Data Management & Data Products
that your AI can trust.

We help Swiss companies and public authorities organize their data so that AI can work with it reliably: clear responsibilities, clean definitions, resilient data products. Your data stays within your own infrastructure. And we start with a concrete use case, not a concept paper.

Data management determines whether AI creates real impact in a company or stays stuck in the prototype stage. The bottleneck isn't the model, but whether the data is findable, understood, maintained, and owned. We help Swiss companies turn scattered data assets into resilient data products – with clear roles, pragmatic architecture, and an approach oriented toward value rather than tools.

What is Data Management?

Data management encompasses all the tasks a company uses to capture, integrate, quality-assure, describe, protect, and provide its data. This includes data architecture, master data management, data integration, data quality, metadata, and data governance. The goal is for every person and every system in the company to receive exactly the data they need – in reliable quality, at the right time, and with clearly defined authorization.

In practice, data management rarely fails because of the technology. It fails because no one is clearly responsible, because the same metric has three different values in three systems, or because knowledge about data assets depends on individual people.

From Data to Data Products

Ein Datenprodukt ist ein klar abgegrenzter Datenbestand, der wie ein Produkt geführt wird: mit benanntem Owner, dokumentierter Bedeutung, zugesicherter Qualität, stabiler Schnittstelle und definiertem Lebenszyklus. Es ist damit mehr als eine Tabelle oder ein Report – es ist eine verlässliche Zusage an alle, die damit arbeiten.

The difference to classic project thinking:

• Zweckgebunden statt universell – ein Datenprodukt löst eine konkrete fachliche Fragestellung, statt alle denkbaren Auswertungen abdecken zu wollen.
• Verantwortet statt verwaist – es gibt eine Person, die über Inhalt, Qualität und Freigabe entscheidet.
• Documented rather than implicit – definitions, origin, and currency are traceable.
• Wiederverwendbar statt einmalig – andere Bereiche und Systeme können darauf aufbauen, auch KI-Anwendungen.

Data products are the point at which data management turns from a cost center into a value contribution: they make the benefit visible and fundable.

Why AI Doesn't Work Without Data Management

AI applications amplify the quality of the underlying data – for better or worse. A chatbot built on unstructured and outdated documents delivers confidently phrased wrong answers. Semantic search only finds what is cleanly described. And AI-driven process automation is only as reliable as the master data it accesses.

Concretely, AI requires the following from data management:

That's why we don't start AI initiatives by selecting a model, but by asking which data the application actually needs to carry – and whether it can do so today.

Organisation und Verantwortlichkeiten im Datenmanagement

Datenmanagement ist zu einem grossen Teil eine Organisationsfrage. Technik lässt sich beschaffen, Verantwortung nicht. Erfolgreiche Datenorganisationen klären deshalb früh, wer welche Entscheidungen trifft – und verankern das dort, wo das fachliche Wissen liegt, nicht ausschliesslich in der IT.

Governance that works in everyday life

We advise against extensive governance frameworks that no one reads after rollout. What works is a lean framework that clarifies three things: What data domains exist, and who is responsible for them? How are definitions decided when departments disagree? And how is data quality measured? Everything else is better developed alongside the first concrete data products than on the drawing board.

Data management consulting in Basel, Bern, and throughout Switzerland

Als Schweizer Beratungsunternehmen mit Standorten in Basel und Bern arbeiten wir seit 2001 an der Schnittstelle von Fachbereich, Daten und IT. Unsere Herkunft liegt in der Integrations- und Unternehmensarchitektur – wir kennen gewachsene Systemlandschaften und die Realität hinter den Präsentationsfolien.

For clients in northwestern Switzerland and the Bern area, we're on-site at short notice; across Switzerland we work in a hybrid model. Where requirements call for it, your data stays within your own infrastructure.

Häufige Fragen zum Datenmanagement

What is the difference between data management and data governance?

Data management encompasses the operational work with data – integration, quality assurance, provisioning. Data governance is the overarching framework: it defines who decides, which rules apply, and how responsibilities are distributed. Governance without operational data management remains paper; data management without governance leads to siloed solutions.

Where should we start?

With a concrete use case that resolves a noticeable bottleneck – not with a company-wide data catalog. A first productive data product creates a learning curve, credibility, and budget for the next steps.

Do we need a data platform before we can use AI?

No. For many AI use cases, it's enough to specifically prepare and take ownership of the actually needed data assets. A complete platform is rarely the prerequisite – more often it's the result of several successful data products.

How many roles do we need to fill?

In smaller organizations, data owner and data steward are often filled part-time by the same people. What matters isn't the number of positions, but that responsibility is clearly assigned by name and accepted in everyday practice.