Scaling Enterprise Data Analytics Through Decentralised Ownership

From Central Data Provisioning to Domain-Driven Ownership

The client is a large enterprise operating across multiple business domains, each with distinct data needs and analytical ambitions. Leadership had set a clear direction: move beyond centralised data provisioning towards decentralised ownership in line with Data Mesh principles according to Zhamak Dehghani.

We want to enable a culture of innovation in a data-driven company by making data valuable and useable for Data & AI use cases.

CTOClient

For years, data had been managed through central teams. While this provided control, it created growing dependencies: every new use case required coordination across multiple teams, platforms and approval chains. Without end-to-end ownership, accountability for data quality, freshness and structure was diluted across teams that often had limited direct relationship with the data they were handling. As demand for data-driven insights accelerated, the central model could no longer keep pace.

The organisation recognised that domain teams, closest to the data, were best positioned to own and deliver it. Shifting to distributed ownership required more than a mandate. It required IT architecture, governance, enablement, organisational change and trust.

Central Dependencies and Governance Gaps

The most visible symptom was speed, or rather the lack of it. Central analytics teams had become delivery bottlenecks. Every request, regardless of size, funnelled through the same overloaded pipelines and the same handful of specialists. New use cases required coordination across multiple dependencies and manual handoffs before any value could be delivered.

Every new use case meant navigating a maze of dependencies and manual handoffs.

Product Owner AnalyticsClient

The lack of end-to-end ownership compounded the problem. With multiple teams involved in each data journey, most with limited context about its origin or intended use, accountability gaps meant issues were discovered late, often only when a downstream use case failed.

Beneath the surface, the challenges ran deeper. Technologically heterogeneous platforms operated with limited interoperability, making lineage difficult to trace. Governance was fragmented: GDPR, the EU Data Act and the AI Act all demanded compliance, but enforcement relied on manual effort and institutional knowledge. The existing platform landscape had grown organically and was not yet fully aligned with modern cloud-native capabilities. A scalable operating model for delivering data products that are easy to understand and consume had yet to be established.

A Scalable Foundation for Data-Driven Excellence

The transformation was guided by a set of principles balancing autonomy with consistency. Domain teams would own end-to-end delivery and quality of their data products. Unified patterns for ingestion, transformation and provisioning had to work across multiple clouds and platforms. Design patterns to document business semantics had to be in place to enable data owners to describe their data efficiently. Governance needed to be federated by design: automated policies, fine-grained permissions and transparent lineage built in from the start, not added afterwards.

Data products had to be discoverable, well-documented, access-controlled and fit for purpose. The organisation aimed for a single logical data environment that minimised replication while ensuring quality-assured consumption. Clear roles, enablement programmes and well-defined processes would give domain teams the confidence to deliver independently.

Our goal wasn’t to control data centrally, but to create guardrails so domains deliver with confidence.

Chief Data ArchitectClient

IT Architecture, Enablement and Implementation Partnership

mmkdigital took on the role of Enterprise Data and AI Architect, working alongside the client’s teams from strategy through to implementation. The target IT architecture spanned AWS and Azure, integrating SAP-based data platforms, Cloudera and adjacent tooling. mmkdigital developed standardised IT reference architectures for ingestion, transformation, provisioning and policy enforcement, giving domain teams repeatable, production-ready patterns.

At the heart of the solution sat a semantic data catalogue serving as the central marketplace for discovery, documentation and access workflows. The semantic layer was designed to be both human- and AI-readable: vendor-agnostic, machine-interpretable descriptions created a single source of truth for data product semantics.

Enablement was a central component: mmkdigital coached over 30 engineering teams, delivered reusable blueprints and co-developed exemplar data products as templates. Joint IT architecture committees across IT, business and governance ensured alignment throughout.

We worked alongside the client’s teams, enabling their capabilities to grow with every sprint.

Alexander RengersSenior IT Architect

Autonomous Teams, Governed Data, Scalable Delivery

The modular Data Mesh approach significantly accelerated the delivery of analytical insights across the organisation enabling wide spread use of available data. Today, over 300 data products serve more than 100 use cases. More than 30 domain teams deliver independently, with transparent ownership from creation through consumption. Governance is unified and automated across the primary data platforms, ensuring compliance without slowing delivery. Every reused data product saves the company significant cost, as existing data pipelines can be leveraged without duplication.

The operable semantic layer positions the organisation for AI-powered analytics and agentic interactions with enterprise data. Time-to-value has improved significantly: standardised data discovery, streamlined approvals and centralised discoverability mean teams spend less time searching and more time delivering insight.

Our human-readable approach to model and describe the semantics of data proved ideal for AI use cases. LLMs understood it directly without rework.

Johannes BitschPrincipal IT Architect

Conclusion

The starting point for this transformation was a clear recognition of the core challenge: central data provisioning was creating bottlenecks, hindering scalability and making it difficult to deliver data-driven insights at pace. By shifting to a domain-driven, decentralised ownership model, the organisation overcame these constraints and established a flexible foundation for analytics and AI at scale. Now mmkdigital further supports in creating more data products and enabling teams to deliver even more valuable insights.

This holistic approach, combining rigorous IT architecture, automated governance and close collaboration across business and IT, ensured that data products could be delivered faster and with greater reliability. Domain ownership, semantic metadata as a first-class deliverable and enablement at scale proved to be the key factors in making the transformation sustainable. mmkdigital’s role as an embedded IT architecture partner demonstrated that lasting change happens through working alongside teams, building capability sprint by sprint and designing systems that grow with the organisation.

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