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You and your company are “data-driven”, right? This certainly means using the most advanced data processing technology. Having a database and querying it using SQL seems like a minimum requirement in this world.
But what if there are times when a straightforward spreadsheet outperforms a carefully written query by a mile? Here are some real-life situations.
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1. Your data is miniature and/or disposable
Examples of such data are:
- CSV file from the supplier
- A financial model with several hundred rows
- Straightforward budget forecast
- Task tracking
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It would almost be strange to load such data into a database and run a few queries. This is an exaggeration. Open it in Excel or Google Sheets and you can quickly filter, sort, and perform calculations and transformations. You would probably still configure tables, indexes, and schemas in the database.
2. You need speed and zero configuration
If you don’t have a relational database, setting it up requires:
- A working database, local or in the cloud
- Defined tables and data types
- Appropriate permissions
With spreadsheets? Nothing. Even if installation is required, it is much faster. But you probably don’t need to install anything because your computer probably comes with a spreadsheet program pre-installed or you can easily utilize the cloud version.
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You just click on the file and get going – especially if you just want to see what’s in it or do some quick calculations.
3. You must cooperate
Databases can support collaboration, but require setting permissions, defining roles, and knowledge of SQL.
For example, collaborating in Google Sheets is much easier:
- Share the link with your colleagues
- Add comments
- Track file changes
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If your colleagues aren’t on a data team (e.g. finance, marketing, operations), they probably don’t utilize SQL – it doesn’t matter for spreadsheets.
4. You want to visualize and present data
SQL is used for querying and analyzing data, but it is not suitable for presenting results. You usually export the results of your queries elsewhere. Ironically, we often tried to avoid spreadsheets.
If data visualization and presentation is vital and analysis can be performed relatively easily in a spreadsheet, choose it over SQL. Spreadsheets are both computational and presentation tools.
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Typically, you choose spreadsheets over SQL if you need:
- Charts for presentations
- Pivot tables for executives
- Financial forecast model
5. Your work is iterative and cluttered
Examples of such work include building models, brainstorming, and testing assumptions. I would utilize spreadsheets here.
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You can utilize them for:
- Brainstorm financial scenarios -> copy the sheet, correct a few cells and see the results instantly
- Test assumptions -> write two formulas, compare them, remove the weaker one; no schema migration required
- Quick what-if models -> create a coarse version in a spreadsheet before formalizing it in SQL
- Ad-hoc annotations -> color coding, leaving comments, highlighting data
- Iterate the formula -> easily change =SUM(A1:A52) to =AVERAGE(A1:A52); there is no need to rewrite queries or check syntax
6. Your audience doesn’t know SQL
Company leaders, project managers and external clients are much more likely to open a spreadsheet than a database.
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Spreadsheets give them something they can read, modify, and share without having to learn a programming language they didn’t intend to learn.
7. You consider cost and access
Maintaining relational databases in production environments can be steep; think cloud storage, compute resources, and admin time.
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Spreadsheets are free or almost free. If your dataset and analysis is something that a spreadsheet can handle relatively easily, then choosing it over SQL avoids additional costs.
8. You need brisk data entry
Databases are not designed to easily manually enter data line by line. You’d either have to write a query or import data from, guess what, a spreadsheet.
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Entering data directly into a spreadsheet is easier if you want, for example:
- Record meeting notes
- Collect some sales leads
- Track tasks and deadlines
Application
The best way, of course, is to combine spreadsheets and SQL. The latter is intended for intensive data processing, transformation and analysis, especially if it involves repetitive tasks; then it pays to create a logical query and just run it again if needed. If you’re just starting out or need a refresher, this is it SQL cheat sheet it’s a great resource for basic concepts you’ll actually utilize.
Spreadsheets are better when you are looking for flexibility, quick understanding of data, lighter analysis, quick data visualization and sharing.
Nate Rosidi is a data scientist and product strategist. He is also an adjunct professor of analytics and the founder of StrataScratch, a platform that helps data scientists prepare for job interviews using real interview questions from top companies. Nate writes about the latest career trends, gives interview advice, shares data science projects, and discusses everything related to SQL.