Data silos: what they are, why they form and how businesses can break them down
Seventy-six per cent of mid-sized businesses in Germany struggle with inadequate data quality and data silos. At the same time, 83 per cent have no comprehensive data strategy in place. It's a problem many recognise but few address systematically.
Data gets collected, stored and used – but not shared across the organisation. Marketing holds its customer data, sales manages its CRM entries, production tracks its machine data, finance runs its own systems. Every department knows a great deal about its own corner of the business. What the organisation as a whole could do with all of that remains largely unexplored.
This article explains how data silos form, what concrete impact they have – especially on AI initiatives – and what practical first steps businesses can take to integrate their data more effectively.
What are data silos?
A data silo forms when data is stored in a system, department or team in a way that makes it difficult or impossible for other parts of the organisation to access. The term is borrowed from agricultural storage: everything is well packed and safely contained, but isolated, separated from everything else.
In practice, this means that information which exists in one part of the business never reaches other departments. Decisions are made on the basis of incomplete data. Analyses remain fragmented. And when someone needs a cross-departmental overview, it requires a laborious manual process of pulling everything together.
Data silos aren't a sign of carelessness. They emerge for understandable reasons, often as a by-product of organic growth. That doesn't make them any less problematic.
How data silos form
Data silos rarely arise from a deliberate decision. They develop through the interaction of several factors over time.
Grown system landscapes. Companies acquire different software solutions over the years: an ERP system here, a CRM tool there, a marketing platform, an HR system. Each solves a specific problem, but they don't communicate with each other. Data stays where it was created.
Departmental thinking and ownership. In many organisations, data is treated as the property of the department that collects it. Marketing "owns" campaign data, sales owns customer contacts, IT owns system logs. Sharing requires coordination, and coordination takes time – so the silo stays in place.
Lack of data strategy. Many mid-sized businesses have no comprehensive data strategy. Without clear rules about where data is stored, who has access and how data flows between systems, silos almost inevitably develop.
Compliance and security requirements. Regulations such as GDPR or sector-specific rules lead companies to deliberately separate certain data sets. That is both right and necessary. The problem arises when this separation is applied across the board, including in cases where shared use would be entirely lawful.
Historically grown IT structures. In many mid-sized businesses, IT was long viewed as a support function rather than a strategic one. Data management wasn't a strategic priority – it was an operational task. The resulting structures are difficult to change.
Why data silos are becoming a more serious problem
Data silos are nothing new. Why are they more relevant in 2026 than ever before? Because AI makes the cost of fragmented data visible.
AI systems – whether analytics tools, automation solutions or generative models – need data. Not just the data of a single department, but data from multiple sources that has been brought together, cleaned and made consistent. Companies that try to introduce AI on top of fragmented data will quickly discover: the technology isn't the problem. The data beneath it is.
The IBM Chief Data Officer Study 2025 makes this clear: AI initiatives don't fail because of missing models – they fail because of historically grown, heavily fragmented data architectures, in which finance, sales, supply chain and service each optimise their own systems without any end-to-end view.
The consequences are felt throughout the organisation. If customer data lives in the CRM, purchasing behaviour in the shop system, complaints in the ticketing system and delivery data in the ERP – and these systems share no common data foundation – then nobody can build a complete picture of the customer. Not the leadership team, not marketing, not sales. Everyone is working with fragments.
How fragmented data structures specifically slow down data analysis in the Mittelstand is explored in our article Data analysis in SMEs: from raw data to better decisions.
The concrete problems data silos create
The effects of data silos rarely show up as a single, clearly identifiable issue. They manifest as constant friction in everyday operations – at points that are hard to trace back to one root cause.
Inconsistent data stores and duplicate maintenance. When the same customer data is maintained in three different systems, discrepancies are inevitable. Which version is current? Which record is correct? Analysis produces different findings depending on which source you use.
Poor collaboration between departments. Teams that serve the same customers or work on the same products are operating from different data sets. Coordination becomes more effortful because you first have to establish whose numbers you're working from. This can't be resolved without addressing the underlying data structure.
AI initiatives that never get off the ground. Many companies don't fail for lack of AI tools – they fail because the data foundation those tools would need simply isn't there. According to a study by maximal.digital, 63 per cent of SMEs report cost overruns in AI projects – a central reason is the laborious data cleansing and integration work that has to happen before the actual project can begin.
Compliance risks from uncontrolled data access. Paradoxically, poorly managed data silos also increase compliance risk. When nobody has a complete overview of all data holdings, it's also unclear where personal data resides, who has access and which regulations apply. That is a real risk in the context of GDPR and the EU AI Act.
How data sovereignty and the question of who controls company data relates to this topic is explored in our article Data sovereignty: definition, cloud considerations and what companies should do now.
From our conversations with Mittelstand companies, we know: this friction has become part of everyday life for most of them. It just goes by different names – coordination problems, missing data, AI projects that keep getting delayed. The same structural issue tends to be behind all of them. At d:u27 on 13 & 14 April 2027 in Münster, companies share how they've tackled it.
Breaking down data silos: approaches that work
Dissolving data silos isn't a project with a clear beginning and end – it's an ongoing strategic task. But there are concrete entry points that companies can act on today.
Take stock: what data exists, and where?
Before silos can be integrated, you need a clear picture of what actually exists. Which systems are in use? What data sources are there? What data is collected and stored in which departments? Where does customer data sit, where product data, where process data?
This inventory sounds straightforward, but many companies simply don't have it. IT teams know which systems are running – but nobody has a complete picture of all data holdings. Creating that transparency is the first step.
Technical integration: bringing data from multiple sources together
There are various technical approaches to breaking down data silos. Data warehouses consolidate data from different sources in a central database and enable cross-departmental analysis. Data lakes allow large, heterogeneous data sets to be stored in their raw form. More modern concepts such as Data Fabric or Data Mesh work with decentralised but interoperable data architectures.
Which approach makes sense depends on the size of the business, its existing IT infrastructure and its specific use cases. For mid-sized companies, pragmatic interim solutions are often more sensible than large-scale transformation projects: an API connection between two central systems, a shared data model for customer data, or a clear set of rules governing data exchange between departments.
Data management as an organisational task
Technical solutions alone aren't enough. Data silos are also an organisational problem – and that can only be addressed with organisational measures.
This means clear accountability for data quality. Rules for sharing data across business units. Agreed definitions of what key terms mean – because if marketing and sales define "customer" differently, even the best database architecture won't help.
Data governance – the systematic management and stewardship of company data – isn't just a large-enterprise concern. Mid-sized businesses also need clear rules about how data is collected, stored, shared and used. Not as a bureaucratic exercise, but as the foundation for everything that's meant to happen with data.
How companies have set up data governance not as a compliance burden but as a strategic lever – and where they've got stuck – is one of the central topics at d:u27 on 13 & 14 April 2027 in Münster. Those currently working through this in their own organisations will find people there who have already been down the same road.
Start with one concrete use case
Abstract data strategy projects often fail because they're too large in scope. A more effective approach: identify a specific use case where missing data integration is causing real problems today – and fix it.
Example: customer service has no visibility of what sales discussed with a customer, because the information lives in different systems. Solution: integrate those two data systems and define a shared data model for customer contacts. It's a small, bounded project – but it delivers measurable value immediately and builds confidence for the larger change process.
What data-driven decision-making looks like when data is finally available across departments is explored in our article Data-based decision-making in SMEs.
Conclusion: data silos are solvable – but not as an afterthought
Data silos don't appear overnight, and they don't disappear overnight either. But they're not an immutable fact of life. Companies that address their data structure systematically lay the foundation for better decisions, more effective collaboration between teams – and AI initiatives that actually work.
The first step isn't a major transformation. It's clarity: what data do we have? Where does it live? Who has access? And which specific use case would immediately work better if data from different sources could talk to each other?
Those who don't want to work through those questions alone will find the right people at d:u27 on 13 & 14 April 2027 in Münster. On the Data Stage and in masterclasses, data teams from mid-sized businesses speak openly about their experiences – what worked, what didn't, and where they stand today. Secure your ticket now.
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