Say Goodbye to Spreadsheets in the Enterprise

Data Basics, Use Cases & Projects Catie Grasso

In the age of data-driven decision making, the familiar spreadsheet has been the trusty companion of many business leaders for decades. However, recent findings from a Dataiku survey of 375 line of business leaders around the world reveal that, for many, the spreadsheet struggle is all too real. A staggering 52% of business leaders rely on spreadsheets as their primary data tool, so it’s time to shed light on why spreadsheets are not scalable for analytics and AI in the enterprise. 

For starters, spreadsheets are detrimental for business for reasons such as:

  • Manual errors (more on this in the infographic below)
  • Single points of failure (i.e., they are not productionized, so if a laptop crashes or someone leaves the company, that work is missing) 
  • Data redundancy (due to it residing in multiple desktops and several formats)
  • Limitations associated with a lot of data
  • Lack of recovery and audit capabilities
  • Overall regulatory and compliance challenges

Check out the infographic below — which includes firsthand insights from business leaders across marketing/sales, operations/supply chain, R&D, finance, HR, and executive leadership — for more social proof that teams should shift out of spreadsheets and into a more scalable option.

GM3515-DAC Spreadsheets Infographic (GLG LoB Survey)

Time to Leave Spreadsheets at the Bottom of the Mountain

By using spreadsheets for data analytics, teams essentially stifle their growth. By moving out of spreadsheets, they will be able to increase the size and number of unique datasets as well as the complexity of data analysis. They can escape the risk of their spreadsheet freezing or crashing when a dataset is too big (it has a worksheet size capacity) and losing all of their changes and move on to processing large amounts of data, doing unstructured data processing, working with predictive models, and more.

Think of it this way: While the base camp (basic spreadsheet functions) is manageable for most, as teams (including analysts and business leaders) begin to ascend to the summit of analytics and AI, the path becomes treacherous, filled with pitfalls and obstacles. Spreadsheets have limitations that hinder those stakeholders from accommodating the demands of today’s data-intensive enterprises and successfully scaling to the summit.

You May Also Like

From Vision to Value: Visual GenAI in Dataiku

Read More

Taming LLM Outputs: Your Guide to Structured Text Generation

Read More

No-Code ML and GenAI With Dataiku and Fabric

Read More

The Objects of an LLM Mesh for Building LLM-Powered Applications

Read More