The Spreadsheet Isn't the Problem
The interface matters less than the foundation underneath it
Listen. I know many data folks don’t like the data you piped and modeled end up in a spreadsheet. Data teams might even view spreadsheets as a sign of immaturity, and the goal becomes getting users out of spreadsheets, and into dashboards or other places. But is the existence of a spreadsheet actually the problem?
The answer is no.
The real problem may be when:
Numbers are manually copied around
Sarah in Finance has 8 versions of the same spreadsheet
Logic lives in someone’s spreadsheet only saved on his laptop
Business users don’t trust the data
If a spreadsheet is powered by trusted, well-governed data, it’s completely different from a spreadsheet built by hand nobody trusts.
Decisions Over Tools
The purpose of analytics is not to create spreadsheets or dashboards. It is to help people make decisions. And the best analytics tool is the one that helps people make better decisions (To be fair, sometimes putting data in a tool is required for reporting. In that case you need something like a spreadsheet or dashboard).
Different decisions require different tools:
A dashboard is great for monitoring KPIs.
A spreadsheet is great for applying last-mile modeling and sharing.
An AI chat is great for grabbing certain information that doesn’t readily available in existing tools. And many times it can go further by doing an analysis for you and crafting a custom report.
Example:
A CFO doesn’t just want to know current ARR. They might want to ask:
What happens if we close these deals?
What happens if churn increases?
What does next quarter look like under different assumptions?
One tool might just tell you what happened. Another might let you tweak the parameters and let you work through what could happen. You’d use different tools for different needs.
The Foundation Matters More
This is obvious, but often overlooked. Nothing matters as much as the foundation.
The data industry spends a lot of time debating:
Which BI tool?
Which data transformation framework?
Which semantic layer?
But the biggest challenges are upstream:
Are metrics defined consistently?
Is the data accurate?
Are pipelines reliable?
Are sensitive fields secured or redacted?
Can people trust the numbers?
You can’t build a house on a loose foundation.
A dashboard or spreadsheet built on unreliable data is still unreliable.
If you built it on trusted, well-modeled data, it can be incredibly powerful.
Focus on the Foundation, Not the Interface
The role of a data team isn’t to force everyone into a specific tool.
It is to provide:
Reliable pipelines
Well-modeled data
Clear definitions
Easy and secure access
(And ultimately somehow make the business more profitable with better decisions)
It’s easy to think of things in terms of tools, but the foundation underneath is what provides value to the business.
And you just let people use the tools that fit their workflows.
Finance might use spreadsheets.
Executives might use dashboards.
Analysts might use SQL or notebooks.
Operations might use reports directly in CRM.
It’s always good to remember - the goal is better decisions, and not tool adoption.
Conclusion
The answer isn’t always a dashboard, or spreadsheet. What’s most important is almost always the foundation that it’s built on.
Some business users keep exporting data to spreadsheets, and that’s okay.
If something works for them to make better decisions or to do good reporting, then that’s what matters to the business and you’re doing what you’re supposed to be doing.
Data Advisory & Consulting
Do you need help with your data & analytics projects? I provide data advisory and consulting services.
Reach out to me at yuki@oremdata.com or on LinkedIn to discuss the data challenges you’re facing.