I often hear people referring to Microsoft Excel as a database—but that couldn’t be further from the truth. Compared to true database systems, Excel is insecure, hits size limits too quickly, and invites corrupt data. Here are six reasons why I stopped trusting Excel with my important information.
Data validation isn’t as robust as you think
I’ve often fallen into the illusion of control in Excel. While the program’s data validation tool lets you define what can go in a given cell, it’s more of an advisory measure than a mandatory one. A true database system uses a strict schema to define exactly what type and format of information can be entered into any given column, guaranteeing data integrity.
Excel offers no such constraint. If someone is determined to enter bad data, they can simply paste values over a validated range or open the file in another program to completely bypass the rules I spent a long time setting up. This breakdown of data trust means that any future calculations or reports are instantly unreliable, undermining the entire purpose of data collection and storage.
Excel has size limits
Excel’s absolute row limit is 1,048,576. However, in reality, the program’s breaking point is far lower due to memory constraints. This contrasts with database engines, which are designed for scalability.
Where databases store data efficiently and only load the necessary records when processing a query, Excel must load the entire workbook into my computer’s RAM to function. Even if a workbook is only half its maximum size, the fact that it’s likely to contain complex formulas, conditional formatting, external links, and other resource-intensive properties quickly makes it sluggish, causes it to freeze, or—in the worst-case scenario—results in it crashing altogether.
This fundamental difference in how the two software types are built means that, whereas a database can scale to handle millions of records without any issues, Excel hits a performance ceiling much sooner than I’d hoped.
Contradictory data is a problem
I often share my dataset with a large team of people, and when I do so in Excel, concurrency control—ensuring multiple people can access the same dataset without corrupting it—becomes a real issue.
True database systems have sophisticated mechanisms, such as temporary record locking, built in to make sure that if two or more people try to edit the exact same piece of information at the same time, only one person’s change is processed cleanly. Even though modern versions of Excel do support co-authoring, allowing many people to edit simultaneously, it’s still a primitive and file-centric solution that lacks true transactional integrity and often leads to “save conflicts,” requiring me to manually reconcile two separate versions.
Excel forces data repetition
Database systems are designed to manage data efficiently by breaking it down into multiple tables to prevent redundancy—also known as relational structures. This prevents us from repeatedly entering the same information everywhere and guarantees that every related piece of data is correctly linked.
To achieve a similar system in Excel, I would need to use functions like XLOOKUP, which, while successfully connecting data, can’t enforce a relationship. In other words, if I delete a client’s information in one spot, all the other linked records instantly break and show an #N/A error. Since this structure is so fragile, I need to resort to re-entering the same information across multiple rows, instantly guaranteeing data redundancy.
Excel isn’t secure
My primary concerns when sharing databases are privacy and security, and Excel falls short in these areas. Security in Microsoft’s spreadsheet software operates only at the file level, which is simply inadequate for data intended for sharing. Even when I protect individual cells or entire worksheets with a password, this is a weak measure intended mainly to prevent accidental changes, and anyone with even moderate technical skills can bypass these protections. What’s more, Excel’s Track Changes feature is feature-limited and easily bypassed, so I can’t always keep a reliable record of the tweaks made to my spreadsheet.
In contrast, databases don’t rely on easily broken file passwords. They use strong network authentication and role-based access control (RBAC), meaning I can create rules that dictate not only who can access the database but also which fields they can interact with. Databases also handle data encryption, protecting information both at rest and in transit. Moreover, database systems typically include centralized, server-based audit logging, so I can reliably track who changed what data and when.
Reporting and querying are time-consuming
Excel’s final failure point when compared to databases is efficiency. When a spreadsheet is treated like a database, creating meaningful, complex reports can take too long and require significant expertise. For example, a request to “find all the sales made in North America by employees hired after 2021 where the order value exceeded $1,000” requires a complex combination of PivotTables, filters, and array functions, or power-user knowledge of Power Query and Power Pivot. Even if I were to use Copilot to execute this task, it’s prone to error and misinterpretation.
This is where databases truly prevail. They use a query language like SQL (Structured Query Language), which means that rather than telling the software how to find the data, I can simply tell it what I want. On top of this, SQL operates directly on the data server, so reports are faster to make, easier to maintain, and are inherently more reliable than AI-generated analyses.
Don’t get me wrong—Microsoft Excel is a great tool for everything from everyday calculations and complex data analysis, but it’s simply not a database. While advanced features can address some data organization and reporting needs for small-to-medium-sized projects, its limitations in security, scalability, concurrency, and data integrity make it unsuitable for large or critical datasets. In these scenarios, you need a true relational database, such as Microsoft SQL Server, to ensure data accuracy and reliability.
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Microsoft SQL Server is an enterprise-grade Relational Database System that scales far beyond Excel. It uses Structured Query Language on a central server to manage critical data and ensures superior data integrity through strict schemas, high security with role-based access control, and excellent multi-user concurrency.