This post is a brief ode to the spreadsheet, which paved the way for many to learn about how to organize information, collaborate, and analyze data. Spreadsheets played and/or still play a substantial parts of our analytical life. Data scientists can get a little smug in associating with technical tools, often inclined to discuss the latest and greatest. However, spreadsheets mostly likely still rule part of the workflow. It may be quickly inspecting the data, the best way to share information with non-technical people, or an accessible way to check results.
Spreadsheet was one of the first software programs that made data analysis accessible to non-programmers. It is still a stepping stone to understanding more complex systems and start with learning how to organize and interact with information. Humans are experiential creatures and spreadsheets is one visual way to “experience” data.
If an organization is driving towards becoming more scientific, data driven, or quantitative the first step can be to get everyone on board with using and sharing information in spreadsheet format. Data analysis and data science cannot escape the spreadsheet format. If interested in doing more data analysis then start simple — with spreadsheets. Below are more reasons to continue loving spreadsheets and/or to embrace them in your analytical development:
- Accessible – on almost every computer (including access via cloud)
- “Easy” to learn from loads of free tutorials online, books, workshops, and your colleagues
- Miles and years of experience in industry
- Equalizing technical tool across teams
- Relational nature of spreadsheets are replicated in more “advanced” tools, Portable theory of data with databases (mySQL, Access DB)