Data silos and the fragmentation problem: Why enterprise brands are making $500M decisions from five contradictory models

Only 39% of technology leaders are confident their current AI investment will positively affect financial performance, Gartner reported in April 2026. The same research found that organizations with successful AI invest up to four times more, as a share of revenue, in their data foundations.

I read those two numbers together as one finding. The money is flowing, the confidence is not, and the gap sits in the layer where decisions actually get made.

When I sit with CEOs and CFOs at Fortune 500 scale, the symptom is the same. Five teams run five models against the same business, the boardroom receives five answers, and a nine-figure decision — which markets to fund, which to cut, where the next $500M of budget goes — turns on which answer wins the meeting.

At a glance

• Data silos are a storage problem — truth silos are a model problem. Consolidating your data into one warehouse fixes the first and leaves the second untouched.
• A truth silo is when each function runs its own model and reaches a different answer to the same business question.
• Marketing mix modeling is built on ordinary least squares regression, which estimates one variable's marginal effect at a time — so it cannot reconcile a second business function into the same answer.
• Correlation-based machine learning is, in MIT Sloan's words, "flawed, at best" at anticipating how a choice changes an outcome.
• The silo separation test settles which one you have: if consolidating the data would not change the answers, the divergence is in your models.

What are truth silos in enterprise analytics?

Truth silos are the state where each function in an enterprise runs its own analytics model and reaches a different answer to the same business question. Finance forecasts revenue one way, marketing measures contribution another, supply chain plans demand on a third method, and each is internally defensible. 

Even within a single function, the same divide shows up. In marketing, for instance, media ROI, trade promo, consumer promo, and shopper models routinely disagree with each other. The data underneath can be perfectly consistent and the answers still point in different directions.

A data silo is about where information lives; a truth silo is about which model interprets it. BCG describes the mechanism well: they call it semantic fragmentation, where the same financial term means different things across one organization. It’s among the most common reasons AI in finance disappoints.

The consequence is concrete — when the CFO's revenue forecast contradicts the CMO's marketing plan, and neither team can prove the other wrong, you have a problem.

Why do different teams in your company get different answers from their analytics models?

The teams across your business get different answers because each one runs its own model, built for a different question under different assumptions. Ask what is driving revenue this quarter and you can get five answers or more: finance's driver-based forecast, sales' pipeline forecast, supply chain's demand plan, the BI team's read straight off the warehouse, and marketing's channel-level view, each produced by a model, or a set of models, that was never built to reconcile with the others.

BCG puts the dynamic plainly: automating a fragmented process "scales fragmentation rather than eliminating it," and "AI amplifies every inconsistency." Adding compute to disconnected models tends to widen the spread of answers rather than closing it.

The divergence, the fragmentation, is structural. It’s not a sign that one team is careless and another rigorous. It’s simply the predictable output of running purpose-built models side by side and expecting a consensus none of them were ever constructed to deliver.

Why do your teams get different answers when you already have one data warehouse?

One data warehouse unifies your data and leaves your models untouched, which is why the answers still diverge. The warehouse solved the plumbing, and every function can now draw from the same clean, governed source. But each function still runs its own model on top of that source, and those models continue to disagree exactly as before.

This is the layer the warehouse never reached. Consolidation merges the inputs; it does not merge the methods. Five teams pulling from one warehouse, each applying its own model, still arrive at five answers, because the contradiction was never in the data.

The cost of that gap compounds at the executive level. McKinsey, with the ANA, found that 70% of CEOs measure marketing on revenue growth while only 35% of CMOs track it as a top metric. The same data reaches both offices and the two still read a different story from it.

The misdiagnosis: data quality is not the problem

Most leaders diagnose this as a data quality problem. The instinct is to clean the data, govern it harder, and consolidate it into one more warehouse, on the theory that a single clean source makes the answers converge. They invest accordingly, and the answers stay split.

The real problem is model architecture. When five functions run five models on identical, pristine data and still produce five answers, the divergence cannot be in the data; it is in the methods sitting on top of it. Cleaner inputs to contradictory models produce cleaner contradictions.

Gartner reaches the same conclusion from the investment side, warning that "siloed practices for data, AI, context and software engineering will fail to realize an AI-first ambition". The fix is not another copy of the data. It is a single model the whole business can stand behind.

Why models cannot agree, even inside one function

To see why consolidation stops at the warehouse, look at what the function-level models can and cannot do. Each was built for a narrow job, and the boundary of that job is also the boundary of the answer it can give. Marketing mix modeling is the clearest case, because it is the model most enterprises trust most.

What marketing mix modeling was built to do

Marketing mix modeling has served marketers well for decades, and it earned that trust. It takes years of spend and sales data, isolates the contribution of each channel, and tells you where the next dollar of media works hardest. For one function asking one question, it is a genuinely good instrument, which is why it is so widely relied upon.

The reason it works is also the reason it is bounded. MMM is built on ordinary least squares regression, a method designed to estimate the marginal effect of marketing variables on a marketing outcome. Inside that scope, the math is sound and the answers useful.

Where a second business function breaks the model

The model breaks the moment you ask it to reconcile a second business function — or even within the same function when you try running a separate model. Ordinary least squares estimates the marginal effect of one variable at a time, holding the rest constant. Introduce finance's revenue logic or supply chain's demand signal as live drivers and you violate that assumption; the variables move together, and the model can no longer separate their effects cleanly.

That is why marketing's model and finance's model cannot simply be merged. Each is correct within its own walls and silent beyond them. Stack them and you get two answers, not one, because neither was built to carry the other's question.

Why is your revenue growing or declining, and what is actually causing it?

Revenue moves for causal reasons, and correlation-based models cannot tell you what they are. They tell you what moved alongside revenue, not what moved it. MIT Sloan Management Review is direct on this: correlation-based machine learning, as traditionally applied, is "flawed, at best" at anticipating how a given choice will change a business outcome.

The fix is a causal method rather than a correlational one. Causal inference is built to answer the what-if question directly: what would have happened without this investment, and what changed because of it. For a deeper treatment, our complete guide to causal AI for enterprise leaders sets out how the reasoning works.

The marketing version of this question has its own answer too. If you want the channel-level view of why returns diverge across teams, our guide to how enterprise CMOs measure true marketing ROI covers it in full.

The truth silo separation test

Before you can fix the divergence, you need to know which kind you have. The truth silo separation test is a short diagnostic that distinguishes a data silo from a truth silo, and it takes one shared business question to run. Pick a question every function answers differently, such as what is driving revenue this quarter.

Run the test in three steps:

1. Gather the answers and the models. Collect the answer each function currently produces to that one shared question, and identify the model behind each answer.
2. Ask the counterfactual. Check whether consolidating the underlying data into one source would change the answers.
3. Read the result. If consolidation would change the answers, you have a data silo — a plumbing problem. If it would not, the divergence lives in the models, and you have a truth silo — a model-architecture problem.

The test matters because the two problems take opposite fixes. A data silo is solved by integration work you may already own. A truth silo is untouched by it, and most enterprises discover they are paying to solve the first while suffering from the second.

The decision layer on top of the data warehouse

Once the test points to a truth silo, the fix is architectural: a single causal model the whole business runs on, rather than one model per function. The warehouse stays exactly where it is. What changes is the layer above it, where the contradictory models live today.

POEM365 is that decision layer. It sits on top of your existing data infrastructure as a Large Causal Model, replacing the patchwork of function-level models with one causal model that answers finance, marketing, and operations from the same logic. Built on DATA POEM's FOUNT causal architecture, it measures causal incrementality rather than correlation, so the answer to what drives revenue is the same answer in every room.

The unification happens at the model layer — the layer the warehouse could never reach. Your data stays where it is and your teams keep their domains; what ends is the state where each runs a private model and the boardroom referees the result. When the whole business reasons from one causal model, the CFO's forecast and the CMO's plan come from the same source.

Frequently asked questions about data silos

What is the difference between a data silo and a truth silo?

A data silo is a storage problem, where information is trapped in disconnected systems and teams cannot see the whole picture. A truth silo is a model problem, where every team can see the same data but each runs its own model and reaches a different answer. Consolidating data fixes the first and leaves the second.

Does a single source of truth solve fragmentation?

A single source of truth solves data fragmentation, not model fragmentation. Unifying the data into one governed source is necessary and valuable, but each function still applies its own model on top of it. Fragmentation in the answers persists until the models, not just the data, are unified.

Why does causal AI matter for enterprise decisions?

Causal AI matters because enterprise decisions are what-if questions, and only causal methods answer them. Correlation tells you what moved together; causal inference tells you what would change if you acted. That distinction is the difference between describing the past and deciding the next $500M of spend.

Ready to see one truth?

The scale is what makes this urgent. The world's top 50 advertisers spent $291 billion in 2024, roughly $5.8 billion each, and Nielsen found that only 32% of marketers measure their media spending across both traditional and digital channels. Budgets at that scale are being governed by models that were never built to agree.

The upside is just as concrete. MIT CISR found that in organizations where at least a third of employees use data assets, data monetization initiatives account for 15% of total revenue versus under 5% where fewer do. Unifying the model layer is how that value gets reached.

Start by running the silo separation test on your own most-contested number. If consolidating the data would not change the answers, the divergence is in your models, and that is the problem we built POEM365 to end.