Siloed Data: What Data Silos Are, What They Cost, and How to Break Them Down

The average organization now runs 897 applications, and only 29% of them are connected to each other, according to MuleSoft’s 2025 Connectivity Benchmark, a survey of 1,050 IT leaders. The same research found that 90% of IT leaders say data silos are creating business challenges. Siloed data is not a niche IT problem. It is the default state of most companies, and it gets worse every time a team buys a new tool. This guide covers what siloed data actually is, the three kinds of silo most organizations have, what they cost, and which approach to breaking them down fits which problem.

What is siloed data?

Siloed data is information held by one team, system, or business unit in a way that the rest of the organization cannot easily access, combine, or trust. A data silo is the container it sits in: a departmental database, a SaaS tool, a spreadsheet, or a regional system that does not connect to anything else. The data inside may be accurate. The problem is that it cannot be used alongside everything else.

The classic example is the customer who looks different depending on which system you ask. Sales sees one set of accounts in the CRM, finance sees another in the billing system, and support sees a third in its ticketing tool. Each team is right about its own data. Nobody can answer a simple question like “how much revenue comes from customers who filed more than three complaints last year” without someone stitching the systems together by hand.

How are data silos created?

Data silos form for technical and organizational reasons at the same time. Technically, they come from tool sprawl, legacy systems, and acquisitions that bring their own platforms. Organizationally, they come from teams that own their data, budget for their own tools, and have little incentive to share. Fixing only one side rarely makes silos go away.

• Tool sprawl. Every department buys software that fits its own workflow. With 897 applications in the average organization, each one storing its own data, silos are the natural outcome unless integration is planned from the start.
• Legacy systems. Older platforms often lack modern connectors or APIs, so their data stays locked in place even when everything around it gets integrated.
• Mergers and acquisitions. Acquired companies arrive with their own systems, definitions, and reporting logic, and integration work tends to lag well behind the legal close of the deal.
• Ownership and incentives. Teams that own a dataset are measured on their own goals, not on how useful their data is to others. Sharing takes effort and rarely shows up in anyone’s targets.
• Shadow IT. Spreadsheets and unofficial tools built to work around slow official processes create silos nobody in IT knows exist.

What are the different kinds of data silos?

Most organizations have three kinds of data silo, and each needs a different fix: technical silos, where systems do not connect; organizational silos, where people do not share; and definitional silos, where the data is connected but the same term means different things in different places. The third kind is the one integration projects most often miss.

Kind of silo	What it looks like	What fixes it
Technical	Systems with no connection between them; data moved by manual export	Integration: pipelines, a central warehouse or lakehouse, or data virtualization
Organizational	Teams that hold data, control access, and have no reason to share	Clear data ownership, shared goals, and governance with executive backing
Definitional	Data is connected, but “customer,” “revenue,” or “active user” means something different in each system	Harmonization: agreed canonical definitions applied across every source

A company can spend a year connecting every system and still produce reports that do not reconcile, because it fixed the technical silos and left the definitional ones untouched. Connected data is not the same as comparable data. That gap is what data harmonization exists to close.

What do data silos actually cost?

Data silos cost organizations in three ways: data that is collected but never used, decisions made on partial or contradictory numbers, and AI initiatives that stall because the data they need cannot be brought together. The last cost is the one growing fastest, because AI depends on connected data far more than traditional reporting does.

• Data that never gets used. An IDC study commissioned by Seagate in 2020 found that 68% of the data available to enterprises is never put to work, and respondents listed siloed data among the main obstacles to putting it to work. The figure is several years old, but the app counts above suggest the underlying problem has grown, not shrunk.
• Decisions on partial numbers. When finance, sales, and operations each report from their own systems, leadership meetings start with arguing about whose number is right instead of what to do about it.
• AI that cannot get started. 80% of businesses cite data integration as a major challenge for AI adoption, per the same MuleSoft research. AI models and agents need context from across the business, and siloed data gives them a fragment of it. This is a large part of what separates organizations with AI-ready data from those still stuck in pilots.

How do you break down data silos?

Break down data silos by matching the approach to the kind of silo: integration pipelines and a central platform for technical silos, master data management and harmonization for definitional silos, and ownership and governance for organizational silos. No single tool covers all three, which is why “buy a platform” alone rarely works.

Approach	What it does	Best fit
Central warehouse or lakehouse	Moves data from many systems into one platform through pipelines	Analytics and reporting across many sources
Data virtualization	Queries data where it lives, without moving it	Data that cannot or should not be copied, quick cross-system views
Master data management	Maintains one trusted record for core entities such as customers and products	The same entity existing in many systems
Harmonization	Aligns definitions, units, and business logic across sources	Connected data that still does not reconcile
Data catalog and governance	Documents what data exists, who owns it, and who can use it	Organizational silos and discoverability

A practical sequence works better than trying everything at once:

1. Find what exists. List the systems and datasets, including the spreadsheets and shadow tools. Data discovery comes first because you cannot connect what you have not found.
2. Pick one high-value question. Choose a business question that currently needs data from several silos to answer, and connect only what that question needs.
3. Connect and transform. Build the pipelines that bring that data together. This is where data transformation does the heavy lifting.
4. Agree on definitions. Settle what each shared term means before the first report goes out, and resolve duplicate records for core entities through master data management.
5. Assign ownership and access. Give each connected dataset a named owner and a clear access policy, so it stays trusted and does not quietly become a new silo.
6. Repeat with the next question. Expand one use case at a time, reusing what you built.

Why do projects to break down data silos fail?

Silo-busting projects fail when they treat a people and definitions problem as a pure technology problem, when they try to centralize everything at once, or when nobody owns the connected data afterward. The technology usually works. The organization around it does not change.

• Trying to centralize everything. A “single source of truth” program that aims to move every dataset into one platform before delivering anything tends to run for years and lose sponsorship. Starting from one business question delivers value in weeks.
• Connecting systems without agreeing on meaning. The definitional silo survives integration and shows up later as reports that do not reconcile.
• No owner after go-live. Connected data that nobody maintains drifts out of date, users stop trusting it, and teams go back to their own spreadsheets, rebuilding the silo.
• Over-correcting on access. Opening everything to everyone in the name of breaking silos creates a security problem. The goal is the right data for the right people, which is the balance covered in our guide to data accessibility.

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Frequently Asked Questions

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

A data silo is the container: a system, database, tool, or spreadsheet that does not connect to the rest of the organization. Siloed data is the information trapped inside it. The terms are used interchangeably in practice, and both describe data that one team holds but others cannot easily access or combine.

Why are data silos a problem?

They leave data unused, produce contradictory numbers across teams, and block AI initiatives that need data from across the business. MuleSoft’s 2025 research found that 90% of IT leaders say data silos are creating business challenges, and that only 29% of applications in the average organization are connected.

Can technology alone eliminate data silos?

No. Integration tools fix technical silos, but organizational silos need ownership and incentives to share, and definitional silos need agreed meanings for shared terms. Projects that buy a platform without addressing those two usually rebuild the silos within a year or two.

Why are data silos common in healthcare?

Healthcare data is spread across electronic health records, lab systems, imaging, billing, and payer platforms, often from different vendors with different data formats. Shared standards such as HL7 FHIR exist to make records from different systems interoperable, but adoption and implementation vary widely between organizations.

Where should a company start breaking down data silos?

Start with one high-value business question that currently needs data from several systems to answer. Connect only what that question needs, agree on definitions, assign an owner, and expand from there. This delivers results faster than a program that tries to centralize everything at once.