Ever started working on a project with data in an Excel spreadsheet only to find the data is out of date? Spreadsheets are the go-to tool for Finance teams to perform analytics, financial modeling and forecasting. But data that is out of sync with the latest information is a major limitation on the value of those projects.
If you've ever worked with data in your organization, you have probably felt a similar pain. If not, it goes like this: It's Monday morning, you need updated revenue numbers for the board deck due Wednesday, but the CSV export IT sent you last week is already stale. You submit another ticket, mark it "urgent," and hope you'll get the data before your deadline.
This problem isn't just painful, its costly for both Finance and IT.
What if I told you that you could effortlessly keep the data in an Excel Live spreadsheet in sync with the latest data from your database? Yes, really. Keep reading.
In most organizations today, there is an ongoing tension that exists between the IT team and their business counterparts. And I'm not talking about the tension where finance tries to limit IT's budget (Go Fig can help alleviate this friction as well by relieving IT bandwidth, but more on this later).
The tension I'm talking about here is the need for data from finance, and the burden these requests create for the IT team:
Finance teams are completely dependent on IT to source data because they can't write SQL and don't even have database credentials. Even when business intelligence (BI) tools do allow for CSV exports, if the existing dashboard isn't exactly what is needed, they default to filing an IT ticket. BI is either too complex to use or the team simply refuses to learn it because spreadsheets are where the real value is. The result? Projects get delayed by days to weeks just waiting for turnaround on their data requests, and spreadsheets simply aren't updated nearly as often as they should be due to this friction.
IT teams are overly burdened in adhoc data requests. Every "quick" data request takes them away from focusing on strategic IT initiatives. A typical data engineer could spend up to 20 hours per week—literally half their time—responding to requests with varying degrees of urgency from finance, sales, and marketing. And the complex technical strategy they were hired to execute gets put on the backburner.
It's a lose-lose scenario that's costing both teams their sanity and the company's competitive edge.
I can already hear the battle cries from data folk that Finance teams should learn SQL or BI. As someone who started off in Finance and eventually did go on to learn SQL and Python in my transition to Data Science, I'm going to make this next prediction very confidently: finance professionals are not going to abandon Excel or Google Sheets anytime soon, and they shouldn't have to.
If I told you there was a data product that was fast, simple and easy to use, everybody on your team already knew how to use it, and as of last week integrates with AI, wouldn't that sound like a product you would buy for your team? Oh and btw, this product was basically free.
That's exactly what a spreadsheet is.
Spreadsheets are fast, powerful, versatile tools. To be honest, very few BI tools can compete with the spreadsheet in a footrace in the last mile of exploratory analytics.
However, the bigger problems with spreadsheets is when users do too much with them: heavy computational data retrieval, data cleaning, and pivoting across dozens of sheet tabs referencing more than 1,000,000 rows of data. Or saving multiple copies of the same spreadsheet to solve for the lack of inherent version control. These are the limitations that a data tool needs to solve for.
Introducing data workflows: no-code ETL that clean, process and aggregate data before shipping it off to a spreadsheet for slicing and dicing.
Imagine a scenario where business stakeholders in an organizations could completely self-serve their data requests:
This isn't a fantasy.
This capability already exists in the Power BI suite: UpSlide describes Power Query as the perfect solution for business users to interface with data in a low-code UI for organizations that don't have a data engineer. It does take some training, as the Power Query logic is noticeably different from standard spreadsheet logic.
Go Fig offers a simpler alternative solution to this problem with visual workflows that work with existing spreadsheet logic.
A Finance Director at a 200-person B2B SaaS company was working on a project to understand operating costs. Previously, they would wait multiple days for IT to pull data from their MS SQL Server into a Power BI dashboard. The Finance director would ask IT for updates on a regular basis, and access the data by downloading the Power BI tables as a CSV. This created a lot of friction in the initial model build, and very challenging to monitor costs to identify spikes in costs quickly.
With Go Fig, they've set up an automated workflow that:
Results:
Leadership at a real estate startup needed to predict cash flows 6 months out to manage their burn rate. They were manually downloading real estate market data from Redfin and combining it with internal sales data—a process that took 8 hours every month.
With Go Fig, their new workflow is completely automated and provides insights more frequently:
Results:
The CFO now has real-time visibility into cash position, and the analyst who used to spend a full day per month on this now focuses on strategic analysis instead of data wrangling.
The breakthrough comes from no-code workflow builders that speaks the language of the business, not IT's. Instead of writing SQL queries like:
SELECT customer_id, SUM(revenue) as total_revenue
FROM transactions
WHERE date >= '2024-01-01'
GROUP BY customer_id
Finance teams use familiar spreadsheet logic:
Even better, with AI assistants like Fig AI, you can simply describe what you need in natural language. For example: "Pull last month's revenue by customer and update my forecast model every Monday at 8am."
If you're still manually updating spreadsheets or waiting on IT for data refreshes, you're not alone. Most finance teams are stuck in this pattern because they don't have the right data tooling. But the technology exists today to connect your spreadsheets directly to your data stack—no SQL required, no IT bottleneck, no more stale data.
The companies that figure this out first will not only experience cost savings, but also increase the engagement of their teams by enabling them to make use of their data to add more value to the organization. And they'll be less likely to leave for a less tech-enabled organization.
If you are an IT leader and you've read this far, you care deeply about enabling your partner teams with the right data tooling. Kudos to you. If you're still on the fence, here's a message I want to reinforce: enabling finance and business teams to self-serve their data needs isn't giving up control. It is strategic delegation. When you implement a no-code workflow solution, both you and your partner teams benefit:
The most forward-thinking IT teams are actively implementing data tooling that better supports business teams because they are starting to recognize that BI tools are failing to meet the business where they need support in regards to adhoc data requests. And freeing up these resources equally benefits IT with more time and focus to build robust, scalable infrastructure that further enables the entire organization. This is a true win-win outcome.
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.
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.
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:
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.
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.
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.
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?
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.
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.
