Data Infrastructure and Global Market Signals

Cirebonrayajeh.com | The relentless surge of data and the transformative power of Artificial Intelligence (AI) are not merely changing financial markets; they are fundamentally rebuilding them from the ground up. For the modern Financial Manager, Controller, or CFO, this evolution transcends simple technological adoption. It represents a profound shift in the very core of financial operations, strategic decision-making, and competitive advantage. The critical link between these forces is robust data infrastructure—the often-unseen technological foundation that determines an organization's ability to harness AI, interpret global market signals, and ensure resilient, compliant performance. In an environment where 90% of financial leaders cite data modernization as their top technology priority, this infrastructure has become the most significant determinant of future success.

Financial institutions today operate in a landscape defined by real-time transactions, exploding data volumes, and intricate regulatory demands. Legacy systems, built on fragmented data and slow batch processes, are increasingly incapable of meeting these challenges. They create bottlenecks that hinder real-time fraud detection, accurate risk assessment, and agile response to market movements. Conversely, a modern, AI-ready data platform acts as a central nervous system. It ingests, processes, and analyzes vast streams of structured and unstructured data—from high-frequency trading feeds and IoT sensors to geopolitical news and ESG reports—transforming them into actionable, predictive insights. This capability to process global market signals efficiently is what separates industry leaders from the rest.

This article will explore the indispensable role of data infrastructure as the backbone of contemporary finance. We will dissect its core architectural principles, examine the evolving regulatory landscape governing financial data, and analyze how it powers the interpretation of complex market signals. Furthermore, we will provide a practical roadmap for financial leaders to champion this essential modernization, positioning their organizations not just to adapt to the future of finance, but to define it.

Why This Matters Now: A Strategic Imperative for Finance Leaders

The imperative for modern data infrastructure is driven by several converging, powerful trends:

  • The AI Revolution's Hunger for Quality Data: AI and machine learning models are fundamentally data-dependent. Their predictive accuracy and strategic value are directly tied to the volume, variety, and veracity of the data they consume. Poor-quality data trapped in silos leads to flawed forecasts, misguided investments, and a rapid erosion of trust in AI tools. Finance departments, with their inherent focus on accuracy, governance, and control, are uniquely positioned to lead the charge in building the clean, well-structured, and accessible data foundation that AI requires to deliver reliable value.
  • The Explosion of Data and Market Complexity: Financial markets are now influenced by a broader, faster-moving set of signals than ever before. Beyond traditional price and volume data, effective analysis must incorporate alternative data sources: satellite imagery of supply chains, social media sentiment, detailed climate models, and real-time logistics information. Processing these diverse global market signals in real-time demands an infrastructure built for scale, speed, and interoperability.
  • Intensifying Regulatory and Reporting Demands: The regulatory environment is becoming more granular and demanding. New standards like IFRS 18 on presentation and disclosure, effective in 2027, require more detailed disaggregation of financial information and new disclosures for management-defined performance measures. Concurrently, data privacy regulations like GDPR and industry-specific rules (e.g., Basel III, PCI-DSS) mandate rigorous governance, security, and auditability. A modern data infrastructure with built-in governance, lineage tracking, and robust security is no longer optional for compliance.
  • The Rise of the Financial Data and Markets Infrastructure (FDMI) Sector: This sector—encompassing data providers, exchanges, and fintech infrastructure companies—is one of the fastest-growing in financial services, outperforming the broader sector with a 17% CAGR in total shareholder return from 2019-2023. This growth underscores the immense economic value placed on high-quality data and the platforms that deliver it. For corporate finance teams, engaging with and leveraging these FDMI providers is a key strategic consideration.
  • The convergence of these trends creates a clear mandate: the organizations that will thrive are those that treat their data infrastructure as a core strategic asset. It is the critical enabler for everything from automated financial reporting and proactive risk management to generating alpha and personalizing client services. The following sections will provide a comprehensive framework for understanding and building this essential capability.

Foundational Pillars: The Architecture of a Modern Financial Data Stack

Building a future-proof financial data infrastructure requires moving beyond legacy, siloed systems to an integrated, scalable, and secure architecture. This modern data stack is composed of several interconnected layers, each serving a distinct and vital function in the data lifecycle. For finance professionals, understanding this architecture is crucial for effective collaboration with IT, informed vendor selection, and ensuring the final system meets stringent financial control requirements.

A cohesive modern stack typically consists of five key layers:

The Data Ingestion Layer: This is the entry point, responsible for collecting data from myriad internal and external sources. Modern systems emphasize real-time ingestion using tools like Apache Kafka to handle streaming data from market feeds, transaction systems, and application logs. Batch pipelines (ETL/ELT) simultaneously manage larger, periodic data transfers from enterprise resource planning (ERP), customer relationship management (CRM), and human resources systems. A robust ingestion layer ensures seamless integration with core banking platforms, payment networks, and third-party data providers, forming a comprehensive data intake framework.

The Storage Layer: Once ingested, data must be stored reliably and cost-effectively. The modern paradigm utilizes a multi-tiered approach. Data Lakes (e.g., on AWS S3, Azure Data Lake) store vast amounts of raw, unstructured, and semi-structured data at scale, preserving its original fidelity for exploratory analysis. Data Warehouses (e.g., Snowflake, Google BigQuery) then house processed, structured data optimized for fast SQL queries and business intelligence. Increasingly, Lakehouse platforms like Databricks unify these concepts, combining the flexibility of a data lake with the management and performance features of a warehouse.

The Processing and Transformation Layer: Here, raw data is refined into usable information. This involves data cleansing, standardization, aggregation, and feature engineering. Stream processing frameworks (e.g., Apache Flink) analyze data-in-motion for real-time use cases like fraud detection. Batch processing engines handle complex transformations on large datasets to prepare them for reporting and advanced analytics. For finance, this layer is where critical rules are applied—such as currency conversion, allocation calculations, and adherence to accounting standards like the new IFRS 18 categorization rules for income and expenses.

The Semantic and Governance Layer: This layer provides meaning and ensures trust. It houses the business logic, data definitions, and key metrics (KPIs) that standardize how data is interpreted across the organization. It is the foundation for data governance, encompassing metadata management, data lineage (tracking data's origin and transformations), and access controls. Tools like Collibra or Alation are often used here to create a searchable catalog, ensuring that when a financial analyst queries "Q3 operating profit in Europe," it is calculated consistently for everyone.

The Consumption and Analytics Layer: This is where value is realized. It provides interfaces for end-users to access insights. This includes Business Intelligence (BI) and visualization tools (e.g., Power BI, Tableau) for dashboards and reports. Crucially, it also includes AI/ML platforms (e.g., MLflow, Azure ML) where data scientists build and deploy models for forecasting, sentiment analysis, and algorithmic trading. Secure APIs also reside here, allowing governed data to be fed directly into other applications, such as risk management systems or client portals.

Table 1: Core Layers of a Modern Financial Data Infrastructure

Layer Primary Function Key Technologies & Tools Finance-Specific Considerations
Ingestion Collect data from source systems Apache Kafka, Fivetran, Airbyte Integration with ERP (SAP, Oracle), Bloomberg/Refinitiv feeds, payment networks.
Storage Store data at scale for different uses AWS S3, Azure Data Lake, Snowflake, Databricks Cost management, data retention policies for compliance, secure storage for sensitive financials.
Processing Clean, transform, and prepare data Apache Spark, dbt, Apache Flink Enforcing accounting rules, currency conversion, data validation for audit trails.
Semantic & Governance Define business meaning & ensure data quality Collibra, Alation, Microsoft Purview Defining single source of truth for KPIs, managing access to sensitive data, lineage for SOX compliance.
Consumption Deliver insights to users and systems Power BI, Tableau, Jupyter Notebooks, MLflow Designing board-level dashboards, enabling self-service analytics for FP&A, deploying predictive models.

Underpinning this entire architecture are non-negotiable core principles: a security-first design with encryption and zero-trust access; elastic scalability to handle volatile transaction volumes; and a commitment to high availability to ensure 24/7 operational resilience. For the finance function, this modern stack is not an IT project but the essential platform that enables accurate, timely, and deep financial insight.

Governance, Compliance, and the Language of Global Finance: IFRS, GAAP, and Data

For finance professionals, data is not an abstract concept—it is the raw material for financial statements, regulatory filings, and statutory reports. Therefore, the design and management of data infrastructure are deeply intertwined with accounting standards and compliance frameworks. The global regulatory landscape is dynamic, with significant updates to both International Financial Reporting Standards (IFRS) and U.S. Generally Accepted Accounting Principles (GAAP) that directly impact data requirements.

The Evolving Standards: IFRS and GAAP in 2025-2027

Recent and forthcoming changes to accounting standards underscore the need for flexible, detailed, and well-governed data systems:

  • IFRS 18 - Presentation and Disclosure (Effective 2027): This landmark standard introduces major changes to the income statement, requiring entities to classify income and expenses into operating, investing, and financing categories and to present new subtotals like operating profit. Crucially, it demands more detailed disaggregation of information and introduces strict new disclosure requirements for management-defined performance measures (MPMs). Data systems must be able to capture transactions at a granular enough level to support these new classifications and reliably reconcile non-GAAP/IFRS measures back to standard subtotals.
  • Amendments to IFRS 9 and IAS 21: Other amendments clarify the accounting for financial instruments with ESG-linked features and provide guidance for currencies that lack exchangeability (e.g., due to capital controls). These rules require systems to capture complex, contingent cash flows and apply sophisticated estimation techniques for foreign exchange rates, relying on high-quality market and contractual data.
  • U.S. GAAP Developments: While convergence with IFRS has slowed, GAAP continues to evolve, with ongoing updates from the Financial Accounting Standards Board (FASB) on areas like cryptocurrency assets. The core principle remains: financial data must be recorded and presented in accordance with a rigorous framework to ensure consistency and comparability, a principle born from the need to restore market trust after the crises of the early 20th century.

The Role of the IFRS Taxonomy and Data Governance

This is where the IFRS Accounting Taxonomy becomes critical. The Taxonomy, updated annually (e.g., the 2025 version reflects IFRS 18), is a standardized digital dictionary of reporting concepts. It allows companies to "tag" each piece of data in their financial statements with a unique, machine-readable identifier. For example, the number for "Revenue from Contracts with Customers" would be tagged with a specific taxonomy element.

The implications for data infrastructure are profound:

  • Structured Data from Source: To efficiently tag financial statements, data must be structured and granular from the point of transaction. Modern ERP and data platforms facilitate this by ensuring accounting codes and data models align with reporting concepts early in the pipeline.
  • Governance as a Compliance Engine: Effective data governance is the mechanism that ensures compliance. It involves assigning clear ownership for data accuracy, implementing controls to protect sensitive information, and maintaining an audit trail of changes. As noted by the Controllers Council, a finance-led governance approach ensures data accuracy, controlled access, and standardized definitions—all preventing inconsistencies that could lead to reporting errors or compliance breaches.
  • Automating Compliance and Reporting: With well-structured data and strong governance, organizations can automate significant portions of their regulatory reporting. The 2025 IFRS Taxonomy even includes a validation formula to automatically check the reconciliation of MPMs. This reduces manual effort, minimizes errors, and allows finance teams to shift from data collection to analysis and insight.

In essence, contemporary accounting standards are not just rulebooks for accountants; they are detailed blueprints for an organization's financial data architecture. Building infrastructure with these standards in mind is a strategic investment that reduces compliance risk, lowers reporting costs, and enhances the credibility of financial information in the global marketplace.

Decoding the World: Data Infrastructure as the Engine for Global Market Signal Analysis

The ultimate competitive advantage in modern finance lies not just in storing data, but in synthesizing disparate global market signals into a coherent, predictive narrative. This involves moving from analyzing internal, structured financial data to interpreting a vast universe of external, often unstructured, information. A modern data infrastructure is the essential engine that makes this complex analysis feasible, timely, and actionable.

What Constitutes a "Global Market Signal"?

These signals are data points that provide insight into economic, political, social, or environmental trends that affect asset prices, credit risk, and strategic decisions. They fall into several key categories, each with unique data-handling requirements:

  • Traditional Financial Data: Market prices, volumes, interest rates, and yield curves. This is typically high-frequency, structured data from exchanges and trading venues.
  • Alternative Data: This broad category includes geospatial imagery (satellite photos of retail parking lots or shipping ports), sentiment analysis from news and social media, web traffic and consumer card transaction aggregates, and supply chain logistics information. This data is often unstructured or semi-structured.
  • Macroeconomic and Geopolitical Intelligence: Government releases (employment, inflation), central bank communications, legislative tracking, and data on cross-border capital flows. The IMF has highlighted how AI can rapidly analyze complex documents like central bank statements or bond indentures to improve price discovery.
  • ESG and Climate Data: Carbon emissions metrics, water usage, biodiversity impact scores, corporate governance ratings, and climate risk model outputs. Amendments to IFRS 9 now specifically address the accounting for financial instruments with ESG-linked features, raising the strategic importance of this data.

The Infrastructure for Signal Processing: From Capture to Insight

Translating these raw signals into investment theses or risk warnings requires an infrastructure capable of several advanced functions:

  • Unified Data Ingestion and Integration: The infrastructure must connect to a proliferating number of external data vendors, APIs, and direct feeds. It must handle both real-time streams (e.g., Twitter firehose, market ticks) and large batch files (e.g., monthly satellite imagery datasets). The goal is to break down data silos so AI models have the broadest possible context for analysis.
  • Processing Unstructured Data at Scale: This is where Generative AI (GenAI) and large language models (LLMs) are revolutionary. LLMs can read, summarize, and extract sentiment from millions of news articles, earnings call transcripts, and regulatory filings in multiple languages almost instantaneously. This capability, as noted by the IMF, allows investors to process vast amounts of unstructured text to enhance their analytical tools, potentially improving forecasts and uncovering hidden correlations.
  • Feature Engineering and Model Deployment: Data scientists use the processing layer to create "features"—derived metrics that serve as inputs to machine learning models. For instance, raw geospatial data might be transformed into a "weekly retail foot traffic index" feature. The infrastructure must support the full ML lifecycle, from experimentation to the deployment of models that can score signals and generate alerts or trading ideas in production.
  • Real-Time Analytics and Decisioning: For use cases like algorithmic trading or dynamic hedging, latency is critical. Stream processing frameworks analyze data the moment it arrives, allowing systems to act on a global market signal—such as a sudden shift in news sentiment or an anomalous options trade—within milliseconds.

Practical Applications: From Theory to Value

The confluence of robust infrastructure and advanced analytics powers transformative applications:

  • Alpha Generation: Quantitative hedge funds have used these techniques for years. Now, the efficiency gains from AI-assisted analysis are lowering barriers to entry, allowing more firms to apply quantitative strategies to less liquid asset classes like corporate debt and emerging markets.
  • Strategic Risk Management: A treasury department can use real-time integration of FX rates, geopolitical news sentiment, and supply chain disruption data to dynamically adjust its currency hedging strategy and working capital forecasts.
  • ESG Integration and Compliance: An asset manager can systematically analyze corporate sustainability reports, NGO publications, and regulatory filings using NLP to score portfolio companies on greenwashing risk or alignment with the EU's Sustainable Finance Disclosure Regulation (SFDR).

However, this power comes with responsibility. The IMF warns that the increased speed and potential homogeneity of AI-driven strategies could amplify market moves, as seen in episodes where algorithms collectively exacerbated sell-offs. This underscores the need for human oversight, robust risk controls, and infrastructure resilience to prevent a cascade of automated decisions from destabilizing markets.

The Implementation Challenge: A Roadmap for Finance Leaders

For CFOs, Controllers, and Financial Managers, championing the transition to a modern data infrastructure is a strategic leadership imperative. It is a cross-functional endeavor that bridges finance, IT, compliance, and business operations. Success requires a disciplined, phased approach that aligns technical execution with financial control and business value.

Phase 1: Assessment and Strategic Alignment (Months 1-3)

  • Conduct a Data Readiness Audit: Begin by taking inventory. Partner with IT to identify all critical data resources—ERP, CRM, trading systems, spreadsheets, and external data feeds. Evaluate the quality of this data, checking for duplicates, inconsistencies, and gaps. This audit, as recommended for AI readiness, provides a clear baseline.
  • Define the "North Star" Vision and Use Cases: Avoid building infrastructure for its own sake. Start with the business outcomes. Collaborate with business unit leaders to identify 2-3 high-value, tractable use cases. Examples include: automated monthly close and reporting, real-time fraud detection for treasury operations, or a predictive model for customer credit risk. These use cases will dictate technical requirements and justify investment.
  • Establish Governance from Day One: Form a cross-functional data governance council co-chaired by Finance and IT. Draft a preliminary charter that defines data ownership (which department is responsible for the accuracy of which datasets), access policies, and quality standards. This framework ensures control and compliance are built-in, not retrofitted.

Phase 2: Foundational Build and Pilot (Months 4-12)

  • Design the Target Architecture: With IT and a potential technology partner, design the stack outlined in Section 2. Prioritize a cloud-native approach for its scalability and managed services. Crucially, ensure the design incorporates security-first principles—encryption, identity and access management (IAM), and network isolation—from the start.
  • Select and Implement Core Platforms: Choose the core components for ingestion, storage, and processing. Many organizations start with a major cloud provider (AWS, Azure, GCP) and a leading lakehouse platform. Integrate a data catalog/governance tool early to begin documenting assets.
  • Execute a Focused Pilot: Select the highest-priority use case and implement it end-to-end on the new platform. For finance, a strong pilot is automating a complex, manual reporting process (e.g., regulatory capital reporting). This delivers quick wins, builds team competency, and creates a tangible proof of concept to secure broader buy-in and funding.

Phase 3: Scaling and Cultivating a Data-Driven Culture (Year 2+)

  • Expand Data Integration and Capabilities: Onboard additional data sources and business units. Begin integrating alternative data feeds relevant to your pilots. Gradually roll out more advanced analytics and BI tools to a wider user base in the finance team.
  • Drive Adoption through Literacy and Tools: A sophisticated infrastructure is worthless if the team cannot use it. Partner with IT to create user-friendly dashboards and interfaces. Host targeted training sessions and encourage experimentation with low-risk AI use cases, such as using GenAI to draft commentary for management reports or to categorize expenses.
  • Monitor, Refine, and Evolve Governance: Establish KPIs for the data platform itself (e.g., data freshness, pipeline reliability, query performance). Regularly review governance policies and adjust them as new data types (e.g., ESG, unstructured text) are incorporated and as regulations evolve. Treat the infrastructure as a living asset that requires continuous investment and refinement.

Critical Success Factors and Pitfalls to Avoid

  • Finance Leadership is Non-Negotiable: The finance function must be a co-owner, not a passive consumer. Finance leaders bring indispensable expertise in controls, accuracy, compliance, and the strategic meaning of data.
  • Partner, Don't Dictate, with IT: This is a partnership of equals. Finance defines the "what" and "why" (business rules, controls, requirements), while IT defines the "how" (technology selection, architecture, deployment).
  • Beware of the "Big Bang" Approach: Avoid attempting a full, company-wide migration in one go. It is high-risk and often fails. The incremental, use-case-driven approach is far more manageable and demonstrates value continuously.
  • Plan for Ongoing Costs: Modern cloud-based data platforms operate on an ongoing operational expenditure (OpEx) model. Budget for not only implementation but also for storage, compute, software licenses, and specialized talent.

By following this roadmap, finance leaders can systematically de-risk the modernization journey, ensure alignment with core financial principles, and position their organizations to leverage data as a definitive strategic asset.

The Future Landscape: AI, Autonomous Agents, and Evolving Risks

The trajectory of data infrastructure and AI in finance points toward a future of both immense opportunity and significant complexity. While today's applications focus largely on augmenting human decision-making, the frontier is moving toward greater autonomy. Understanding this horizon is essential for strategic planning and risk management.

From Augmentation to Autonomy: The Next Frontier

Currently, human oversight is considered essential for AI-based financial strategies due to regulatory, liability, and ethical concerns. Most market participants remain wary of fully autonomous "black box" systems that generate unexplainable trades. However, the technology is evolving rapidly. The next decade may see the rise of more sophisticated autonomous AI-driven financial agents. These systems could manage discrete tasks—such as optimizing a corporate treasury's short-term cash investment across a network of bank accounts based on real-rate signals—within tightly defined parameters and guardrails.

This evolution will be fueled by advances in AI reasoning, improved model explainability (XAI), and regulatory frameworks that provide clarity on liability. The Financial Data and Markets Infrastructure (FDMI) sector, a hotbed of innovation, is already seeing GenAI accelerate the pace of change, with applications moving from pilot to rollout in months.

Structural Shifts and Systemic Risks

The widespread adoption of AI and advanced data analytics will reshape the financial ecosystem in several key ways:

  • Changing Market Structure and Liquidity: As AI lowers the barriers to quantitative strategies, less liquid markets like corporate bonds and emerging market equities may see improved liquidity and price discovery. Conversely, the potential for herding—where multiple AI models trained on similar data react identically to a global market signal—could exacerbate flash crashes or create sudden liquidity vacuums. Regulators are already considering the implications for circuit breakers and margining requirements in a faster-moving world.
  • The Rise of the Non-Bank and Transparency Challenges: AI expertise often resides with agile, less-regulated entities like hedge funds and proprietary trading firms. As these non-banks become more dominant in market-making, regulators face a challenge: a critical role in market functioning may be played by entities without intrusive supervision, potentially obscuring our understanding of systemic leverage and interconnectedness.
  • Concentration and Third-Party Risk: The provision of critical AI models and foundational data services is concentrated among a handful of large tech and data firms. This creates new interdependencies. A significant outage or a bias discovered in a widely used model could have cascading effects across the global financial system, making operational resilience and vendor risk management paramount.
  • The Arms Race in Cybersecurity and Fraud: AI is a dual-use technology. While it powers fraud detection, it also empowers adversaries. The threat of AI-generated "deep fakes" to manipulate markets or impersonate executives is real. Regulators and firms must "fight fire with fire," investing in supervisory technology (suptech) that uses AI to detect fraud, market manipulation, and cyber threats.

Preparing for What's Next

For finance leaders, preparing for this future means building infrastructure and teams that are adaptable, resilient, and ethical.

  • Infrastructure Must Be Explainable and Auditable: Future systems will need to log not just transactions, but the key data inputs and rationale behind AI-driven recommendations or actions. This is crucial for internal audit, regulatory compliance, and maintaining stakeholder trust.
  • Talent Strategy Must Evolve: The finance professional of the future will need AI literacy—the ability to formulate problems for AI, interpret its outputs, and understand its limitations. Teams will require a blend of deep financial expertise, data science awareness, and strategic thinking.
  • Ethics and Bias are Financial Risks: An AI model that inadvertently discriminates in credit scoring or that amplifies biased market narratives poses severe reputational, regulatory, and financial risks. Proactive bias testing and ethical AI frameworks must be part of the governance model.

In conclusion, the future will belong to organizations that view their data infrastructure not as a cost center, but as the core of a new financial intelligence system. This system will be capable of sensing global market signals with unprecedented acuity, analyzing them with sophisticated AI, and acting upon them with a blend of automated efficiency and human judgment—all within a robust framework of governance, security, and ethical responsibility.

Building the Indispensable Foundation

The integration of robust data infrastructure with advanced AI analytics is no longer a speculative advantage for financial institutions; it is the indispensable foundation for relevance, resilience, and growth. As we have explored, this technological backbone is central to every critical function: ensuring compliance with evolving standards like IFRS 18 and GAAP, enabling the real-time interpretation of complex global market signals, and providing the clean, governed data that powers trustworthy artificial intelligence.

The journey begins with recognizing that data is the most strategic asset on the balance sheet. From there, financial leaders—CFOs, Controllers, and FP&A directors—must step into a central role as champions of this modernization. This involves partnering with IT to architect scalable, secure platforms, establishing iron-clad governance from the outset, and relentlessly focusing on use cases that deliver tangible business value, such as automated reporting, enhanced risk management, and deep customer insight.

The path forward is not without challenges, from managing systemic risks associated with AI-driven markets to combating next-generation cyber threats. However, the greater risk lies in inertia. In a sector where the Financial Data and Markets Infrastructure (FDMI) industry itself is thriving with a 17% CAGR, the message is clear: the greatest value is accruing to those who master the data value chain.

The call to action is urgent. Begin by assessing your organization's data readiness. Define a compelling, finance-led pilot project. Build the cross-functional team to execute it. In doing so, you will not just be upgrading your technology stack; you will be future-proofing your entire financial operation, building the intelligent core that will navigate the uncertainties and capture the opportunities of the coming decade.

Frequently Asked Questions (FAQs)

As a CFO, how do I justify the significant investment in modern data infrastructure to my board?

Focus on value drivers and risk mitigation. Frame it as an investment in: 1) Regulatory Resilience: Reducing the cost and risk of compliance with new standards (IFRS 18, ESG disclosures). 2) Strategic Agility: Enabling real-time responses to market opportunities and threats. 3) Operational Efficiency: Automating manual reporting and closing processes to free up finance staff for analysis. 4) Revenue Enablement: Supporting AI-driven product personalization and risk-based pricing. Use pilot project ROI as a proof point.

What is the single most important first step for a finance department to take?

Conduct a data readiness audit. Partner with IT to inventory key data sources (ERP, CRM, spreadsheets), assess data quality (duplicates, errors), and map data ownership. This diagnostic provides a clear, factual baseline of your current state, highlights immediate risks (e.g., control gaps), and informs a pragmatic roadmap. It moves the conversation from abstract theory to concrete action.

How does the rise of Generative AI (GenAI) change the data infrastructure requirement?

GenAI, particularly Large Language Models (LLMs), places a premium on processing unstructured data (text, audio, images) at scale. Your infrastructure must efficiently store and serve vast corpora of documents—earnings calls, contracts, news—for LLMs to analyze. It also increases the need for robust governance to prevent models from using inaccurate or non-compliant data and to ensure outputs are explainable and auditable.

We use several legacy systems. Do we need a full "rip-and-replace" approach?

Almost never. A successful strategy is typically modernization, not replacement. Use cloud-based data platforms to create a new "analytical layer" on top of legacy systems. Implement connectors to ingest data from these systems into a modern data lake or warehouse. This "bimodal" approach preserves existing operations while unlocking new analytics capabilities, allowing for a gradual, lower-risk transition.

What is the role of the finance team in governing AI models that use financial data?

Finance must be a key stakeholder in the model risk management framework. This includes: 1) Validating Input Data: Ensuring the data feeds used by models are accurate and complete. 2) Reviewing Outputs: Applying professional skepticism to model-generated forecasts or recommendations. 3) Controlling Deployment: Ensuring models with financial impact (e.g., forecasting, valuation) go through a controlled release with clear ownership. Finance's expertise in controls and accuracy is critical to trustworthy AI.