Excel data validation

Strategy   |   Paul Warburg   |   Aug 4, 2022

What Is Excel Data Validation?

Data validation in Excel is a feature that allows you to control the type of data entered into your worksheet. For example, Excel data validation allows you to limit data entries to a selection from a dropdown list and to restrict certain data entries, such as dates or numbers outside of a predetermined range. Data validation can also help you control formulas and the input from those formulas. You can even craft custom Excel data validation messages that help guide users toward the right data entry when they hit a limit. As a result, Excel data validation helps reduce the amount of unstandardized data, errors, or irrelevant information in your worksheet.

It’s a helpful feature, especially when widely sharing an Excel worksheet with others for completion. Many data analysts find data validation in Excel to be beneficial when they are working with many users or with strict guidelines in data entry. In addition, data validation in Excel can help save analysts valuable resources that are spent when the data isn’t input correctly. Overall, data validation in Excel is a beneficial feature, but even beneficial features have limitations that can impede its ability to help users.

The Limitations of Data Validation in Excel

Using data validation in Excel to set certain restrictions in your worksheet can help guide users toward more accurate data entries, but ultimately, these restrictions can easily be bypassed. If a user copies data from a cell without Excel data validation to a cell with Excel data validation, the validation is destroyed (or replaced). So despite using data validation in Excel to control input, analysts can still be left with messy data sets because of this way to bypass any restrictions. In addition, if the validation is destroyed or replaced, it could affect how future data entries are input and generate future analysis complications.

What’s more, having the ability to control data collection is a rare occurrence for most business users; more often than not, data is collected from various systems or third-party sources without being able to control how that data is entered and managed. Instead, you’re simply given messy spreadsheets and must make do with them as best you can. This situation is common for many modern businesses, and it means that the analysts don’t have any control over how data is input. When analysts are handed a data set, they aren’t able to use data validation in Excel at all. But they still want to be able to clean and control the data in some form in order to use analysis tools properly.

In both cases, when Excel data validation breaks down or when there’s not an opportunity to use it in the first place, validating data within any given Excel spreadsheet becomes much more difficult. Identifying unstandardized data requires tedious searching and replacing. Spotting errors in rows and rows of data can feel like finding a needle in a haystack. And overall, the process adds precious time to the data preparation process, which is already widely understood as the most time-consuming part of any analytics project.

In instances where users can’t rely on Excel data validation and need to quickly prepare faulty data, many are turning toward intelligent data preparation tools. Intelligent data preparation platforms allow users to more quickly validate their data and prepare it for use in data modeling  or visualization projects with great assurance of its accuracy. Data preparation tools are necessary for data sets that use data validation in Excel and can also be a better alternative as well.

The Need for Data Preparation

Certainly, there will continue to be many instances where data validation in Excel is a reliable and helpful feature. However, as data grows beyond the realm of our control, and Excel data validation becomes less feasible, having other resources in mind to prepare messy data is critical. The key is to use data preparation tools. Data preparation can reduce the amount of time spent analyzing and mitigate the complications that could come from using data validation in Excel. Data validation Excel tools can be extremely valuable, but analysts are better able to benefit during the analysis process from these preparation tools over data validation in Excel. Even when data validation in Excel is possible and beneficial, it can still be necessary to prepare the data properly to eliminate anything that went wrong during the validation phase. For example, data preparation is necessary if a user was able to override the data validation in Excel.

Data preparation tools focus on taking the raw data and making it usable for data analytics. This process can involve removing null values, standardizing data entries, or eliminating duplicates. While data validation in Excel can help with some of these issues while the data is being entered, the tools can be overridden. That’s why data preparation tools are crucial with large and growing data sets. Preparing the data will save analysts time and make tools like data validation in Excel more beneficial.

Data Validation In Excel Meets Designer Cloud

When it comes to data preparation tools, Alteryx Designer Cloud is widely recognized as the leader in data preparation. Its visually-driven platform not only allows users to easily spot data quality issues, but also allows anyone in the organization to speak the same language, which improves transparency about how particular data sets have been transformed. Designer Cloud is a data preparation tool that can save analysts time and extra effort. The Wrangler works well with data validation in Excel to bring these two time saving processes together. In addition, the Wrangler can be used in place of data validation in Excel for those data sets that are overwhelming and detailed.

We’d love to chat with you about how you could use Designer Cloud as an alternative to Excel data validation when applicable or how to incorporate our data preparation tool with data validation in Excel. Schedule a demo of Designer Cloud today.