Data platform: what it is, its building blocks and how to choose the right one for your business

What is a data platform?

A data platform is the central technical infrastructure that brings together, stores, prepares and provides all of a company's data for analysis, reporting and AI applications. It's the foundation on which data analysis, business intelligence and AI projects are built.

Three terms are often used interchangeably but describe different concepts:

A data platform is the umbrella term. It covers all the tools and layers needed to collect, store, process and analyse data.

Data architecture describes the overall design: which data sources exist, how data flows, who has access, which standards apply. It's the blueprint; the data platform is the implementation. Anyone who wants to go deeper will find the key foundations in our article on data architecture.

A data warehouse is a special form: a central database that prepares structured data for reporting and analysis. Modern data warehouses (such as Snowflake, Microsoft Fabric and Google BigQuery) are part of today's data platforms, but don't replace the overall system.

‍

The five building blocks of a modern data platform

Whether you're building a platform from scratch or extending an existing one, the following five building blocks form the core of every modern data platform.

1. Storage

This is where all data lands, structured or unstructured. Modern platforms usually rely on a combination of a data lake (for raw data, unstructured content, logs) and a data warehouse (for structured, analytically prepared data). Current architectures such as the "lakehouse" merge both concepts. Providers like Snowflake, Databricks and Microsoft Fabric have established themselves here as the most important options for SMEs.

2. Data integration

Data comes from the ERP system, the CRM, the production line, the web shop, sensors, Excel lists and external APIs. It has to flow reliably into the platform, in the right format and at the right cadence. Tools like Fivetran, Airbyte and Microsoft Data Factory automate these pipelines and keep the data current and consistent.

3. Compute

Once the data is there, it has to be prepared: filtered, transformed, enriched, combined. This step is called data engineering. Tools like dbt (data build tool) and Apache Spark are standard today. The compute layer is where raw data becomes usable information.

4. Data governance and security

Who's allowed to see which data? How is access documented? How is personal data protected? These questions aren't an afterthought, but a central component. Microsoft Purview, Collibra and Atlan offer governance solutions that bundle data catalogue, access management and compliance documentation into a single tool. Especially with a view to GDPR and the EU AI Act, governance is no longer a nice-to-have. Anyone who wants to go deeper will find the necessary foundation in our article on data security in the company.

5. Analysis and visualisation

At the end of the chain are the tools with which data is used: Power BI, Tableau and Looker for classic dashboards. Notebooks like Jupyter and Microsoft Fabric Notebooks for data science. And, increasingly, AI interfaces that allow natural-language queries against the data platform. This layer is the face of the platform for its users.

‍

The most important types of data platform

Which architecture suits which company? Here are the three dominant models.

Cloud data platform

The standard architecture for 2026. Providers like Snowflake, Google BigQuery and Microsoft Fabric offer fully managed data platforms in the cloud. Advantages: fast scaling, low initial investment, pay-per-use, regular updates with no effort of your own. Disadvantages: ongoing costs depending on usage, dependence on the provider. For the vast majority of SMEs, this is the pragmatic path today.

On-premise data platform

The platform runs in your own data centre. Advantages: full data sovereignty, predictable costs, no dependence on external providers. Disadvantages: high initial investment, your own staff required, slower scaling. For heavily regulated industries (defence, critical infrastructure) and companies with particularly sensitive data, it's sometimes still the right choice, but increasingly in the minority.

Hybrid and multi-cloud data platform

A combination of your own infrastructure and several cloud providers. According to the Bitkom Cloud Report 2025, 29 per cent of German companies already use hybrid cloud and 41 per cent use multi-cloud. Advantages: flexibility, protection against vendor lock-in. Disadvantages: greater complexity, more coordination effort. For SMEs, it's often the answer to requirements like "some data mustn't leave the EU, other data can sit in the global cloud".

‍

A data platform for the Mittelstand: 6 questions before you choose

Choosing a data platform is a strategic decision with a long-lasting effect. The following six questions help you make a well-founded choice.

1. Which data sources need to be integrated?

Draw up a list of all the systems that should be connected today or in the next two years: ERP, CRM, production data, web shop, external APIs, Excel spreadsheets, sensors. Platforms differ greatly in the pre-built connectors they offer. A good integration ecosystem saves a lot of engineering effort later.

2. What volumes of data do we process – today and in two years?

A platform for 10 gigabytes of data looks different from one for 10 terabytes. What matters isn't just the current volume, but the growth you expect. Cloud platforms scale elastically, but the cost curve isn't linear. Modelling early what will grow avoids unpleasant surprises later.

3. What are our data-protection and compliance requirements?

Do you process personal data? Are you in a regulated environment (financial services, healthcare)? Does data have to stay in the EU? The Bitkom Cloud Report 2025 shows that for 67 per cent of companies, a trustworthy country of origin for the cloud provider is a mandatory requirement. These requirements need to be clear before you choose a tool, not after.

4. What skills do we have in the team?

A data platform is only as good as the people who run it. Microsoft Fabric is far easier to adopt if the team is already at home in the Microsoft world. Databricks is worthwhile for teams with a Python affinity and data-science ambitions. Snowflake offers the lowest barrier to entry for classic SQL users. Anyone who chooses a tool no one on the team has mastered is buying dependence on consultants.

5. What does the total cost of ownership look like?

Licence and hosting costs are only one part. On top come implementation costs, training, ongoing maintenance and extensions. A realistic TCO calculation over three to five years avoids poor decisions. Cloud platforms look cheap in year one, but can become more expensive than an on-premise setup as usage grows.

6. How well does the platform connect with AI?

Data platform and AI increasingly go hand in hand. Platforms like Snowflake, Databricks and Microsoft Fabric now offer integrated AI features: from the embedding model to vector search to LLM integration. Anyone who wants to run serious AI projects in the coming years should factor this connection in from the start.

How the Mittelstand builds data platforms for AI is one of the core topics at d:u27, from 13 to 14 April 2027 in Münster. The Data Stage covers data strategy, infrastructure, governance and security – with real-world examples from SMEs.

‍

Practical tips for introducing a data platform

Three tips have proven themselves in practice and turn up in almost every successful data platform rollout.

Start small, deliver value early

The temptation is great to plan the grand project: all data sources, all reports, all use cases at once. In practice, such mammoth projects fail regularly. Successful platforms start with a manageable use case, often in sales or controlling. Within three to six months there are first visible results, and further use cases build on them.

Factor in data governance from the start

Anyone who collects data first and introduces governance later has a problem. Access rights, the data catalogue and lineage (the traceability of where data comes from) should be part of the platform from day one. That saves expensive clean-up operations later.

Invest in data literacy

A data platform only creates value if the business units can use it. Training, internal data communities, clear points of contact for data engineering and analytics: these organisational investments are just as important as the tooling. More on this in our article on AI for executives, where we look in depth at how decision-makers can support data projects effectively.

‍

Conclusion: the right data platform is the one that gets used

There's no single right data platform. There's only the one that fits your data, your requirements, your team and your strategic goals. An expensive platform that no one uses is worthless. A pragmatic platform that works in everyday life and improves decisions is worth its weight in gold.

Anyone who takes the step from scattered data silos to an integrated data platform creates the foundation for automation, AI and data-driven decisions. And in 2026, that's no longer a strategic extra, but a must.

How SMEs make the move to an integrated data platform, which architectures prove themselves in practice, and how data turns into real business value, is shown by practitioners at d:u27, from 13 to 14 April 2027 in Münster. On the Data Stage, the Mittelstand Stage and in the masterclasses, SMEs share their experiences – hands-on, approachable and with concrete lessons learned. Get your ticket now and benefit from the exchange with people who have already built data platforms successfully.