Spreadsheets by definition is a tabular form of application that stores data in a grid of cells. The origin of it can date back to the early days of newspapers and ancient accounting books. Quantitative information has long been stored in a tabular format. A database on the other hand is much more recent. It wasn’t until the 1960s that a database became a term we now know it today.
There are some major differences between a spreadsheet and a database. However, many people use them interchangeably. Admittedly, these two formats of data can be quite similar for a certain number of use cases. Though they beg to differ once reaching beyond simple use cases such as data entries, and bookkeeping.
When to use a spreadsheet?
When should you use a spreadsheet? Spreadsheets are particularly useful for use cases listed below.
Data modeling for smaller static dataset
Ask any financial analyst how much they use Excel or a spreadsheet program on a daily basis. Most entry-level financial analysts spend over 80% of their time working on a spreadsheet with a variety of financial data. To build a quick Pro Forma or DCF model, there’s really not a replacement for spreadsheets. It's quick, easy and flexible to work with. Data points can be manually entered into each cell. By a simple drag and drop, values can be auto-filled. Calculations can also be easily applied.
This however changes when someone has to deal with a larger quantity of data. If someone needs to extract information from more than 1 million rows of data, a spreadsheet becomes much less capable. The speed will drastically slow down and it will certainly consume a lot of computation resources.
- Financial data modeling
- Forecasting modeling
- Statistical modeling
Manual data entry
Accountants and people in operations have been entering data in spreadsheets for centuries. It is perhaps still the most flexible way of building a database till this day. Again, this changes when you have a thousand of sales people all entering sales numbers in one database. This is where a lot of proprietary software comes in and transforms the manual data entry process.
- Project management
- Calendar and scheduling
- Pipeline tracking
- Customer Relationship Management
Quick charting and data exploration
This is maybe the weakest spot to a spreadsheet’s prowess. There are thousands of charting tools on the market; some cater to a particular type of data feed, some are even designed for a certain type of data (time-series, text and even image). A typical spreadsheet program does not handle all types of data equally when it comes to data exploration and visualization. This is why you have hundreds of Business Intelligence software that charts your spreadsheet data in like a thousand ways. Nevertheless, they’re all built on tops of a spreadsheet!
- Line graph
- Pie chart
- Scatter plot
When to use a database?
Databases on the other hand are organized collections of data. They’re much more powerful in keeping records updated and cross-referencing data from different sources. When it comes to a large number of data records, they must be stored logically and systematically. Unlike spreadsheets, databases can be joined, and merged programmatically since the table schema is formatted and consistent.
Storing big data tables from different sources
A major limitation to a spreadsheet is the fact that it cannot store beyond a certain number of data records due to the memory capacity limit of our computers. Unlike a spreadsheet, a database has practically no limit on how much data it can store. Breakthrough technologies such as parallel processing and distributed computing make it possible to process big data across a network of computers. This way we can deliver some of the powerful use cases such as the following.
- Search engine (Google, Siri, E-Commerce)
- Data Aggregation (Online listing, Video publishing, Online Marketplaces)
- Predictive modeling (recommendation engine, demand sensing, risk detection)
- Data warehousing (RDBMS, data lake, file system)
- Data transferring (ETL, file sharing folder, data backup)
Supporting application development and deployment
Another key drawback about a spreadsheet is the fact that it cannot support an application that keeps requesting new data. A database however can support a variety of applications that process data at high frequency. This means real-time systems such as digital payment, automation and quantitative trading must be built on top of a database rather than a spreadsheet.
- Real-time analytical applications (Internet of Things, performance dashboards, health monitoring)
- Digital interface (Website, Desktop App, Mobile App)
- Real-time decision system (financial trading, fraud detection, face recognition)
- Transactional system (digital payment, crypto wallet, ERP system)
- Automation system (autonomous vehicle, business process automation, workflow automation)
Why use a database instead of a spreadsheet
Why would you use a database over a spreadsheet then? Consider the following things before setting up a database. Do you need to set up a system that
- can process large amounts of data much quicker?
- can enforce more strict data entry. Most data types?
- can be used by multiple users at the same time?
- can be accessed from different devices and applications — desktop, web, phone apps, etc?
- can be backed up safely, centrally, and automatically?
- can collect, merge data systematically?
- can integrate with a number of third-party tools for things such as reporting, charting, accessing, forms, etc. for most databases?
- can support different types of interface across devices such as a smartphone, desktop, media console, or anywhere that displays information on a digital screen?
If your system does need to fulfill any of the above goals, you should consider building a database to store your data.