Public or sensitive data
Data must demonstrably remain within agreed jurisdictions, data centres and access models.
Sovereign data platform
Sovereign Cloud Dataplatform
Datalab delivers the same modern Datalab Studio platform for data warehousing, dashboards, BI, analytics and AI, but on infrastructure that fits your requirements: Dutch or European cloud, on-premises, hybrid, or first a conventional cloud and later a controlled move. You keep control over data, cost and ownership without functional concessions.
Control over data
Location, access and ownership
Modular and portable
Cloud, on-premises or hybrid
BI, analytics and AI
No functional step back
When data sovereignty starts to matter
A sovereign data platform is relevant when you want to work with modern data capabilities without becoming dependent on one cloud provider or infrastructure choice.
Ownership must stay clear
Your organisation wants to know where data resides, who can access it and how audit, logging and governance are arranged.
Cloud dependency feels risky
A standard public-cloud setup may work technically, but feel too vulnerable for policy, cost or future regulation.
Infrastructure already exists
Existing servers or data centres can be valuable for storage, compute power or integration with internal systems.
Costs must remain predictable
For intensive analytics or large datasets, owned or sovereign infrastructure can be financially wiser.
AI requires controlled data
Modern AI applications are only responsible when data, models and access are technically bounded.
Datalab Studio
The same data platform, deployed differently
Sovereign Cloud Dataplatform is not a stripped-down version of our data warehouse service. It follows the same Datalab Studio approach: unlock sources, set up pipelines, retain history, create data models and make data available for dashboards, analytics and AI.
The difference is the infrastructure choice. We deploy the platform in the environment that fits your requirements around sovereignty, security, management, cost and existing IT. That can be a Dutch or European cloud, your own servers, a hybrid model or temporarily a conventional cloud environment.
Because the architecture is modular, the first choice does not have to be permanent. You can start small, prove value and later move or extend components without redesigning the entire data platform.
- The same foundation for data warehousing, BI, analytics and AI.
- Freedom of choice in cloud provider, data centre and management context.
- Designed to be moved, split or extended hybrid later.
Deployment
Four deployment models, one platform
Not every organisation has the same technical reality. That is why we design the platform so deployment does not block data-driven work.
Sometimes a Dutch or European cloud is best. Sometimes on-premises, on your own servers. Or we choose a hybrid or gradual-growth model. We advise on what best fits your situation.
No functional step back
From KPI dashboard to modern AI appliance
Data sovereignty does not mean settling for less. A well-designed platform can support the same user layer as a public-cloud data platform: management dashboards, Power BI or Tableau, R Shiny applications, Python and R analyses, forecasting, data marts and AI applications.
The craft is in the architecture. We implement data, processing, models and access so users can work quickly while sensitive data remains within the agreed environment. That allows you to build modern applications without giving up control over data.
- Relatively simple KPI dashboards and management reports.
- Advanced BI environments with Power BI, Tableau or R Shiny.
- Data science with Python, R, forecasting and simulation models.
- AI applications and controlled assistants on reliable datasets.
- Modern AI appliances when data or models need to stay close to the organisation.
Practice
Experience with organisations where data control matters
Three examples where control over data and infrastructure goes together with modern analytics.
Gemeente Weert
For the Municipality of Weert, we deliver an analytics and advanced BI platform based on R Shiny. The core: process data securely, remain cloud-independent and still make advanced analysis available to the organisation.
Optimodal
For Optimodal, we deliver an on-premises analytics product for high-volume logistics data. Because suitable servers were already available, we could use the existing technical stack and keep costs sharp.
UTR8
For UTR8, a trading firm, speed, price and data ownership were decisive. The platform supports analysis of large volumes of data for due diligence, statutory reporting and analytics.
They also trust Datalab
Approach
From sovereignty requirement to working data platform
We start with the data question and the constraints. Then we design a platform that not only works technically, but also fits policy, management, cost and users.
We deliberately keep the first delivery compact. Once the first value is visible, we build modularly with extra sources, dashboards, analytics or AI applications.
Independent advice
Datalab is not tied to cloud providers or software vendors. The infrastructure follows your requirements, not our vendor interest.
1.
Define requirements and first use case
We map data, users, risks, policy, costs and the first valuable application. Resultaat: a clear starting point for architecture and scope.
2.
Choose deployment model
We choose between sovereign cloud, on-premises, hybrid or a conventional cloud start with a migration path. Resultaat: a practical infrastructure choice with room for change.
3.
Build the platform
We set up source integration, pipelines, storage, data models, access rights and monitoring. Resultaat: a working data foundation that is secure and usable.
4.
Connect BI, analytics and AI
We deliver dashboards, R Shiny applications, analyses, forecasting or AI applications on top of the same foundation. Resultaat: usable insights without giving data away.
5.
Extend or move
New sources, other providers or hybrid splits can be added modularly later. Resultaat: a platform that moves with policy, technology and organisational growth.
Frequently asked questions
Questions about Sovereign Cloud Dataplatform
What does Datalab mean by Sovereign Cloud Dataplatform?
A Sovereign Cloud Dataplatform is a complete data platform for data warehousing, dashboards, BI, analytics and AI where you keep control over where data resides, who has access and which infrastructure is used. It can run in Dutch or European cloud, on-premises, hybrid or first on a conventional cloud provider. Also read the knowledge-base explanation about data sovereignty and cloud data platforms.
Is this different from setting up a data warehouse?
The underlying foundation is close to our data warehouse setup service. The difference is the deployment and control layer: with Sovereign Cloud Dataplatform, we explicitly design for data sovereignty, portability, cloud independence and hybrid possibilities.
Do we lose functionality if we do not choose Azure, AWS or Google Cloud?
No, not necessarily. Dashboards, BI, R Shiny, Python/R analyses, forecasting and AI applications remain possible. We do design more deliberately which components run where, and how data, access and management are bounded. Also see our services for dashboards & BI and analytics & AI.
Can the platform run entirely in Dutch or European data centres?
Yes. We can set up the platform with Dutch or European providers when that fits your requirements around jurisdiction, privacy, procurement, audit or information security. The technical trade-offs resemble the choices in our article about cloud choice and vendor lock-in.
Can you also deliver this fully on-premises?
Yes. On-premises is possible when you want to use your own servers, sensitive data may not go to the cloud, latency matters or costs need to remain predictable. We also look at management, backups, monitoring and scalability. See our explanation of data and data security.
Is a hybrid setup possible?
Yes. Hybrid is often the most practical route. Sensitive data can stay local while less sensitive processing, dashboards or experimental workloads run elsewhere. The architecture determines how those layers work together securely. In the article working in the cloud, you can read more about the broader cloud trade-off.
Can we start in a conventional cloud and move later?
Yes. That is a strong reason to design modularly. You can start quickly on Azure, AWS or Google Cloud and later move components to sovereign cloud, on-premises or a hybrid setup when policy, cost or risk requires it.
Which BI, dashboard and analytics tools remain possible?
Power BI, Tableau, R Shiny, Python, R, SQL, dbt and Apache Airflow can all be part of the solution. We choose tooling based on your users, existing environment and management context. Read more about R Shiny for more than dashboards or our Apache Airflow consultants.
Can modern AI applications also run on this infrastructure?
Yes. AI applications can be built on controlled datasets, with clear access rights and technical boundaries. Depending on the use case, this can use cloud models, private models, local inference or a hybrid setup. A reliable data foundation remains essential; see data warehouse setup.
Is sovereign or on-premises always more expensive than public cloud?
Not necessarily. Public cloud is often fast and flexible, but intensive analytics, large datasets or egress costs can add up. If suitable infrastructure already exists, or workloads are predictable, on-premises or sovereign cloud can be financially attractive. More context is available in Microsoft Fabric: 7 points to consider.
Which organisations is this most relevant for?
For organisations with sensitive data, public duties, financial or legal reporting, high analytics volumes, existing infrastructure or clear requirements around data ownership. Examples include municipalities, financial organisations, logistics, healthcare-related environments and knowledge-intensive teams.
How quickly can we start?
We usually start with a short assessment of data sources, requirements and the first use case. Then we choose the deployment model and build a compact first version that can later grow modularly.
Next step
Discuss your sovereign data platform with a specialist
In a short conversation, we map out your requirements, infrastructure and data sources and outline a feasible first step.
Schedule a call