From silos to data-driven value: Why your data costs you millions — and how to fix it

Data has become one of the most powerful strategic assets of the digital age. But even though companies generate more data than ever, much of it remains isolated in silos. This doesn’t just slow down innovation, it is extremely expensive. Data silos quietly drain millions through rework, delays, bad decisions, and missed opportunities. Poor data quality results in 15–25% revenue loss per company on average ^[1]. The good news: An estimated two-thirds of these costs are identifiable and can be permanently eliminated by addressing the causes rather than the symptoms. Learn how you can get rid of your data silos with Dataspaces and Cofinity-X as your access point.

Your daily price of data silos

What exactly are data silos? Data silos arise when organizations store information in separate systems, formats, or data models that cannot be easily combined or shared. Over time, individual teams or departments often create their own approaches to managing information, which results in isolated data pools that do not work together. Although companies possess large amounts of valuable data, these fragmented structures make it difficult to protect the data, exchange it or realize its full economic potential. The absence of shared standards limits collaboration and innovation and even prevents organizations from gaining a complete and consistent view of their data treasure. Even worse, the business impact is severe:

1. Time and productivity
   ‍Data silos are massive barriers. Teams spend hours or even days searching for, cleaning up, and reconciling information. This hidden work rarely appears in budgets but adds up to millions of dollars per year. More than one in four organizations report annual losses exceeding USD 5 million due to poor data quality, and about 7% report losses of USD 25 million or more ^[2].
2. Errors in operation and analysis
   Incomplete or contradictory data leads to bad decisions, process inefficiencies, and operational failures. Gartner research shows that companies lose $12.9M/year on average due to poor data quality ^[3].
3. Security risks
   Data silos create blind spots that increase vulnerability: if you don’t have a complete overview of the data you possess, how can you reliably protect it from misuse, loss, or unauthorized access?
4. The risk of scaling bad data
   Many companies are deploying Artificial Intelligence (AI) agents to support their workforce. But if the underlying data is inconsistent or incorrect, AI will simply scale those mistakes. Instead of improving efficiency, poor data quality causes errors to propagate faster and across more processes.
5. Missed opportunities
   Data silos do not only cause massive hidden costs and endanger your business. A lack of data readiness has additional negative consequences for initiatives in the areas of data-driven business such as AI and dataspaces.
6. The 1-10-100 rule: Bad data becomes expensive fast, and it costs more than once
   The 1‑10‑100 rule, introduced in 1992 by George Labovitz and Yu Sang Chang, shows how the cost of poor data quality grows exponentially the later you address it:
   
   • $1 — Preventing errors at the source: Validating data at the moment it is created is the cheapest and most effective way to ensure quality.
   • $10 — Fixing errors later: Once incorrect data is inside your systems, correcting it becomes significantly more expensive.
   • $100 — Doing nothing: Unresolved data errors spread across processes and systems — resulting in operational failures, rework, and major financial losses.

This framework illustrates clearly: the longer bad data remains in your organization, the faster its cost multiplies.

From isolation to data collaboration with dataspaces

Dataspaces solve the core problem of disconnected data. They create a secure, federated, and interoperable environment where companies can share data with confidence.

Dataspaces, as defined by the International Data Spaces Association (IDSA) and Gaia‑X, represent a modern approach to unified, secure and sovereign data exchange based on:

• Data sovereignty
• clearly defined usage policies
• shared standards
• verified digital identities
• trust based participation

Participants follow a certification framework, and data usage is governed through contractual agreements. Dataspaces help companies move from data silos to connected value chains and provide the foundation for scalable, data‑driven value creation. But the impact of dataspaces reaches far beyond individual organizations, influencing entire industries. They are not merely technical infrastructures but strategic building blocks for a new federated data economy.

Catena-X – the dataspace for the automotive industry

For the automotive industry, dataspaces are not just a concept, but a lived reality. Catena-X is a collaborative dataspace  that enables standardized, secure, and sovereign data exchange across the automotive value chain. Its goals include:

• increased supply chain resilience
• improved sustainability
• reduced costs
• new value creation opportunities

OEMs such as Volkswagen, BMW Group, Mercedes and Ford are actively participating and engaging their supply chains, as are the largest suppliers.

Cofinity-X is the leading dataspace operator

Cofinity-X is the first operating company of the Catena-X ecosystem, bringing the idea of an interconnected, digital automotive supply chain to life. It was established on January 26, 2023, by ten companies:

BASF, BMW Group, Henkel, Mercedes-Benz, SAP, Schaeffler, Siemens, T-Systems, Volkswagen, and ZF.

In detail, Cofinity-X provides the technical infrastructure for dataspaces such as Catena-X including digital identities, security and compliance.

Cofinity-X takes on the following tasks:

• Running the required technical infrastructure
• Ensuring sovereignty, security, and interoperability, the core principles of a dataspace
• Onboarding of dataspace participants and issuance of digital identities
• Provision of a marketplace for certified solutions (e.g., Cofinity X Marketplace)
• Enforcement of shared standards
• Management and ongoing support of participants

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Experience dataspaces in action with the Cofinity-X Dataspace Lab

To help companies move from theory to practice, Cofinity-X offers the Dataspace Lab,  a fully functional, cost-effective dataspace test environment that enables organizations of all industries and backgrounds to experience secure data exchange in real-world scenarios. Designed as the easiest entry point into the world of dataspaces, the Dataspace Lab allows business users, developers, data teams and scientists to explore standardized data sharing, test concrete use cases, and gain hands-on experience without complex setup or upfront investments. It provides a safe environment to understand how dataspaces work in practice, validate business ideas, and accelerate your journey toward scalable, data-driven value creation.

Get started productively

But how can I practically move from silos to data-driven value? The foundation for technological innovation with AI and dataspaces is a reliable database. The quickest way to stop wasting money and turn your inefficiency into business opportunities is to clean up. The Technical Consultancy of Cofinity-X supports your journey into the world of dataspaces! Explore our Connectivity Package which allows you to get started with Catena-X. We help you connect your systems and transform your data into common data models. Our dataspace experts will guide you through the process, ensuring smooth and fast implementations.

Want to stop wasting money on siloed data? Let’s build your data-driven business together.

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Explore more:

• Learn more about the smart architecture of dataspaces here.
• Want to try? Check our fully functional dataspace test-environment here.
• Explore our consultancy offerings such as our Connectivity Package on our marketplace.

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[1] https://sloanreview.mit.edu/article/seizing-opportunity-in-data-quality/
[2] https://www.forrester.com/report/millions-lost-in-2023-due-to-poor-data-quality-potential-for-billions-to-be-lost-with-ai-without-intervention/RES181258
[3] https://www.gartner.com/en/data-analytics/topics/data-quality