Last week, I talked with the CIO at a tech company in Columbia, South Carolina. They were lamenting about how their Finance manager spends hours updating the same report every month. Spreadsheets and data science are closely related, and he helped connect a spreadsheet to their database so that it had all their information, but it contained millions of rows of data. This has so much data, that simply updating one action in a pivot table takes an hour to update.

This was the same Finance manager who he described as a brilliant individual who could build a complex DCF model in her sleep, yet she was stuck doing digital grunt work.

The irony wasn't lost on me, or on the CIO I was talking to. We both knew that a few lines of SQL and Python can pivot and aggregate millions of rows of data in a fraction of a second.

How can that be? Spreadsheets are an incredibly powerful tool and has one of the most versatile interface that we have all come to know and love.

But the upside of the familiar user interface is also the downside of its speed and performance. Spreadsheets have limitations on the amount of data that it can work with, and will start to get buggy and crash if it exceeds these limits.

Coding with SQL and Python on cloud-based environments, on the other hand, is lightning fast. But it comes with the downside of not having any UI.

If you're reading this, you probably recognize yourself in that story. You've built financial models that would make McKinsey consultants weep with envy. You can manipulate data in ways that reveal insights others miss. You speak fluent VLOOKUP and think in terms of scenarios and sensitivities.

But you're dependent on your IT team to get you the data you need. You feel bad about bugging them, so you only ask them once a week for a refresh. You wish you were manifested the full capacity of a data scientist to work out of SQL and Python, so you try asking ChatGPT to write some code for you, but it doesn't seem to be working out the way you hoped it would

You're essentially doing data science—you just don't have the tools to match your ambition. What you need is a way to marry the computational power of code with the flexibility and familiarity of spreadsheets.

The Analytics Gap Between Spreadsheets and Data Science

Here's what I've learned from talking to hundreds of finance professionals and business analysts: you don't actually want to learn Python or become a data engineer. You want the outcomes that data science delivers—automated insights, scalable analysis, real-time intelligence—without having to rebuild your entire skillset.

You're caught between two worlds. On one side, you see data scientists pulling insights from massive datasets, building predictive models, and automating complex analyses. On the other side, you're stuck manually refreshing data connections and praying your SUMIFS formula captured all the edge cases.

The problem isn't your skills—it's that the tools haven't evolved to meet you where you are. This challenge is increasingly common in modern data analytics, where business users need advanced capabilities without technical barriers.

Think about it this way: you wouldn't expect a surgeon to forge their own scalpels, yet we expect finance professionals to become software engineers just to automate their reporting. It's backwards.

The Three Things That Actually Matter

Forget about dashboards and data lakes for a minute. When I talk to finance teams about what would genuinely transform their work, it always comes down to three core needs:

• Start with accurate, up to date data
• Computational horsepower to clean and process data
• Speed and flexibility to understand the story, propose a solution, and prepare the presentation

Data That Doesn't Lie (or Lag)

Remember the last time you presented budget variance analysis, only to have someone in the room question whether you were using the latest actuals? That sinking feeling when you realize your "current month" data is actually from two weeks ago because someone forgot to update the export from the ERP system?

The truth is, most financial analysis is built on a foundation of stale data held together by manual processes. You spend more time verifying data freshness than actually analyzing what the numbers mean.

What you really need is a direct line to your live business data—not another CSV download, not another "can you refresh this report," but actual real-time connection to the systems that matter. Your revenue recognition platform, your CRM, your operational databases.

Advanced Analytics Without Programming Knowledge

Here's a scenario that probably sounds familiar: You're trying to analyze customer lifetime value across different acquisition channels, but Excel keeps crashing because you're dealing with three years of transaction data across 50,000 customers. So you break it into smaller chunks, run separate analyses, and manually piece together the insights.

Or maybe you want to build a cohort analysis to understand subscription churn patterns, but it requires complex SQL joins across multiple tables that would take your IT team weeks to set up (if they even have time to prioritize it).

The computational power exists to solve these problems instantly. Data scientists working with the same datasets would have answers in minutes, not days. The barrier isn't the complexity of your questions—it's that you need the processing capabilities of Python and SQL without having to master Python and SQL.

Spreadsheet Integration for Data Science Results

Let me be controversial for a moment: spreadsheets aren't the enemy. They're actually the perfect environment for the final mile of financial analysis. The problem is when they become the entire journey.

You need spreadsheets for that final layer of modeling speed and flexibility—the what-if scenarios, the presentation formatting, the collaborative review process with your team. The CFO who wants to adjust assumptions and see results instantly. The board member who wants to drill down into a specific metric during the presentation.

But you shouldn't need spreadsheets for data extraction, transformation, and basic aggregation. Those are computational problems that deserve computational solutions.

The ideal workflow? Automated data processing that feeds clean, validated, current results directly into a tab into your spreadsheet, where you can apply your analytical superpowers without getting bogged down in data plumbing.

This approach aligns with modern data democratization strategies that give business users self-service capabilities while maintaining data quality.

The Missing Bridge

This is where most "solutions" fail you. They either dumb things down so much that you lose analytical power, or they expect you to become a part-time developer just to get your quarterly variance report.

I've watched finance teams try to adopt traditional BI tools, only to discover they can't modify the canned reports when business requirements change. I've seen analysts attempt to learn Python, then give up when they realize they need to understand data engineering concepts just to connect to their company's database.

The market keeps telling you to pick a side: either accept the limitations of spreadsheet-based analysis, or invest months learning technical skills that aren't really your core competency.

What if there was a third option?

Go Fig: Marrying Spreadsheets with Data Science

Look, I'm obviously biased here—I built Go Fig specifically to solve this exact problem. But let me tell you why this matters to you.

Traditional data tools were built by engineers for engineers. They assume you want to learn their technical frameworks and adapt your thinking to their constraints. Go Fig flips that completely around.

We started with a simple premise: the most sophisticated business minds shouldn't be constrained by technical limitations. Your ability to model complex scenarios, identify meaningful patterns, and generate actionable insights shouldn't depend on whether you know how to write a SQL query or how to access data where it lives.

Instead of forcing you to learn data science, Go Fig brings data science capabilities into your existing workflow. You work with the same conceptual frameworks you already understand—tables, relationships, calculations—but with computational power that can handle enterprise-scale datasets.

And here's the key part: any Workflow built in Go Fig can export data to Google Sheets or a local CSV. Not because we couldn't build a fancier interface, but because we know that's where you do your best analytical work. Where you can tweak assumptions, collaborate with stakeholders, and present results in the format that actually drives decisions.

Learn more about how this integration works in our guide to automating reporting workflows.

The Reality Check

I talk to CFOs every week who tell me their teams are still drowning in manual reporting despite significant investments in business intelligence platforms. The problem isn't the technology—it's that most data tools weren't designed for how finance professionals actually think and work.

You don't need another dashboard. You need computational automation that respects your analytical process.

You don't need to become a data scientist. You need data science capabilities that work within your existing expertise.

You don't need to abandon spreadsheets. You need spreadsheets fed by systems that are worthy of your analytical sophistication.

That's exactly what Go Fig delivers. And honestly? It's about time someone built a tool that meets you where you are instead of demanding you become someone else.

Ready to stop settling for manual data plumbing? Explore our step-by-step implementation guide for finance teams looking to modernize their analytics without losing spreadsheet flexibility.