Six building blocks. Buy them separately or combined.
For anyone who wants to know technically what StriData actually builds. Not a propositional story, but an honest overview of the capabilities our projects are built from. Hire us for one building block, for several, or for the whole stack.
One architecture supporting all six building blocks. Architecture & Scoping delivers the blueprint, data flows from sources through Medallion layers into Power BI and Activate, and the Managed Data Platform keeps it running.
The design phase that locks scope, architecture and roadmap.
Before anything gets built: knowing what you're building. We assess your data landscape, design the Medallion architecture that fits your environment, and deliver a phased roadmap validated across your IT, operations and business stakeholders. A standalone product, not a pre-sales conversation in disguise.
What we actually deliver
- Assessment of your data landscape, source systems and data quality
- Medallion blueprint tailored to your environment and compliance needs
- Module proposal validated across multiple stakeholder groups
- Implementation roadmap with phasing, dependencies and risks
- Working sessions with IT, operations and business
- Written deliverable: architecture document and investment plan
When this is useful on its own
- Executive decision-making requires a roadmap with internal buy-in
- You want to set the foundation strategically before you scale
- Tech debt has to be reviewed before further investment
- You're running a vendor selection and want an independent blueprint
Medallion architecture on Azure.
A structured data foundation that turns industrial data from raw intake into operationally usable. Bronze, Silver and Gold layers, with governance and lineage built in. The base every other capability rests on, and the same foundation any future AI use case will need.
What we actually deliver
- Medallion layer structure in Azure SQL or Fabric
- Bronze: raw history, traceable storage
- Silver: clean, harmonised data with fixed definitions
- Gold: datasets ready for reporting, analytics and operations
- Governance: naming, RBAC, change management
- Deployment pipeline and version control (CI/CD for data)
- Documentation and knowledge transfer to your own team
When this is useful on its own
- You've collected data without a clear structure
- Different departments use different definitions
- A prototype has grown out of hand and needs to become production-grade
- You want the foundation first, dashboards and analytics you'll do yourself
- You have an AI ambition and need clean, governed data before any model is worth building
Industrial sources and IT systems into one model.
OT and IT data rarely come together by themselves. We build the integrations between your existing source systems and the data foundation. Vendor-neutral, with attention to repeatable patterns we reuse on later projects.
OT side (machine data)
- PLC integrations via OPC UA, MQTT and direct gateways
- Industrial connectivity platforms (IXON, Secomea and white labels such as Beijer CloudVPN)
- Embedded telemetry and historians (PI System, AVEVA Wonderware)
- Sensor layers using proprietary or standard protocols
IT side (business data)
- ERP systems (SAP, Dynamics 365, Exact, Infor)
- MES and QMS
- CRM (Salesforce, HubSpot, Dynamics)
- File and API-based sources, data warehouses, custom databases
What is explicitly out of scope
- Router or device template configuration on connectivity platforms. We work together with your existing partner for that.
- Replatforming your ERP, MES or historian. We build on top of what you have, not in place of it.
Reports that read without explanation.
Power BI isn't a standalone skill for us. It's the operational layer on top of the data foundation. We build semantic models, datasets per role, embedded reporting for customer portals, and the governance to keep it running. Not for the pretty report, but for the working view.
What we actually deliver
- Semantic model and data modeling on the Gold layer
- Datasets and reports per operational role
- Embedded reporting for white-label customer portals
- Workspace governance, RBAC and deployment
- Performance tuning on large data volumes
- Migration of existing Power BI environments
When this is useful on its own
- You already have a data warehouse and only need the reporting layer done properly
- Your Power BI environment has grown organically and needs cleaning up
- You want customer-facing reports embedded in your own portal
- You have reports but nobody opens them, and you don't know why
AI proposes. Your people validate.
The layer that goes beyond reporting without sliding into autonomous AI. The system detects anomalies, proposes interventions with reasoning attached, and waits for validation by your employee. Audit trail and learning loop built in. No black box, no shifting of responsibility.
What we actually deliver
- Anomaly detection based on historical data
- Proposal generation with reasoning and reason codes
- Validation interface for the planner, engineer or manager
- Audit trail covering proposal, validation and outcome
- Learning loop: the model learns from every correction
- Integration with your existing workflows (ticketing system, ERP, planning tool)
When this is useful
- Your teams already work with data in daily routines (Operate-ready)
- You want to move beyond dashboards with green and red dots
- You have governance requirements for compliance or customer accountability
- Autonomous AI is a no-go for you, by principle or regulation
Management that goes beyond a ticket mailbox.
What we build, we have to keep running. The Managed Data Platform service combines monitoring, small developments, advisory hours and SLA response in a fixed monthly fee. Three tiers, transparent, no surprise change-order invoices.
What we actually deliver
- Monitoring of data pipelines and data quality
- Incident response within a defined SLA
- Small developments within agreed hours
- Advisory hours for roadmap discussions and architecture questions
- Version control and scheduled updates
- Periodic health check on your platform
When this is useful on its own
- You've built a platform yourself (or with another party) that now needs operating
- You want off-the-shelf SLA tiers instead of hourly-rate debates
- Your internal data team is small and needs specialist backup
Three entry patterns we see again and again.
No fixed packages. Which combination fits you, we determine together in the Quick Scan.
You build the dashboards, we build the foundation
For teams that want to build their own reports, or outsource that elsewhere. We deliver the blueprint, build Bronze, Silver and Gold, integrate the sources and transfer knowledge. Done.
Foundation plus reporting in one engagement
Architecture blueprint, foundation, connectors and the Power BI layer in one engagement. You decide afterwards whether the Activate layer becomes a follow-up.
Someone else built it, we run it
For platforms built elsewhere that now need structural operations and development. We do a short audit, then the Managed Data Platform service starts.
And yes, you can also take the whole stack. That conversation starts with a Quick Scan.
Technical questions we get often.
Can we buy only the data foundation, without modules or reports?
Yes. Combination A exists precisely for that reason. We build Bronze, Silver and Gold including connectors, transfer knowledge, and your team builds the reporting layer and analytics themselves. Many customers start this way and expand later.
Do you host yourselves, or does it run in our Azure tenant?
Both are possible. Hosting in a StriData tenant is faster and simpler to manage. Hosting in your own tenant is typically the choice if you have data residency or compliance requirements. We discuss this in the Quick Scan.
Which Azure components do you use by default?
Typically Azure SQL Database or Microsoft Fabric for the data foundation, Data Factory or Synapse Pipelines for orchestration, and Power BI Premium or PPU for reporting. For the Activate layer it varies between Azure ML and custom services, depending on the use case.
Do you also build on clouds other than Azure?
Our focus is on Azure because in the industrial market it's by far the most used stack and because it integrates seamlessly with Power BI. AWS and GCP are also possible when your context calls for it; Azure is simply where our focus and depth lie.
What if we already have a data warehouse?
Then we build on top of it or extend it, rather than introducing a second layer. The Quick Scan determines what's usable and what's missing. We see this often: a prototype has grown out of hand and needs to become production-grade without redoing everything.
Can you also do only the Power BI implementation on an existing model?
Yes. Many organisations have a data environment in place but no workable reporting layer. We can pick up just the Power BI part, provided the underlying model has enough structure. Otherwise we advise a light restructuring first.
Is the Activate layer buyable separately, without you having built the foundation?
In principle yes, but in practice it rarely works. The Activate layer depends on clean data, fixed definitions and a working Operate layer. If those aren't there, we first do a check on the existing platform before building Activate on top.
