Data Silos: What They Are, and the One Integration Cannot Fix
A data silo is data trapped where others cannot use it. Here are the causes, the standard fixes — and the silo that survives every integration project.
A data silo is data held where the people who need it cannot get to it, or cannot combine it with anything else. The familiar causes are structural: separate systems bought by separate teams, no integration between them, and no shared owner.
The familiar fix is integration: connect the systems and centralize the data. It works on the technical silo. It does not touch the other kind — two systems already connected, already feeding the same warehouse, whose records still cannot be joined because the same campaign, customer or asset is named differently in each. That silo is semantic, and every integration project that has ever been declared successful has left some of it behind.
What is a data silo?
Data held where others cannot reach or combine it.
Salesforce’s definition of the problem (accessed 2026-09-11) covers the familiar version: data isolated in one team’s system, invisible to everyone else.
The second half of the definition is the part usually left implicit. A silo is not only data you cannot reach. It is also data you can reach and cannot use together with anything else, which is a different condition with a different cause and a different fix. Most marketing organizations have solved the first and assume that means they have solved the second.
Why they cost more than they look like
The cost is not storage; it is every decision made on a partial view.
Oracle’s account of why silos are problematic (accessed 2026-09-11) sets out the usual costs: duplicated effort, inconsistent reporting, missed opportunity.
The cost that does not appear on any list is that partial views are not experienced as partial. A report built from three of five sources looks exactly like a report built from five. There is no gap on the screen, no warning, no asterisk. Decisions get made with ordinary confidence on evidence that is missing a quarter of the picture, and nobody involved is being careless.
That is also why silos survive so many attempts to remove them. The pain is real but diffuse, it never has a single owner, and it is never the most urgent thing in any given quarter.
The three types of silo
Technical, organizational, and semantic.
| Type | What causes it | What it looks like | What fixes it |
|---|---|---|---|
| Technical | Separate systems, no connection between them | The data exists and you cannot get at it | Integration, warehousing, pipelines |
| Organizational | One team owns the data and has no reason to share | You can technically get it and are not permitted to, or do not know it exists | Governance, ownership, incentives |
| Semantic | Systems are connected and describe the same thing differently | You have all the data and it will not join | Agreed definitions and values, enforced at creation |
The first is a plumbing problem, the second a political one, the third a vocabulary one. They are usually tackled in that order, which is sensible, and the order is also why the third is the one most organizations still have.
Data silos vs information silos
One is about systems; the other is about people not sharing what they know.
The terms get used interchangeably and describe different failures. An information silo is knowledge that stays inside a team: context, history, the reason a decision was made. It is a communication and culture problem, and no integration project touches it.
A data silo is records a system holds. It has technical and semantic causes and technical and semantic remedies.
The distinction matters because the remedies do not transfer. Buying a data platform does not make teams share what they know, and running better cross-team rituals does not make two systems agree on what a campaign is called. Programs that conflate the two tend to under-deliver on both.
The standard remedies, and what they reach
Integration, warehousing and centralization solve the technical silo.
Profisee’s guide to eliminating silos (accessed 2026-09-11) lists the standard approaches, and they work as advertised on the problem they target.
- Integration and pipelines move data between systems. Solves: reach. Does not solve: whether the records mean the same thing.
- Warehouses and lakes put everything in one place. Solves: reach, at scale. Does not solve: two rows about one campaign under two names sitting in the same table.
- Centralization of ownership gives the data an accountable owner. Solves: the organizational silo. Does not solve: values already recorded inconsistently in upstream systems.
Each row’s second sentence is the same shape, which is the point. The standard remedies address location and access. None of them addresses meaning, because meaning was set upstream, at the moment each record was created, by whoever created it.
The ownership layer: Data governance explained — who owns which field, and where rules are enforced.
The silo that survives integration
Two connected systems, one campaign, two names, no join.
Here is the scenario in full, because the argument turns on its specifics.
A marketing team runs a Q3 brand campaign across North America. The ad platform records it as Q3_Brand_NA. The CRM, fed by a different team working from the campaign brief, records it as Q3 Brand North America. Both systems are integrated. Both feed the same warehouse nightly. The pipeline is healthy and has never failed.
The analyst writes a query joining spend to pipeline on campaign name and gets nothing. Or worse, gets a partial result, because a third of campaigns happen to match and two thirds do not, and the report renders with no indication that it is missing most of its input.
“The integration finished and the reports still disagreed. That is when they called it a silo problem again.” — Zach Lewis, Principal CSM / Team Lead, Claravine
Across Claravine’s enterprise customer conversations, integration failures, gaps and fragility blocking data flow is the most frequently raised pain in the corpus, present in 105 accounts. It is worth reading that number carefully alongside this section: the pain persists at scale after organizations have invested in integration, which is what you would expect if integration were addressing a different problem from the one causing the symptom.
One team described what changed once the underlying descriptions agreed.
“Now, we can collectively optimize the customer experience rather than have siloed brand activities.” — unnamed, multinational healthcare company
The phrase “siloed brand activities” is exact. The activities were siloed, not the systems — the systems had been connected for years.
What actually has to change
The naming has to be agreed and enforced where the record is created.
Not corrected downstream. A value fixed in the warehouse is fixed in one place, while the ad platform, the CRM and every export taken from them keep the original.
Three steps, and the order matters:
- Agree the shared dimensions. Which fields every system must carry identically — campaign identity, channel, region, business unit. A short list, decided once.
- Close the values. Permitted lists rather than free text, so
Q3_Brand_NAandQ3 Brand North Americacannot both come into existence. - Enforce at creation. In each system, at the moment the record is made, including by the agencies and regional teams who make many of them.
This does not replace integration and is not an alternative to it. Integration remains necessary; it is simply not sufficient, and the gap between necessary and sufficient is where the semantic silo lives.
The standards layer: Explore data standards — agreed fields and permitted values, applied where records are created.
Frequently asked questions
What does data silos mean?
Two conditions, and the second is the one that survives most remediation programs: you cannot reach the data, or you can reach it and it will not combine with anything.
Why are data silos problematic?
Because every decision made on a partial view is made on a partial view, and a partial view looks identical to a complete one on screen. There is no warning that a report is missing a quarter of its input.
What are the three types of silos?
Technical, organizational and semantic. The third is the one integration does not fix, because it is a disagreement about meaning rather than a gap in connectivity.
Can you give an example of a data silo?
Two connected platforms reporting the same campaign under two different names. The pipeline works, the warehouse has both rows, and the join returns nothing or a misleading partial result.
Do data silos and information silos mean the same thing?
No. Information silos are about people not sharing knowledge; data silos are about systems and naming. The remedies do not transfer between them.
Sources
- Salesforce, “What Are Data Silos & Why is it a Problem?” (accessed 2026-09-11) — the category definition.
- Oracle, “What Are Data Silos? Why Are They Problematic?” (accessed 2026-09-11) — the cost framing.
- Profisee, “What Are Data Silos and How to Eliminate Them” (accessed 2026-09-11) — the standard remedies.
