Every Business Has Data.
The Question Is Whether It's Working For You.
You've probably heard that you need a "data warehouse" or a "modern data stack." But what does that actually mean for a company your size? And what does it really cost? This guide is meant as orientation, not as a substitute for a proper assessment of your data landscape with the customer.
Strip away the buzzwords and what you're left with is an engineering pattern that's been around since the early 1990s. At its core, it's about consolidating your different data sources by pulling data from your most important systems into a set of so-called Data Marts that complement each other and provide a unified, analytical view of your business: a single source of truth you can actually rely on.
A Data Mart is simply a clean, detailed collection of tables describing one aspect of your business, without that view being limited to a single source system. Think of a data warehouse as a modular system made up of these Marts, each of which can evolve as your business does. The Marts contain the business logic that people across your company already reference when they build reports, run ad-hoc analyses, or try to answer a business question, but with the advantage of being auditable, governable and clean.
Because each Mart represents the single authoritative source for one area of your business, reporting becomes standardised inside departments and inconsistencies in reporting explainable when identified in a meeting. Business hypotheses can be tested against real data. AI and BI strategies become something you can actually pursue rather than just plan for. The barrier for new employees, in finance, operations, or anywhere else also drops significantly. Instead of requesting access to five different systems and chasing three colleagues for context, they can reference a unified definition of your KPIs and business transactions and start delivering real answers to real problems from day one.
There's a technical upside too. By separating analytical workloads, which often require significant computational resources, from your operational databases, you ensure that reporting and exports never interfere with the systems running your day-to-day operations.
Building your first Data Marts on a warehouse that's ready for your growing data needs sounds like a significant undertaking, in effort and in cost. But over the past decade, developer communities around the world have optimised and open-sourced tools that make this more approachable for SMEs looking to turn years of collected business data into a real asset. The question is not only whether the technology exists, but whether the investment makes sense for your business and what still needs to be assessed properly together with you. This guide is designed to help you understand where you currently stand, what common architecture paths look like, and what the platform side of that journey could roughly cost.
To give you a useful first estimate, we need to start with a simple question: how do you work with data today? If you're unsure about some of the answers, just skip them, we'll work with approximations where we need to and be transparent about what we're less certain of. Migration effort, transformation logic, governance, rollout, and team enablement still need to be assessed with you directly.
How Do You Work With Data Today?
Nobody is starting from zero. You're probably looking at data and reports several times a week, in direct reports, in team meetings, in company-wide presentations. But where that data comes from, and how much you trust it, varies enormously.
Still running primarily on spreadsheets and manual exports. Most others sit somewhere in the middle: a central database exists, but Finance defines revenue differently than Sales, and nobody can agree on which dashboard to trust come Monday morning, or even explain clearly why the numbers don't align when the reason may be perfectly valid from a business perspective.
Pinning down where you currently are is the first step to figuring out what's actually worth building and what you can already leverage to unlock value immediately.
What Systems Run Your Business?
Now that we have a sense of how you work with data, we need to understand what systems you're actually running. That tells us how much needs to be integrated and where the complexity lies.
Keep in mind: you don't need to list everything. Focus on the sources that would make the most sense to connect in a first step. That's enough to get an initial cost estimate. Additional sources can remain manageable, but their real effort depends heavily on the quality of the source systems, the business rules involved, and the way data is expected to move. If you leave this blank, we'll approximate based on what we do know and widen the uncertainty range in the final estimate accordingly.
This is often where consulting projects start to feel expensive, because the prospect of integrating many different source systems can make scoping conversations uncomfortable fast. Modern tooling does improve the starting point significantly, especially on the connector side. But connectors alone are rarely the whole story. In practice, source semantics, change tracking, validation, and business logic are often where the real work begins. That is exactly where we usually come in and support.
Who Needs Access, and Where Does Everything Live?
We're nearly there. This last set of questions gets more concrete. The answers here are what allow us to narrow down what your data warehouse would actually cost to set up and run.
Three Common Architecture Paths For Your Situation
The estimates above cover platform costs: compute, storage, ingestion, and operational overhead. What they deliberately leave out is the transformation work, meaning the design and implementation of Data Marts shaped around your specific business logic. That is intentional, because that work varies significantly from company to company and cannot be approximated from a few inputs alone.
That is also where we come in.
Our goal is to help European SMEs turn years of collected data into real data assets that AI and BI strategies can actually be built and executed on. We believe that everything starts from the data and that clean, maintainable data infrastructure is not a trend or a luxury. It is a differentiator.
If this resonated with you, if you would like a more detailed assessment based on your actual data landscape, or if you feel our approximation missed the mark, we would love to hear from you. Reach out and let us talk data.
Detailed Cost Breakdown
A final note on the estimates: the approximations shown here can vary from reality depending on how complete your inputs were. In the "How we calculate this" section you will find our full methodology. We refine our approach continuously based on the feedback we receive, so if something does not add up or you would like to understand the model in more detail, please do not hesitate to get in touch.
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Download a PDF with your data landscape, the three architecture options, and the cost breakdown, plus a few things we would want to assess with you in a follow-up call.
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