Data & Analytics
Sovereignty is an architecture decision. Not a slide.
We design data architecture that keeps you negotiation-ready. With open standards, multi-cloud capability and concrete exit paths from every platform.
Book a free introductory call30 minutes. We get to know each other. You decide if it fits.
Current state
You might recognise this.
Reports contradict each other. Three sources, three truths.
In the steering meeting, you argue about numbers instead of deciding. Sales has one view, controlling another, BI a third. No one knows who's right, and that costs every meeting time that's missing for decisions.
Data platform on the Microsoft stack. Convenient, but not sovereign.
Azure, Fabric, Synapse. Pragmatic, because licences, identity and skills are already there. Sovereign only if open standards are applied consistently. Whoever builds on proprietary Fabric features builds the next lock-in. The problem only shows when you want to switch.
Sovereignty in the strategy paper. Not in the architecture.
After a short audit, the same pattern often shows: management presents the "sovereignty" slide from the strategy paper. A look under the hood shows something else. Data in proprietary format. No documented exit paths. No data contracts. Sovereign is none of it. On the slide, yes.
The BARC study "Data Sovereignty 2026" confirms what we see in practice: sovereignty sits on the strategic agenda for the majority. 40 percent invest zero euros in it. In North America, awareness and budgets are noticeably more consequent. This asymmetry gets more expensive the longer it lasts.
Our approach
Three steps. Pragmatic. As equals.
Audit first, then architecture, then accompany. What we recommend, we recommend because it holds, not because it generates billable days.
Audit and understand
We get to know your data landscape. Strategy, platform, pipelines, reporting, governance, stakeholders, team maturity. We open the lakehouse browser and see what's really there. We talk to business, IT and leadership, because those three rarely mean the same thing.
Day 1 to Week 4
Strategy and architecture
We design the data strategy and platform architecture that fit your organisation. With open standards, multi-cloud capability, defined exit paths and governance that doesn't become a brake. Lakehouse, data mesh, classic warehouse or hybrid setup, whatever fits maturity and size.
Week 4 to 10
Accompany and hand over
We support implementation, mentor data teams, validate critical architecture decisions. When your team can carry it forward, we step back.
From Week 10, as long as you need
A testable question: If you had to switch cloud providers tomorrow, because prices rise or geopolitics shift, in what format does your data sit? And how long would the switch take?
Whoever has no answer has an architecture problem. Spend 30 minutes with someone who knows this from practice.
Areas of focus
Where we focus.
Data strategy and roadmap
What creates value now, what becomes relevant later, what is deliberately left out. We develop a data strategy that thinks architecture, platform, governance and organisation together, instead of stringing single initiatives.
Enterprise data platform: lakehouse, data mesh, data products
The platform architecture on which data becomes reliable, scalable and negotiation-ready. We design lakehouse architectures, data mesh structures and data product models, aligned to your organisation and maturity. We avoid dogmatism: sometimes the classic warehouse is the better answer.
Vendor sovereignty and multi-cloud
Multi-cloud is not hyperscaler marketing. It's a negotiation position. We design architectures with open table formats like Apache Parquet and Delta Lake, with clear data flow boundaries and documented exit paths. So you stay able to act when prices rise or providers change.
Data pipelines, data contracts and data engineering
Data doesn't flow on its own. We build pipelines that run reliably, define data contracts between producers and consumers, and establish the engineering discipline that prevents pipelines from becoming black boxes.
Data governance, data quality and master data
Governance isn't a compliance talk, it's practice. We define accountabilities, design data quality mechanisms, build master data management and secure data protection so it doesn't become a brake. Including architectures that structurally make misuse harder.
BI, data literacy and advanced analytics
Self-service analytics only works if business users can understand the data. We design BI platforms so reports are non-contradictory, build data literacy, and prioritise advanced analytics use cases by value contribution, not by trend.
Why DRICH.CONSULTING
Strategy and engineering in one person. Sovereignty as an architecture discipline.
Data topics often fail because strategy and engineering come from different people who don't speak the same language. I've worked as a software developer, architect, IT project lead and Chief Data Officer. From that practice I know both sides and the typical stumbling blocks between them.
Strategy and engineering in one person
I don't decide on lakehouse architectures without knowing what that means in code. And I don't talk to leadership about data strategy without knowing the engineering consequences. That connection prevents beautiful strategies from failing on engineering realities, or good engineering solutions from failing on strategic gaps.
Sovereignty through architecture, not through promises
Data is the long-term asset. Whoever lets it get locked into proprietary formats or closed platforms pays for it later. We design data architectures with open formats like Apache Parquet and Delta Lake, with clear data flow boundaries and defined exit paths. Multi-cloud capability is part of this discipline, because it preserves negotiation room.
20+ years of data practice from enterprise and mid-sized companies
Data strategies implemented at enterprise level, accountability carried as Chief Data Officer, pragmatically solved in mid-sized companies. That range changes the consulting: I know the cultural and technical differences of both worlds and the solutions that hold up in both.
100 percent independent. What that means concretely.
No platform commissions, no vendor partnerships with commercial strings attached, no implementation follow-on business. Concretely: We don't recommend a lakehouse we earn money on. We don't build architecture that only runs with one provider. We don't sell licences for tools we wouldn't buy ourselves. Particularly important with data, because wrong platform decisions persist for years.
20+
years of IT
and data
CDO
experience from enterprise
and mid-sized
★
multi-cloud practice,
open formats
100%
independent,
vendor-neutral
Frequently asked
What decision-makers ask us.
Classic BI consultants build reports and dashboards. We start before that: with architecture, sovereignty and governance. If the foundation doesn't hold, even the best dashboard won't be contradiction-free. Plus we are vendor-neutral and come from data practice, not from tool implementation.
The question isn't whether you have a platform, but whether it's sovereign. Does your data sit in open formats? Are exit paths documented? Can you switch providers in a foreseeable timeframe if prices rise? If yes, you don't need us. If no, it's worth looking.
Data protection and compliance are architecture questions, not slide questions. We design access, data flow boundaries, anonymisation and pseudonymisation so they hold regulatorily and structurally make misuse harder. Specific legal advice comes from a lawyer or DPO; the architecture behind it comes from us.
We are vendor-neutral. We work with AWS and Azure and often design multi-cloud setups so you stay able to negotiate at vendor reviews. We recommend the provider that fits your situation, not the one with whom we hold a partnership. We don't hold a partnership.
Data mesh organises data as products, owned by domain teams, instead of centrally by a single data office. Whether data mesh fits your organisation depends on size, maturity and team distribution. In some cases, a well-built data warehouse is the better answer. Dogmatism rarely helps here.
No, and we don't pretend otherwise. 30 minutes are enough to roughly understand your situation and to tell you honestly whether we're the right partner. If we're not, we say so. If we are, we suggest a sensible next step, which isn't automatically a paid engagement.
Introductory call
Free introductory call
Tell us your situation. We listen, ask questions and tell you honestly whether we're the right partner. If we aren't, that's fine too.
- 30 minutes. No pitch, no presentation.
- You describe the problem. We listen first.
- You get an initial read, with no sales intent.
- No commitment afterwards.
Slots are offered on weekdays between 9am and 6pm (CET).
Sovereignty on the slide, lock-in in the architecture?
Book a free introductory call