Spreadsheet vs. Database

Both spreadsheets and databases are fantastic tools for businesses, the key is to know when to use the right tool for the right purpose. We see companies struggling to scale business operations because they rely on complicated spreadsheets with multiple tabs containing numerous calculations that are reliant on data from several worksheets within a spreadsheet. Multiple employees use and enter new data into the spreadsheet leading to errors in the data as well as an enormous number of columns and rows. After time the spreadsheet becomes less manageable, which leads to a bottleneck in operations. At this point, the spreadsheet needs to be transformed into a database.

Before we get too far into the signs when a database should be used rather than a spreadsheet, let’s define what is a spreadsheet and what is a database.

Spreadsheet:

A spreadsheet is a software program that contains textual and numeric data arranged in a tabular format within rows and columns. The spreadsheet programs most used today are Microsoft Excel and Google Sheets. Spreadsheets are useful tools for making quick calculations or organizing data in a systematic format.

Pros	Cons
Low cost	Limited ability to share with multiple users
Quick learning curve	It takes time to learn complicated formulas
Multiple spreadsheets can be contained within one file	Manual entry into cells could lead to poor data quality as there are no rules to validate data input
Easy to integrate into other programs	Has a maximum number of rows and columns
Great for data formatting and visualization	Large data sets cause slow performance in the spreadsheet
Easy to set up for calculations	Not designed to scale
Flexible format to manipulate and augment data	Limited data security

Database:

A database too is designed to organize data electronically with a computer system or in a cloud application; however, the data is organized via a structured query language (SQL) in rows and columns within numerous tables. The database structure enables an efficient method for querying the data within the database.

Pros	Cons
Can handle large volumes of data efficiently	Must be technically proficient to use a database.
Can set rules for data input that allows for better data integrity	More expensive to use and maintain than spreadsheets
Designed to share data with multiple users
Enables data security
Ability to quickly filter and query data
Can setup automatic backups of data
Scalable
Can be integrated into reporting solutions to create automated KPI dashboards and drillable reports

When is it time convert your Spreadsheet into a Database?

Below is a list of business requirements that should have you thinking database instead of using a spreadsheet.

Data size has exceeded the capacity of the spreadsheet (Microsoft Excel limits are up to 1,048576 rows and up to 16,384 columns)
Multiple users are using or entering data into your spreadsheet
Data quality issues (you can set rules for data entry to help with data quality
Integration with an application (for data input and/or reporting/dashboards)
Data Security
Ability to have role level access to data (remember with a spreadsheet all the data on the worksheet is revealed)
Advanced ability needed to filter and query data
When you need better data organization
When you want data insights – databases allow data scientists to quickly explore data and conduct data analysis
When you want to aggregate data from not only your spreadsheet, but from other sources to create one data repository

Types of Databases:

Depending on what you are trying to achieve with organizing your data, there are many different database options.

Database Type	Description	Example
Hierarchical DBMS	The data is organized into a tree-like structure	IBM Information Management System Windows Registry
Object-oriented DBMS	The data is stored in the form of objects	ObjectStore Gemstone COP Smalltalk LISP Gbase
Relational DBMS	The data is stored in the form of tables	MySQL MS SQL Server IBM DB2 Oracle SQLite
Object-relational DBMS	Hybrid of relational and object-oriented DBMS	Oracle PostgreSQL IBM Informix Microsoft SQL Server Greenplum Database
Data warehouse	Data from multiple sources is combined into one comprehensive database	Google BigQuery Amazon RedShift Cloudera Microsoft SQL Database Snowflake
Cloud-Native Data Warehouse	The data warehouse uses compute and space allocated by the cloud provider.	Azure Synapse Amazon RedShift Google Big Query Azure SQL Database Snowflake
NoSQL document/JSON DBMS	The is stored as document collections, usually using the XML, JSON, BSON formats	MongoDB Redis Infinite Graph RethinkDB Amazon SimpleDB Cassandra
Data Lake	Used to collect unstructured data, such as videos, emails, pictures and social media posts	Hadoop Amazon S3 Snowflake
Data Lakehouse	A platform that combines a data lake and a data warehouse	Databricks SQL Snowflake

*Coupler.io https://blog.coupler.io/spreadsheet-vs-database/

Database Conversation Case Studies:

Our Scalesology team over the years have had the opportunity to transform businesses needing to convert paper or spreadsheets into a database/data warehouse/data lake to scale their business. Check out how we ensured these businesses scaled with the right technology:

The key to a successful implementation of a database application is working with an experienced team to build the right database solution to tackle the needs of today without limiting the scalability of what will be needed in the future.

Is it time to convert your business spreadsheet to a database? Wondering which database solution is right for your organization? Give us a call, we are here to help. Our team at Scalesology is ready to help you with all your data needs.