The Information Warehouse

For FP&A teams, the promise of AI is real, but the path to production is often anything but straightforward. Many AI initiatives struggle to scale due to foundational issues, particularly around data quality and alignment. Studies show an estimated 88% of pilots never make it to production (CIO), and about 85% collapse under messy, misaligned data (Forbes).

In Article 1 of The Next Wave, I argued that this failure pattern isn’t random. It’s what happens when organizations skip the groundwork. AI built on shaky foundations doesn’t deliver value and won’t last. One of the most overlooked foundations for scalable AI is what we call the Information Warehouse.

Today, we focus on the Information Warehouse. I’ll break down its core elements, show what it looks like in real systems, and suggest first steps for FP&A teams ready to build.

What is the Information Warehouse?

The Information Warehouse is the structured framework that makes data usable. It’s where raw inputs become business-ready information – dimensions, rules, hierarchies, and logic that reflect how your organization thinks and operates.

This layer is what keeps teams using the same definitions. Finance defines “revenue” one way, operations another, and dashboards start contradicting each other. The Information Warehouse enforces shared definitions and governed logic, so everyone is working from the same version of the truth.

Unlike traditional data warehouses or lakes, which store data without context, the Information Warehouse adds meaning. It connects raw data to real analysis, making planning, analysis, and decision-making reliable.

Why It’s Critical for AI and Advanced Use Cases

AI is only as good as the information it’s built on. Without the Information Warehouse, machine learning models and AI assistants end up ingesting inconsistent, incomplete, or outright dirty data – which leads to flawed forecasts, contradictory dashboards, and AI appear confident but deliver incorrect results. For FP&A teams, this means scenario models that run on shifting definitions – or worse, models that can’t be trusted at all.

The Information Warehouse creates the conditions for higher-level tools to succeed: BI dashboards that everyone can trust, scenario models that run on consistent definitions, and AI agents that can query and act on planning figures with confidence.

And the payoff grows with scale. The value of the Information Warehouse multiplies as more data sources and use cases connect, and it eventually becomes the system every function depends on.

Five Core Elements of the Information Warehouse

Building an Information Warehouse is a multi-step framework. These five components form the backbone of a reliable, scalable Information Warehouse.

1. Data Integration
The effectiveness of the Information Warehouse depends on the quality and consistency of its data inputs. Integration—through APIs, data pipelines, and connectors—is what ensures timely, accurate, and usable information flows from source systems into the warehouse.

Today, integration isn’t just about nightly batch uploads. APIs, ELT/ETL pipelines, and orchestration frameworks (often Python-based) now enable near real-time flow from ERP, HR, CRM, operational systems, and even streaming data. Modern platforms like Snowflake or Databricks can store and query large datasets, but raw data alone doesn’t make a system intelligent.

The Information Warehouse adds structure, semantics, and governance to that raw flow, and transforms it into business-ready information. That distinction turns disconnected data lakes into a consistent foundation FP&A teams can rely on.

2. Business Logic, Rules & Structure
If integration moves the data, business logic makes sense of it. It’s where raw data turns into decision-ready information that reflects how the business operates. Dimensions, hierarchies, consolidations, and validation rules give the warehouse structure and meaning so it mirrors reality.

This is also where governance takes root. Shared rules, calculations, and versioning ensure that everyone applies the same definitions and that those definitions evolve transparently over time. When finance looks back, they can see both the numbers and the reasoning behind them.

Without shared structure, teams often spend more time debating definitions than making decisions.

3. Semantic Alignment: “The $1 Trillion AI Problem”
This is where data learns to speak the same language across the organization. Finance, operations, and strategy agree on what “revenue” means, what’s included in “profit margin,” and how metrics are calculated. The semantic layer translates technical information into business terms that everyone can understand and trust.

The Open Semantic Interchange (OSI) calls inconsistent metadata the $1 trillion AI problem. When definitions drift, both BI dashboards and AI models start to lose credibility. The semantic layer prevents that by enforcing consistency across systems and reports, ensuring that every number, model, and metric traces back to a single definition.

When the semantics align, insight is explainable, and collaboration is faster.

4. Data Quality & Governance
Governance must be built into the system’s core infrastructure. In a recent post, I argued that “Generative AI isn’t the risk – bad oversight is.” That risk materialized in a big way when Deloitte had to refund part of a government contract after AI-generated sections in a report were found to include fabricated citations and quotes (AP News). When AI fails, it’s usually a governance problem, not a technical one.

Validation, cleansing, and anomaly detection keep noise out. Clear ownership and accountability policies define who owns what. Monitoring brings visibility so you see when things go off the rails. But that’s not enough on its own. Every workflow should record what data was used, who reviewed it, and when, just as you’d demand for financial reporting or compliance.

If you can’t explain precisely which model (or version, or prompt) produced a number (and who reviewed it!) the downstream AI logic is a black box.

5. Performance & Scalability
Accuracy alone isn’t enough – the system has to stay fast as it scales. The Information Warehouse should deliver insights as quickly with ten sources as it did with one. Performance is what turns a well-designed system into one that people use.

Architectural choices like in-memory processing, caching, and parallel execution keep analytics responsive as data volume and complexity grow. Partitioning and elastic compute ensure that workloads can expand without bottlenecks. These choices are what keep decision-making in real time rather than “refresh and wait.”

Scalability determines whether the system gets adopted. When performance holds up, people rely on it, and usage grows.

Common Pitfalls and How to Avoid Them

Even well-designed systems can fail if built when shortcuts pile up. When it comes to the Information Warehouse, the pitfalls usually fall into three categories:

1. Architectural Fragility

In 2012, Knight Capital Group lost $440 million in under an hour due to a software deployment error that activated outdated trading code. The glitch triggered a flood of unintended trades and nearly bankrupted the firm. The root cause? Legacy code that hadn’t been fully retired and a lack of safeguards in the deployment process (New York Times).

This kind of fragility isn’t limited to high-frequency trading. In finance and analytics, similar risks emerge when brittle integrations, hidden dependencies, and duct-taped ETL scripts are scaled without proper refactoring. What starts as a shortcut can quietly become a liability until it breaks under pressure.

Fix: Refactor before you automate. Clean up the foundation before adding complexity.

2. Definition Drift

AI and BI projects rarely fail because of missing data, but because teams can’t agree on what that data means. Finance defines “customer churn” one way, operations another, and dashboards quietly contradict each other. StrategySoftware found that 49 % of organizations cite mismatched semantics as their biggest barrier to AI readiness(Strategy).

This is the “$1 trillion AI problem” identified by the Open Semantic Interchange: inconsistent metadata erodes trust and cripples adoption.
Fix: Build and enforce a shared semantic layer early. Align on definitions and get everyone speaking the same language before you model anything.

3. Pilot Paralysis

AI projects don’t always fail because of bad data or flawed models – many stall out before they ever reach production. A 2025 survey by S&P Global found that 42% of companies abandoned most of their AI initiatives, up from just 17% the year before. On average, organizations scrapped 46% of AI proof-of-concepts before they made it to production (workos.com).

One of the biggest culprits? What experts call “pilot paralysis.” Teams launch proof-of-concepts in safe sandboxes but fail to design a clear path to production. Engineering teams spend quarters optimizing model performance while integration, compliance, and user training sit in the backlog. When it’s finally time to go live, the business case is still theoretical, and the project stalls.

This pattern is especially common in finance, where the instinct is to perfect every detail before releasing anything. But in fast-moving environments, waiting for perfect often means delivering nothing.

Fix: Start with a real business problem and deliver a working solution quickly, even if it’s imperfect. Early wins build trust, surface real-world feedback, and create momentum. As RAND’s research notes, successful AI projects focus on solving practical problems, not showcasing technical sophistication (rand.org).

First Steps for FP&A Teams

Getting started with an Information Warehouse doesn’t require a massive, multi-year project. The key is to focus on structure before scale, and get agreement on key definitions before expanding.

• Start with shared definitions. Before any modeling or integration, agree on how core metrics like revenue, margin, and cost are defined across finance and operations. Consistent semantics are the foundation for everything that follows.
• Inventory systems and data sources. Map where critical data lives today (ERP, CRM, HR, and operational systems) and identify overlaps, gaps, and ownership.
• Establish early pipelines for key data flows. Connect one or two priority streams (for example, actuals from ERP and workforce data from HR) to prove end-to-end flow and validate governance processes.
• Embed governance from day one. Assign data owners, implement validation and audit checks, and create alerts for quality issues before the first dashboard goes live.
• Iterate and communicate. Treat the warehouse as a living system. Gather feedback, measure adoption, and refine continuously as definitions, processes, and use cases mature.

These early moves demonstrate quick wins to build confidence, proving value while laying the foundation for expansion.

What’s Next

The Information Warehouse connects scattered data to clear insight. It’s where data becomes reliable and ready for AI. This is what transforms disconnected systems into a single, reliable source of truth for planning, forecasting, and decision-making. Start small, build momentum, and you’ll create the foundation your AI and analytics initiatives need to thrive.

In the next article, we’ll move from foundation to application. We’ll explore how modern BI tools build on the Information Warehouse to create a single version of the truth using governed metrics, shared definitions, and timely analysis to turn data into insight that drives better decisions.