In today’s fast-paced business landscape, data has become a critical asset for organizations, shaping various functions, especially in finance. High-quality, structured data is now a vital ingredient for Chief Financial Officers (CFOs) to move beyond traditional roles and embrace strategic responsibilities. This transition represents an ongoing shift in the finance sector, where data flows influence real-time business decisions impacting payments, supply chains, and procurement. Companies are now relying on finance teams to use this enhanced data to forecast and avert potential financial risks. The structured data ensures that CFOs can synthesize insights and integrate logistics and capital forecasting on a unified platform.
While structured data has been central to finance for years, evolving business needs demand more than traditional metrics. CFOs must deal with various challenges posed by legacy systems, which can hinder integration with modern analytics platforms. Suppliers’ reluctance to share granular cost or performance data further complicates data collection efforts. Beyond such constraints, there is a pressing need for finance teams to bridge the skills gap to handle complex analytics. Previously, reports highlighted how companies, including FIS, have identified data forecasting as crucial for financial decision-making. Additionally, sectors are increasingly incorporating AI and machine learning models to integrate semi-structured data, complementing existing structured data streams.
What Led to This Shift?
CFOs have transitioned from basic ledger management to complex data orchestration against a backdrop of global economic uncertainty and technological advancement. The ability to anticipate supply chain disruptions and optimize working capital has become crucial, driven by the need to align finance functions more closely with procurement and logistics. This strategic oversight extends to influencing vendor selection, production scheduling, and logistics routing. These roles necessitate comprehensive and actionable data insights.
How Do Different Data Types Impact Financial Functions?
Structured data, found in formats like databases and spreadsheets, remains a cornerstone for finance departments, facilitating streamlined analyses and audits. Yet, the realm of semi-structured data demands attention. This type, found in formats like PDFs and XML files, is abundant with insights but requires advanced processing technologies such as optical character recognition and natural language processing for effective extraction and analysis.
Organizations are now moving towards innovations like centralized data lakes and AI-driven classification systems to automate data ingestion. This amalgamation of structured and semi-structured data serves to enhance operational flexibility and elevate the finance team’s strategic value. According to the head of product management at FIS,
“Accounts receivable [AR] is no longer about settling the past. It’s about predicting the future of cash,” noted Pamela Novoa Ralli.
The shift towards using AI and machine learning in processing data has been bolstered by efforts to standardize frameworks and reporting mechanisms across industries. This aids in concise supplier evaluations and contributes to developing strategic financial blueprints. Emphasizing granularity and the decision-support nature of modern finance, the need for clean, structured data is paramount. Finance leaders reiterate that,
“It’s moving from a responsive to a proactive view,” Ralli emphasized.
In light of evolving data demands, the finance sector’s ongoing transformation highlights how invaluable structured data is becoming for CFOs committed to strategic leadership within organizations. Teams looking to maximize operational agility must focus on seamless integration of varied data types, better standardization, and advanced analytics to enable comprehensive decision-making processes.
