Single Source of Truth? It Doesn’t Exist

For years, I’ve worked at the intersection of finance, technology, and operations, helping organisations across sectors and geographies implement complex systems. No matter the size of the business or the sophistication of its technology stack, one reality has remained constant: finance data is fragmented.

Survey research shows 96% of CFOs struggle with data quality - a global pain point. In my own conversations with finance leaders across industries and regions, the discussion almost always begins, and often ends, with the same frustration:

“Our data is disconnected and unreliable.”

The Unfulfilled Promise of the Modern Finance Stack

The modern finance ecosystem is marketed as an engine for insight, automation, and strategic intelligence. Yet in practice, finance leaders and their teams remain mired in inefficiency, spending countless hours wrestling with:

• Disconnected data scattered across systems, breaking continuity and slowing decisions
• Humans acting as the “glue” between processes, relying on manual effort to move work forward
• Critical insights locked inside spreadsheets, PDFs, and static documents

As a result - strategic decisions stall, automation remains out of reach, and CFOs are stuck solving yesterday’s problems instead of shaping tomorrow’s opportunities.

Rethinking the Problem

The traditional approach assumes the problem lies in the data itself. The solution, then, is to “fix” it - by deploying consultants, analysts, and developers to clean, standardize, and integrate. Only after these expensive and time-consuming exercises can advanced analytics or AI supposedly be applied.

But what if the real opportunity isn’t in fixing the data for AI - it’s in building AI that works with your data as it is?

From Data-Ready AI to AI-Ready Data

Imagine a different model, one where AI adapts to the messy, real-world conditions of finance operations:

• AI that reads and understands spreadsheets like a human - navigating complex file structures, formulas, and formatting without requiring a predefined data model.
• Automation that runs on your current workflows - performing reconciliation, variance analysis, or close processes without demanding perfect inputs.
• Learning from real context, not just rules - eliminating the need for custom scripts or endless integrations.

This isn’t a “data lake” initiative. It’s a mindset shift: from AI as a capital-intensive project to AI as an everyday collaborator.

The Rise of the AI Coworker

At LuminaData, we’re building role-based AI coworkers that work alongside finance teams, delivering a 10x productivity lift by working with your data, not against it.

Consider the possibilities:

• A reconciliation that takes seconds, not hours
• A month-end close without 14 manually linked spreadsheets
• A real-time spreadsheet review completed while you’re sipping your morning coffee

This is AI as a teammate - not as another project to manage. And without any costly IT implementation.

Accepting Reality as the Norm

The “single source of truth” is a worthy goal, but in reality, it’s a moving target. Systems evolve. Spreadsheets proliferate. Mergers, regulatory changes, and market volatility guarantee ongoing complexity.

The future of finance transformation isn’t about waiting for perfect data, it’s about delivering results in spite of imperfect data. The CFOs who adopt AI coworkers now will move faster, decide sooner, and lead markets, not follow them.

Do not wait to get data ready for AI. Bring AI to your data and shape the future of finance.

About the Author

Afrozy Ara is the Co-Founder and CEO of LuminaData, a Techstars- and Berkeley SkyDeck-backed company transforming finance operations with role-based AI coworkers. With over a decade in consulting and enterprise systems implementation, she has advised CFOs across industries on data strategy, automation, and digital transformation. At LuminaData, she pioneers AI solutions that adapt to real-world finance workflows, eliminating manual processes and delivering 10x productivity gains.