sets. The IF function can be used to create conditional formulas. And the MATCH function can be used to find the location of a specific value in a data set.

The CHOOSE function is also often used in data transformation. This function allows you to select a value from a list of values, based on a specified position.

The DATE function can be used to insert the current date into a cell. And the FIND and FINDB functions can be used to locate specific values in a data set.

These are just a few of the many Excel functions that can be used for data transformation. To learn more about Excel functions, and how they can help streamline your data transformation processes, check out the Microsoft Excel Function (by category) Support Article.

What is the Get & Transform tool or Power Query?

Power Query, as the name implies, is a powerful tool for extracting, transforming, and loading data for analysis. It is very useful for getting easy, meaningful insights from your data and maintaining data workflows. By default, it has been part of Excel since Excel 2016, and was available as an add-in as early as 2010.

So how does this process work? The Power Query for Excel help guide explains the process in detail, but we'll give you a quick overview here. Essentially, the features involve three main steps:

  1. Extract: This is where you extract data from various sources, including databases, text files, and Excel worksheets.
  2. Transform: This is where you transform the data into a format that is more amenable to analysis. This may involve filtering, combining, and grouping data.
  3. Load: This is where you load the transformed data into a destination, such as an Excel workbook or database.

There are many different ways in which you can transform data with Power Query. For example, you can filter data to only include certain rows or columns, combine data from multiple sources, and group data by time period.

This process is very helpful for loading large databases into a single table, making files more digestible and easy to understand, converting raw sales data into data that can provide insights, grouping data from smaller time periods into bigger time periods, and vice versa.

What is the best way for you to transform your data?

Excel is the world's most popular data analysis tool, used by over 1 billion people worldwide. While not as powerful as a coding tool like Python, it's still a versatile tool that can be used for everything from calculating basic formulas to building complex models.

However, Excel is not a silver bullet - there are some limitations to using it, especially when it comes to large data sets or data that is constantly changing. For example, using formulas and functions can be time-consuming when you're storing, prepping, or analyzing large amounts of data, and you might need to redo a lot of work or spend time troubleshooting if your formulas are broken or not applied correctly. 

If you have multiple people working on the same sheet, the chances of error are much higher, and even with the Power Query tool, there are limitations when you're analyzing more than a million rows of data. Excel also isn't very user-friendly if you have data from multiple sources or data meant to be shared/used by multiple users.

While Excel is not a one-size-fits-all solution, it is still a valuable tool that can be used for many purposes. If you're using Excel for your data storage and prep, with the intention of using it for AI later, it's better to use a tool that is built for machine learning.

AI is used now more than ever in all kinds of businesses. Tools like Akkio have made AI and ML technologies more accessible to businesses and removed the need for a dedicated team of data scientists - it can be used by anyone.

Why you need a tool like Akkio

Machine learning has become a business imperative, with over 80% of executives saying AI will enable them to obtain or sustain a competitive advantage. If you're not using AI, you're at risk of being left behind.

Akkio is a no-code platform that makes AI accessible to even those without any data science knowledge, it takes mere minutes to set up ML models. Akkio's data prep feature makes it easy for you to prepare your data for success on the same platform and optimize it for better and more accurate predictions and analysis.

With Akkio, you can quickly and easily build machine learning models without any coding, making it the perfect tool for those who want to use AI but don't have the resources or expertise. Akkio is also constantly improving, with new features and enhancements being added all the time, so you can be sure you're always using the best tool for the job.

How Akkio works for data transformation

Akkio is an end-to-end machine learning platform that makes it easy to prep, train, and deploy models.

Regardless of the data types, formats, and sources, Akkio provides automated data transformation capabilities that make it easier to process raw data. This includes ingesting data, handling missing values or differing data types, splitting datasets into training and testing sets, encoding multi-value categorical features, and transforming data into a numerically understandable format.

Marketing and sales teams, for instance, can connect their Hubspot or Salesforce account to Akkio, and within minutes, start using machine learning to score leads, optimize their sales funnels, and increase order value. In the customer success domain, Akkio can be used to predict churn and proactively take steps to reduce it. For HR teams, Akkio can be used to predict employee attrition and identify at-risk employees.

Akkio is also built for collaboration, with features that make it easy to share data and work together on projects. With Akkio, you can easily give other members of your team access to the data and models you've created, and they can contribute without needing to know how to code.


Excel is far and away the world's most popular data analysis tool, but its data transformation features are no panacea - they're often time-consuming and error-prone.

Akkio is a no-code platform that offers powerful data transformation capabilities, making it easy to prepare your data for machine learning and get accurate predictions and insights.

Whether you're in marketing, sales, customer success, finance, or HR, Akkio can help you transform your data and make your business more data-driven. To learn more about how Akkio can help you, sign up for a free trial today.

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