Structured Data at the Center of CFO’s Forecasting Revolution

“Data is the new oil” is a long-overused cliché. But like oil, data can be refined and structured. Like clichés, data can always be applied in relevant contexts.

And quality data, which has always helped grease the wheels of commerce, is now helping finance teams and chief financial officers (CFOs) build real-time back-office systems that can move at the speed of business.

The catalyst is not just a greater volume of data, but cleaner, standardized, and more context-rich information delivered in real time. This improved data structure and delivery velocity is helping reframe the CFO’s role from scorekeeper to strategist, enabling them to activate insights derived from better-structured data across payments, supply-chains and procurement.

For example, better-structured supply-chain data means that figures are standardized across vendors, integrated with financial systems, and enriched with metadata that adds strategic context.

Instead of reconciling disparate spreadsheets from procurement, logistics and warehouse teams, CFOs can now operate from a unified dashboard that links shipment statuses directly to working-capital projections. Standardized frameworks like the Supply Chain Operations Reference Model and ISO-compliant reporting make supplier comparisons straightforward.

Read more: Why CFO Now Stands for ‘Chief Forecasting Officer’

Finance’s Step-Change From Ledger Operations to Data Orchestration

The modern CFO is now likely to be involved in vendor selection, production scheduling, and even logistics routing decisions. Against this backdrop, as well as the ongoing tariff-driven uncertainty, historical analytics can deliver only so much.

What CFOs increasingly want and need is forward-looking clarity. High-quality, granular data can help forecast supply chain disruptions and optimize working-capital deployment.

“[Accounts receivable (AR)] is no longer about settling the past. It’s about predicting the future of cash,” Pamela Novoa Ralli, head of product management at FIS, told PYMNTS in an interview published Tuesday (Aug. 5). “It’s moving from a responsive to a proactive view.”

PYMNTS Intelligence in the June CAIO Report, “The Enterprise Reset: Tariffs, Uncertainty and the Limits of Operational Response,” found that 60% of firms reported that they are addressing today’s tariff-induced challenges through tighter partner coordination, smarter sourcing contract terms, more dynamic price modeling and greater alignment between finance and procurement function. These are all areas where structured or semi-structured data can play a key role in giving CFOs a multi-dimensional view of every operational dollar.

The challenges, however, are not insignificant. Many companies still operate on legacy ERP systems that are difficult to integrate with modern analytics platforms. Data quality can pose a hurdle when legacy systems hold data in incompatible formats, while some suppliers may also remain hesitant to share granular cost or performance data, seeing it as a competitive vulnerability. And within finance teams, there’s often a skills gap.

See also: CFOs Embrace Data Clouds Amid Shift Away From Pure-Play Record-Keeping

Understanding the Data Types Behind the Finance Engine

As corporate finance departments expand their reliance on analytics, the distinction between structured and semi-structured data has become a critical factor in how effectively the Office of the CFO can forecast, report, and activate insights.

Structured data, which refers to information stored in rigid formats such as databases, ERP tables and spreadsheets, remains the backbone of corporate finance. It includes general ledger entries, accounts payable records, tax filings and financial KPIs. This data type is inherently machine-readable, making it straightforward to analyze, audit and feed into business intelligence platforms.

Yet, finance teams are increasingly confronted with semi-structured data — information that carries some organizational tags or markers but lacks the rigid schema of traditional systems. Examples range from invoice PDFs and XML bank statements to contract management systems.

While semi-structured data can contain valuable financial and operational insights, extracting them requires additional processing through tools such as optical character recognition (OCR), natural language processing (NLP) and robotic process automation (RPA).

The primary hurdle is integration. Structured data flows easily into dashboards and regulatory filings. Semi-structured data often lives in silos and is buried in email attachments, embedded in procurement portals, or locked inside vendor-submitted documentation. The delay in converting it into usable formats can slow decision-making, impede compliance and obscure risk indicators.

Forward-leaning finance organizations are responding with centralized data lakes, AI-assisted classification systems, and finance-specific machine learning models. These tools aim to automate the ingestion of semi-structured data, standardize it and merge it with the company’s structured financial records, powering greater operational agility and unlocking the potential of the finance function.