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Build a trusted data foundation for analytics, AI and governance. BE-terna integrates, models and delivers your data as reusable products your teams can trust, completing what your existing systems are missing rather than replacing what already works.
AI-ready data is unified, contextual, live, quality-controlled, traceable and reusable. BE-terna helps you engineer this foundation as governed data products, so analytics, AI agents and business teams work from the same trusted source.
In complex hetrogenous enviroments we connect to what already works: Microsoft Dynamics 365, SAP, Infor, databases, SaaS platforms, mainframes, files and event streams. We then design scalable data architectures using open lakehouse and medallion patterns, with platforms such as Qlik Talend Cloud, Qlik Open Lakehouse and Microsoft Fabric.
The result is a data foundation that reduces AI risk, improves decision quality and keeps future technology choices open.
Before data can support analytics or AI, it needs to be connected. BE-terna integrates data from SAP, Microsoft Dynamics 365, Infor M3, Oracle, SQL Server, Salesforce, SaaS platforms, files, mainframes and event streams. We use batch integration where it is sufficient and real-time streaming where business processes depend on current information.
Outcomes:
A modern lakehouse combines the flexibility of a data lake with the reliability of a data warehouse. BE-terna designs the right architecture for your use cases, platform strategy and governance requirements.
Fast pipelines do not create value if they deliver unreliable data. BE-terna builds quality, lineage and observability directly into the pipeline and defines KPIs once in a shared semantic layer.
AI agents are only useful when they can access the right governed data. BE-terna connects trusted data products to AI platforms such as Azure AI Foundry, Microsoft Copilot, Amazon Bedrock, Microsoft Fabric and Qlik Cloud.
BI modernization works best in stages. BE-terna helps you move from legacy BI landscapes to platforms such as Qlik Cloud and Microsoft Fabric while keeping today’s reports running.
BE-terna already offers Qlik Academy and Qlik Cloud Analytics Academy training formats, which can support enablement after go-live.
Not every data engineering project needs to start from zero. BE-terna Data Accelerators provide preconfigured pipelines, models and data products for common ERP and analytics use cases.
We map your systems, pipelines, reports, business logic, use cases and governance gaps, then define a backlog and clear target architecture for business and IT.
Typical duration: 2 to 4 weeks
We design the target foundation around your most relevant use cases and validate it with a focused pilot, such as a first data product, real-time pipeline or SAP extraction.
Typical duration: 3 to 6 weeks
We build the new foundation while reports and processes keep running. Data products and pipelines go live step by step, proving value before wider rollout.
Typical duration: 3 to 12 months, scope dep.
Once the foundation is trusted, we support cutover, enablement and long-term operation. Training and Managed Services help your teams own, run and evolve the platform after go-live.
Ongoing after go-live
Most consultancies cover only one part of the journey. BE-terna brings operational process knowledge, data engineering and AI-readiness together in one delivery model. We help organizations move from strategy and framework design to tool configuration, adoption and continuous improvement across all our business solutions.
Millions of transactions in the ticketing and access system, but limited options for fast business analysis: That was the challenge facing Dachstein Salzkammergut cable cars. Together with BE-terna, the company built a centralized analytics platform based on Qlik Cloud that brings together sales, access, and ride data from SKIDATA, enriches it with business context, and makes it available for management, controlling, and operations. Today, users can analyze more than 50 million data records in seconds, with updates every 15 minutes, near real time reporting, and mobile access on the mountain. The project went live exactly on schedule.
AI ready data is data that passes six tests at once. It is unified across every system that matters, contextual enough that business and machine read it the same way, live so it reflects today and not last week, quality controlled with measurable accuracy, traceable through end to end lineage, and delivered as products your teams can reuse. Miss one of the six and AI initiatives stall. Get all six right and analytics , AI and governance run on th e same foundation.
No, and that is deliberate. We build the data foundation that agents and models need to produce trustworthy answers, and we integrate that foundation with your AI platform through MCP servers, vector databases and controlled API access. The agent runtime itself, whether that is Azure AI Foundry, Amazon Bedrock, or another platform, sits on top of what we deliver. You keep that decision open and vendor independent. Where helpful, we can support with agent skill definitions and industry expertise around the data layer.
A traditional data warehouse was designed for one use case: structured data and static reporting. It defines the schema up front, loads data in batches, and serves fixed dashboards well. AI ready data needs more. It needs context for unstructured content alongside structured tables, shared business meaning across systems, near real time freshness, and delivery as reusable products rather than one off extracts. Most warehouses can contribute to that foundation, but they rarely are the foundation on their own.
A lakehouse combines the flexibility of a data lake with the reliability of a data warehouse. Data can be stored cost efficiently in cloud object storage, while open table formats add structure, metadata, updates, deletes, governance and reliable access on top. This allows companies to land raw data quickly, refine it step by step, and create trusted data products for reporting, analytics, AI and RAG use cases. Compared to a traditional warehouse only approach, a lakehouse can reduce unnecessary data movement and ingestion costs as data volumes grow.
Qlik Open Lakehouse is a managed lakehouse built on the Apache Iceberg open table format. It handles ingestion, optimization, governance and lineage as one platform, so customers get lakehouse benefits without running the infrastructure themselves. The "open" part matters: because Iceberg is an open standard, the data can be read by multiple query engines, including Snowflake, Databricks, Spark and others, in parallel. You stay independent of any single engine while getting a fully managed experience.
That is specifically what we design against. We favor open table formats like Apache Iceberg, because they let the same data be read by multiple query engines without duplication. If you start on one cloud warehouse and later want to switch, it is a configuration change, not a rewrite of your pipelines. Qlik Talend Cloud applies this principle through Iceberg natively. Microsoft Fabric stores data in the Delta format on OneLake, which is open, but the surrounding platform is Microsoft managed; moving off Fabric is possible but more involved than a simple engine swap. We design with both realities in mind, so you know what you are committing to before you commit.
Yes. We use an outbound connection pattern: a secure gateway installed inside your network talks out to the integration platform, rather than the platform reaching in. No inbound ports opened, no firewall exceptions required. Sensitive fields can be masked or pseudonymized in the pipeline itself, so compliance is handled at source. On-premise systems stay on-premise for as long as you want them there.
We use log based change data capture. Instead of querying the source database directly, we read the transaction log, which every database writes anyway. Changes flow into the target system continuously, with almost no load on the source, and no batch window to wait for. This is why we can deliver near real time replication from SAP, Oracle, SQL Server, mainframes and other transactional systems without operational teams noticing.
Regulation shapes how data moves through pipelines and how AI systems access it. For GDPR, sensitive fields can be masked or pseudonymized in the pipeline itself, with lineage tracking every use. For the EU AI Act, training and input data used by AI systems needs traceability and quality controls, both of which are engineered into our pipelines. For DORA in financial services, reliable pipelines are part of operational resilience, not separate from it. We partner with your compliance and governance teams, and program-level governance programs live on our Data Governance page.
