Why Move Data from Excel to a Relational Database?

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Excel allows business professionals to unlock the potential of their data – to a point. But when it comes to storing and manipulating large amounts of data, Excel can cause some problems.

Excel has many good points. It’s often used by specialists to work with data related to their particular field. But it can come with problems, such as sluggish performance and difficult team collaboration. Even worse, Excel is vulnerable to the most dreaded problem of all: the accidental deletion of important data.

All spreadsheet programs struggle with the sheer volume of information associated with Big Data. So let’s take a few minutes to consider these common problems and why a relational database is a good solution to them.

 - Dilbert by Scott Adams
source

Do These Excel Problems Happen to You?

Do you always have problems when working with Excel? Of course not. It depends on the complexity of the data, its size, and how often you have to perform operations on it.

Excel is primarily a spreadsheet, not a database. Therefore, if you need a data storage tool, think about whether Excel is a really good solution for your company. But what if you are already working in Excel and you’re running into difficulties? Read on to find out if you’re facing one of these common problems.

 

A Sudden Increase in Data

Perhaps you started working in Excel with a small amount of client data. Initially, data was scarce because the company was just starting out. After a few years, did you notice that the number of your clients had increased significantly? And did you also notice that you needed more data fields for each customer?

 - Dilbert by Scott Adams source

In other words, you could see that your Excel file was ballooning and would continue to do so. Getting operations to run on such a huge amount of data can be extremely burdensome. It takes a long time and it’s hard to get to the right information, filter the data you need, and sort the results.

The Relational Database Solution

In a relational database, large amounts of data are not a problem. Browsing will be faster, and searching, sorting, and filtering will return instant results. No more waiting for your data (and then explaining to the boss the report is late).

 

Chaotic, Uncoordinated Data

Another common problem is maintaining proper data structure and integrity. If you use a spreadsheet to store data, this is not easy.

You’ve probably seen something like this. Suppose the column used to store the date of a payment was set up for dates (i.e. 01-01-18). However, someone entering the data only knew the day of payment and decided to enter information using an integer data type (i.e. “1” for Monday)  – meaning to go back and fix it later, but forgetting to do so. Later, someone else decided to complete this payment date info and used a text data type to enter only the year and month (i.e. January 2018).

What’s the result if this scenario goes uncorrected? Eventually, any records with that missing information will be left out of relevant reports  – not a good thing!

Image result for dilbert dogbert doubting my data source

The Relational Database Solution

Marketers, analysts, and bosses often stress the need for high-quality data. Maintaining data integrity and structure is critical to data quality; a well-designed relational database makes this much easier. You can define specific data types for each column, which means that column will only accept information in that type or format. It’s impossible to insert a text data type into an integer field; you can’t insert incomplete data, either.

 

Changes to Data and Table Structure

This might come as a surprise, but it’s actually not a good thing to be able to easily alter the structure of a spreadsheet. Take the example described above with the missing date information. What if each person trying to correct the problem simply added another payment date column? It would be easy enough for them, but a nightmare for the person trying to make a report about payment dates! Plus, the constant changing of data input types can cause unreadable values.

The Relational Database Solution

A relational database can be designed to accept only one type of data in a column. Furthermore, it can require the inputted data to match a defined length and format. It can even make certain columns mandatory, which will not allow users to leave blank fields.

Data Encapsulation - Dilbert by Scott Adams source

 

Accidentally Deleted Data

Have you accidentally deleted data from a worksheet? I have. We all have. From the point of view of data protection, being able to easily delete data is not a desirable feature  – especially when we’re dealing with important data.

A closely-related problem is the mistaken deletion. Technically, the user meant to delete the data, but they didn’t understand what they were really erasing. Someone may think that a column contains obsolete data and delete it, not realizing that they’re removing valuable historical info.

The Relational Database Solution

When you properly configure a relational database, the database server will limit users’ access to the data to only what they need for their job. If your task is to enter data, you won’t be able to modify the structure of the data table. No user can add, delete, or change a column unless they have the appropriate permissions. This way, data stays safe and intact.

 - Dilbert by Scott Adams source

 

Working Together On Data

If you are the only person working on an Excel file, there are no problems; you always have access to it. However, what if a team needs to work on the same spreadsheet? That can be a problem, especially when the file is on the server instead of in the cloud. In that case, it’s first come, first served!

Suppose you work around this by having a local copy of the file on every team member’s workstation. This is great – until it’s time to merge all the copies into one master file. Determining who did what is not simple. If the same fields were modified, it may be practically impossible to figure out who did each modification!

The Relational Database Solution

When a team works on a centralized database, the collaboration process is much easier. Each person can independently access the database and work on the same data at the same time. There’s no lag, and the data is constantly being updated.This allows everyone to complete tasks in less time.

 

Data Loss and (In)Security

Imagine this: The file containing your clients’ information exists in only one place. Even if you copy it, you may lose some of the most recent data. What if someone deletes some data? Or erases the entire file?

The more people with access to a file, the more likely it is that something bad will happen. It’s important to make backup copies, but it’s even more important to ensure the security of your data. Unfortunately, spreadsheets offer only limited data security controls. As we’ve seen, accidentally deleting all or part of a file is a real possibility!

The Relational Database Solution

Relational database servers secure your database files quickly and easily. One of the main ways they do this is by assigning permissions to individual users. Passwords and encryption protect the sensitive and vital data in the database.

 - Dilbert by Scott Adams source

 

Why a Relational Database is The Solution

There are some signs that your business is outgrowing Excel as a data storage option:

  1. There’s a large (and expanding!) amount of data
  2. You’re having problems with chaotic, unorganized data.
  3. You want to enable multiple people to work on the same data at the same time.
  4. You’re worried about data integrity or security, or
  5. You’ve actually experienced data integrity and security problems.

Excel is a very useful tool for calculations, but it’s not a database. If you are dealing with any of the above problems, consider transferring your data from Excel to a relational database. It could save you time, nervous strain, and money. There are some good free database servers available for commercial use; go ahead and try one of them!

 

Dorota Wdzięczna

Data Science Writer @ Vertabelo

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