Data silos are the most common problem if medium-sized enterprises want to improve their data situation.

ERP knows the orders, CRM knows the customer contacts, the goods industry knows the stocks, the production-MES knows the utilization – but no system knows the overall picture.

Anyone who wants to answer a question about the company that needs more than one source must consolidate manually. This costs time, creates errors and delays decisions.

This article shows how data silos are broken up structurally.

Why data silos arise

Short: Short answer: ERP says one, CRM says other, commodity economy doesn't know the truth – Data silos are the largest enemy of meaningful analyses.

Short answer: ERP says one, CRM says other, commodity economy doesn't know the truth – Data silos are the largest enemy of meaningful analyses.

Breaking up data silos: system integration as foundation for data analysis** arrange cost calculator: API development, solution: interface chaos, comparison: RPA vs. API integration and system integration** System A sends data directly to system B via an interface. Simple in two systems, becomes complex in many systems (n×n problem: each system must communicate with each other).

What makes sense: Few systems, clear bidirectional data transfer, operational purpose (e.g. order from the webshop automatically to the ERP). interface development is here the technical performance. .Appendix 2: Middleware / Integration Platform A central platform (iPaaS: Integration Platform as a Service) acts as a mediator. Each system speaks only with the middleware, not with all others. n×1 problem instead of n×n.

Examples: MuleSoft, Boomi, Azure Integration Services, n8n (Open Source). For mid-sized businesses: n8n is cost-effective and flexible for many standard integrations.

Appendix 3: Data Warehouse / Data Lake All systems deliver their data to a central analytical repository.

The Data Warehouse is not an operating system, but an analytical – Data is not changed here, but evaluated. ETL processes (Extract, Transform, Load) prepare the raw data.

*When the target is analysis and reporting, not operational data exchange. Data Warehouses (Snowflake, BigQuery, Redshift, DuckDB for smaller environments) are the foundation for business intelligence.

**Appendix 4: Unified Data Model ** Instead of copying and synchronizing data, a common data scheme is defined that uses all systems.

This is the most consistent approach – and the most elaborate as it often requires changes to existing systems.

What makes sense: For Greenfield projects or as a long-term goal in building a new IT landscape.

Data mapping: The real challenge

Short: Connecting systems is often not the biggest problem.

Connecting systems is often not the biggest problem. The real problem is Data mapping: Which object in System A corresponds to what object in System B?

Typical mapping challenges:

• Customer ID is a number in the ERP, in the CRM a GUID, in the webshop an email address – how are they assigned?
• Product names vary between systems (Code vs. EAN vs. internal code)
• Date fields are interpreted differently (order date vs. delivery date vs. invoice date)
• Quantity units are not harmonised (piece vs. kg vs. pallet)

Without a clear Master Data Management (MDM) – the definition which system is the "leading source" for which entity – every integration leads to inconsistencies.

Practical steps: How to start

Short: **Step 1: Create integration mapping.

**Step 1: Create integration mapping. ** Invent all systems. For each system: what data are generated, which are consumed, what integration needs are there?

**Step 2: Define leading data sources. ** For customers, products, orders and other core objects: which system is the "Single Source of Truth"?

This hierarchy must be determined organisationally, not only technically implemented.

**Step 3: Start with a connection. **Not all Silos at the same time. The connection with the highest analysis value first – e.g. ERP → BI tool for sales and margin analysis.

**Step 4: Secure data quality. ** Before integration check: What data quality problems exist in the source? Better integrate a cleaned subset than dirty data completely.

**Step 5: Scale and expand. ** If the first integration is stable, add further sources. Each step increases analytical added value.

The connection to database development

Short: Data integration and database development are closely related: The data warehouse database design decides how flexible and performantly later analyses are.

Data integration and database development are closely related: The data warehouse database design decides how flexible and performantly later analyses are. Star Schema and Snowflake Schema are proven models for analytical databases. Anyone who works carefully here will later save development effort for any new analysis.

Conclusion

Short: ** Breaking up data silos** is a technical and organizational task.

** Breaking up data silos** is a technical and organizational task. The technology – APIs, Middleware, ETL pipelines – is detachable. The organization – data responsibility, leading sources, governance – is often the greater challenge. Anyone who addresses both creates the basis for real data-driven analyses in mid-sized businesses.

Frequently Asked Questions (FAQ)

Do we need a Data Warehouse or do we need a direct database connection to the BI tool?

A direct connection can be sufficient for a few clean sources. As soon as several sources have to be consolidated or the queries burden the production database, a data warehouse is useful.

What does data integration cost?

Simple API connection: 5,000–20.000 €. Complete integration architecture with multiple sources and data warehouse: €30,000–150.000 per complexity.

How long does it take to break data silos?

A first, functional integration can stand in weeks. A complete, stable integration landscape for a medium-sized company: 6–18 months in phases.

Should we replace our existing systems?

No. Integration means not to connect existing systems. Only if a system does not provide a reasonable interface and will not provide a system change will be relevant.

Additional notes

Security, privacy and compliance

Depending on the industry and data types, Access concepts, encryption, storage and deletion concepts can quickly become a bottleneck.

Check early on whether personal data are processed, which are legal bases and how affected rights are technically supported. .Supplier and open source components should land in a regular review: licenses, known vulnerabilities, update path.

This not only protects against incidents, but also accelerates audits and alerts – especially when public authorities or regulated markets are in play.

Breaking up data silos: System integration as foundation for data analysis

can be successfully implemented when technology, organization and measurability fit together – instead of insulated tool rollouts without process reference.

Use the overview in this article as a basis for discussion on priorities, risks and the first loadable pilot.

Intensify matching topics in category overview Blog category and check operational support via software development, IT consulting. Groenewold IT accompanies analysis, implementation and operation – from the first classification to scalable releases.

Technical sources and further links

The following independent references complement the classification on the topics of this Article:

• Bitkom – Digital Economy Association
• BSI – Federal Office for Information Security
• European Commission – Digital Strategy
• MDN Web Docs (Mozilla)
• W3C – World Wide Web Consortium

"DevOps means less tool sense than common responsibility for quality and rollout – without that, automation remains superficial."

— *Björn Groenewold, Managing Director, Groenewold IT Solutions *

Conclusion and next steps