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Spreadsheets are one of the most widely used tools in business. But with more businesses stressing the need to learn programming languages such as Python to land data analytics roles, a common question being asked is, “How important is it to learn Excel?”.
We’ll answer this question and more.
Yes, Excel is still widely used in business. For many business analysts and managers, the ability to use spreadsheet technologies such as Excel or Google Sheets is considered common knowledge.
There are many alternatives to spreadsheet technologies, namely:
SQL, R, and Python can be considered more powerful alternatives to Excel that enable higher volume data storage, more advanced features for data analytics and data visualization.
Although powerful tools exist such as SQL and Python, nothing tops spreadsheets when it comes to quick and accessible data analysis. Of all the existing data analytics tools, spreadsheets remain the most accessible and traditional tool for business analytics; many businesses have used spreadsheets since the start to store and analyze data, and Excel is the easiest to pick up and use for most users.
In 2019, market research indicated that roughly 54% of businesses use Excel—and this doesn’t include other spreadsheet applications. Worldwide, more than 2 billion people use spreadsheet technologies such as Excel and Google Sheets. Companies hire candidates with Excel skills because the use of spreadsheets is considered standard for daily business analytics.
Spreadsheets make it easy to manage data and visualize information. Of the available options for data analytics, Excel offers arguably the best balance of power and accessibility; spreadsheets provide powerful functionality such as graphs, pivot tables, and formulas, but are more accessible than advanced tools such as SQL.
Excel in particular is also compatible with many other popular data analytics tools, such as Google Sheets, and can interface with more advanced data analytics options such as Python’s pandas package—as both are compatible with the popular “csv” data storage format.
Ultimately, Excel is the most easy-to-learn and widely-used data analytics tool for businesses, and that’s likely not going to change.
Businesses use Excel to store, visualize, and analyze data. Some example business use-cases include:
Excel is also widely supported by other data pipelines and automation tools. For instance, businesses can pair Excel with Zapier to automate data-gathering; a client can complete a form with their contact information, and Zapier automatically populates a row in the relevant Excel sheet with their information.
Although Excel is popular and accessible, it’s not always the optimal tool for businesses.
In the 1990s, a tax accountant in Fidelity Investments made a costly mistake. The accountant accidentally left out a minus sign. This incident resulted in a $2.6 billion error. Although this incident didn't affect shareholders, it demonstrated reliance on technology and the importance of double-checking work.
The larger a database, the slower Excel becomes. At some point, a business’s databases may become too big and complex for Excel to handle; at that point, analysts must transition to database-specific tools such as SQL and Python.
Database technologies such as SQL are designed to reduce the chances of errors when storing, migrating, or exporting data. Excel does not come with very robust tools and features to ensure reliable data. For smaller collections of data, Excel works just fine, but the tool becomes riskier and less reliable with larger databases.
If a business needs to use advanced statistical methods such as machine learning or predictive analytics, it’s best to use more powerful tools offered by programming languages such as Python. Although Excel provides some features for statistical analysis, such as linear regression, it is not the best tool to use for more complicated data science tasks.
Excel is the Swiss army knife for business data analytics. It is a powerful yet accessible jack of all trades solution for business needs such as quick data storage and manipulation, collaboration with data, automating tasks, and more.
Although a business may want to use more advanced tools as they scale, most businesses can save time, get the answers they need, and ensure smooth operations on the day-to-day by using spreadsheet technologies.
edX offers free online data analytics courses and Excel courses. Whether you’re looking to change your career or wanting to improve your data analytics skills, you can use the edX platform to learn data skills from top institutions such as MIT and Georgia Tech.