Unify metrics across systems
Integrate ERP, CRM and operational data into consistent definitions.
Business data engineering
Data engineering for mid-sized businesses
Manual exports and fragmented data models prevent teams from using information consistently. We develop ingestion, validation and transformation pipelines with ownership, monitoring and interfaces for downstream applications.
Where the current process reaches its limits
Start with the business workflow.
Orders, machine events and customer records often live in separate systems. Teams reconcile exports and adjust mismatched figures manually, delaying planning and operational work.
Applications and business capabilities
The right use case depends on available data, operational value and verification requirements.
Connect manufacturing and planning
Relate machine events, orders and inventory to planning decisions.
Replace recurring manual exports
Automate controlled ingestion and transformations.
Provide reliable analytics inputs
Deliver validated, documented data models for reporting.
Prepare data for AI
Supply representative, quality-checked records for models and evaluation.
Enable data-driven products
Deliver secure APIs and data services to applications.
How the technology works in a real system
A data platform is only as good as its sources and pipelines. First identify authoritative systems, update intervals and data permissions. Then design ingestion, validation, transformation, history and delivery for specific consumers. Monitoring and recovery are essential when a pipeline becomes operational.
From requirements to a verified implementation
We validate the assumptions and practical workflow before expanding implementation scope.
Identify decisions and data products
Begin with reporting, application or model consumers rather than an abstract platform.
Document sources and ownership
Map ERP, CRM, manufacturing and third-party sources.
Choose an appropriate architecture
Compare direct integrations, warehouses, lakehouses and event pipelines.
Implement pipelines and data models
Develop ingestion, validation, transformation and documented interfaces.
Secure quality and operations
Establish permissions, lineage, monitoring and recovery procedures.
When the investment makes technical and business sense
A centralized platform makes sense when teams repeatedly need the same data and source systems lack consistent histories. It also adds operational and ownership responsibilities. Small integrations can be more economical when a single application is the only consumer.
Requirements for reliable delivery
These factors define feasibility, evaluation effort and ongoing responsibility.
Authoritative data owners
Identify the system responsible for each record.
Required freshness
Choose batch or event updates according to real use.
Data quality
Measure completeness, duplication and business-rule violations.
Access and security
Define lawful and role-based distribution.
Operating responsibility
Own monitoring, incident response and pipeline changes.
Engineering the complete capability
We engineer data architectures for the people and software consuming the data. Reporting needs consistent history and calculations; applications need defined API behavior and error handling. The architecture follows those requirements rather than a preferred data stack.
Frequently asked questions: Data engineering for mid-sized businesses
Practical questions about feasibility, integration, cost and quality.
What is data engineering in the mid-market?
Developing and operating the data flows and models that make company information usable across systems.
Do we need a warehouse or lakehouse?
No. That depends on the number of consumers, data types, history and operating model.
Can existing ERP and CRM remain?
Often yes. We integrate through appropriate interfaces.
How is it different from data analytics?
Data engineering prepares and delivers reliable data; analytics uses it to explain and support decisions.
How is data quality maintained?
Through tests, ownership, lineage, monitoring and explicit exception handling.
Can we start without a large data team?
Yes, by prioritizing one useful data flow and defining ownership and support.
Discuss your project
What would you like to improve?
Describe the current workflow, systems, available data and intended result. Aleksandar Basara can discuss a technically grounded starting point.
Aleksandar Basara
Managing Director
aleks@valanor.co
+49 (0) 711 16 44 6 83
Lindenspürstrasse 22, 70176 Stuttgart
