Contact now
Contact now
Data streams flow across a meadow and become a tree, which turns into a cloud.

Data Architecture Consulting

Make data usable before AI and cloud projects fail because of it.

BMW Group Logo
DeutscheBahn_logo-2
Creditreform Logo
DERTOUR
jochen-schweizer
Dräger Logo
kuka
BMW Group Logo
DeutscheBahn_logo-2
Creditreform Logo
DERTOUR
jochen-schweizer
Dräger Logo
kuka
ProSieben_Logo_2015-2
Mercedes
Volkswagen Logo
DEKRA
stihl
Sonax_logo
Weidmüller logo
Das Logo der Bundesagentur für Arbeit
ProSieben_Logo_2015-2
Mercedes
Volkswagen Logo
DEKRA
stihl
Sonax_logo
Weidmüller logo
Das Logo der Bundesagentur für Arbeit
HomeServicesData & AIData Architecture Consulting

A Robust Data Architecture as the Foundation of Your Digital Transformation

How usable is your data today? Is it stored in silos, duplicated, and inconsistent—or does it flow reliably to where decisions are made? In most companies, AI and cloud initiatives fail not because of the idea itself, but because of the foundation: a missing or ad-hoc data architecture. This is exactly where our consulting services come in.

MaibornWolff helps you gain a deep understanding of your data landscape, define a clear vision, and build a scalable, governable data architecture —as the foundation for data-driven decisions, confident cloud adoption, and AI that truly delivers in production.

Schedule a free consultation

Why Data Architecture Determines Success or Stagnation

Many companies have been investing in digital transformation for years—yet the returns have fallen short of expectations. According to the MaibornWolff Technology Efficiency Study 2026 ( n = 305 IT decision-makers), 61% of respondents report that their own IT systems tend to slow down productivity rather than accelerate it. The reason is rarely a single tool, but rather a lack of a coherent underlying architecture.

This finding is particularly evident in the data layer: The Lünendonk study “Data & AI Services in Germany 2025/2026” (n = 170) articulates the principle “Enabling Data Layer before Use Case.” Only 62% of the surveyed companies have a unified data management system, and just 77% have a company-wide data strategy—and 35.1% of project budgets are already being allocated to data infrastructure and integration. Without this foundation, AI initiatives get stuck in the conceptual phase.

Or, as we put it at MaibornWolff: AI acts as an amplifier. It makes good processes faster—and turns poor structures into chaos even faster. A clean data architecture is therefore not a technical detail, but rather the prerequisite for investments in the cloud and AI to pay off at all.

MaibornWolff: Your Partner for a Scalable Data Architecture

For us, data architecture is not an end in itself, but a means to business success. We take a goal-oriented approach, consistently strip away complexity to focus on what’s essential, and empower your teams to further develop the architecture on their own. Four principles guide our consulting work:

From a business perspective

We don't start with the tool, but with value creation: What decisions, products, and use cases should the architecture enable? That way, you invest where the greatest impact is achieved.

Enabling the Data Layer

We establish the data foundation before the first use case is launched—consistent, integrated, and usable. This prevents AI and analytics projects from failing due to silos and poor data quality.

Data Governance by Design

We embed responsibilities, standards, and data quality into the architecture from the very beginning—rather than adding them later. This builds trust in the data and ensures compliance.

Scalable & Future-Proof

We're laying a foundation that can scale to accommodate new data sources, domains, and requirements—for example, using data mesh principles. The architecture can be expanded step by step.

What does a good data architecture consulting service offer?

A data architecture describes how data is collected, stored, integrated, made available, and used within your organization —including the underlying structures, standards, and responsibilities. It serves as the link between your business strategy and its concrete technical implementation in platforms, pipelines, and applications.

Good data architecture consulting answers three key questions:

  • Where do you stand today (assessment of the data landscape)?

  • Where do you want to go (target architecture, derived from business objectives)?

  • And how do you get there (a prioritized roadmap with quick wins and strategic investments)?

In doing so, we also clarify the architectural choice between centralized approaches (data warehouse, data lake) and decentralized approaches such as data mesh.

A term often used synonymously is “data platform”: While the architecture describes the target vision and principles, the platform represents the concrete technical implementation. Our consulting approach deliberately starts one level earlier—at the foundation, which determines the success of the subsequent implementation.

These companies are already relying on our expertise

Why Data Architecture Consulting Is Worth It for You

The clearer your data architecture, the faster and more secure your digital projects will be. A well-thought-out architecture offers four specific advantages:

AI- and cloud-enabled

Your data is structured and made available in a way that allows AI models and cloud services to rely on it—a prerequisite for moving use cases from the prototype stage to production.

Fewer Silos & Duplication of Effort

Unified structures and a clear data flow replace isolated silos. This reduces maintenance efforts, duplication of work, and friction between business units and IT.

Sovereignty & Compliance

With clear data governance and well-thought-out architectural decisions, you maintain control over your data and meet regulatory requirements—rather than having to implement them retroactively.

Predictable Investments

A prioritized roadmap makes it clear which steps yield which benefits. This allows you to manage budgets effectively and avoid investing in structures that won’t pay off later.

Data Architecture Consulting with MaibornWolff: Our Approach

To transform your data landscape into a robust architecture, we follow a clear, consultant-led process. Depending on your starting point, we get involved at the appropriate stage—the result is always a concrete, prioritized roadmap.

Abstract Lines

1. Data Inventory and Assessment of the Current Situation

To begin with, we map out your data landscape: What data sources, systems, and platforms exist? Where are the silos, disconnects, and quality issues? How mature are your data management and governance practices today? This assessment provides the factual foundation for all subsequent steps—especially in established environments.

2. Refine the data strategy and vision

3. Design the Target Architecture (Centralized vs. Data Mesh)

4. Embed Data Governance and Data Quality

5. Roadmap, Prioritization, and Enablement

Our References and Projects

The best way to see what a robust data architecture actually achieves is to look at completed projects. Take a look at selected case studies from the data and AI field—ranging from data platforms to AI applications in production.

See all references
  • Close-up of colorful puzzle pieces floating in the air, each piece engraved with a different insurance symbol.
    WIRKSTATT: Business Intelligence for the sales force
    To the WIRKSTATT reference
    CloudData/Data PlatformsApps

    Aggregation of internal customer data & external data in a single web application

    To the WIRKSTATT reference

    Data bundling & analysis with Amazon Bedrock

    To the WIRKSTATT reference

    Intuitive user interface for sales, 88% reduced preparation time before customer visits

    To the WIRKSTATT reference
  • A fleet of self-driving trucks from MAN on a spacious test site.
    MAN - ATLAS L4. Control Center for the autonomous truck
    To the MAN reference
    CloudData/Data PlatformsApps

    Control center for the technical monitoring of driverless trucks

    To the MAN reference

    UX design, product strategy, data structure, vehicle data visualization

    To the MAN reference

    Monitoring, remote support, mission management, reports for commercial autonomous transport solutions

    To the MAN reference
  • Header_NOW
    NOW: National Organization for Change in Mobility: development of a data warehouse system
    To the NOW reference
    CloudData/Data PlatformsIT Consulting & Strategy

    Data foundation for nationwide charging infrastructure in Germany

    To the NOW reference

    Cloud data warehouse for integration & analysis of many diverse data sources (AWS)

    To the NOW reference

    Solid architecture, single point of truth ensures data-based evaluation of charging station demand

    To the NOW reference
  • Two people in white protective suits stand in front of a pipeline through which green glowing data streams are pumped
    NETZSCH: Development of an IoT platform
    To the NETZSCH reference
    CloudData/Data PlatformsIoT

    Unified IoT platform for 3 business units, harmonization of existing IoT solutions

    To the NETZSCH reference

    IoT device connectivity, visualization software for data analysis, cloud infrastructure, operations

    To the NETZSCH reference

    Quick testing in the cloud infrastructure, fast integration of use cases such as predictive maintenance, process optimizations, etc.

    To the NETZSCH reference
  • A man in a TÜV Nord shirt operates a diagnostic device in front of a vehicle.
    TÜV NORD: IT system for damage assessments
    To the TÜV Nord reference
    Data/Data PlatformsWeb & Portal PlatformsBanking/Insurance/FSI

    Holistic, flexible IT system to support expert assessors

    To the TÜV Nord reference

    Digitalization of the inspection & damage process from order creation to invoicing

    To the TÜV Nord reference

    More efficient creation & billing of damage assessments & vehicle valuations, at least 2 days time savings

    To the TÜV Nord reference
  • Two orthopaedic surgeons view a transparent 3D hologram of the skeleton and musculature on an elegant tablet interface, surrounded by floating UI panels.
    Health.exe: AI-supported platform creates training plans for patients
    To the Health.exe reference
    CloudData/Data PlatformsApps

    AI-supported service for orthopedic & sports medicine practices

    To the Health.exe reference

    Cloud-based web application for doctors for the automated, evidence-based creation of individually tailored patient training plans

    To the Health.exe reference

    New revenue source without fixed costs, higher patient retention, AI-supported & guideline-based

    To the Health.exe reference
  • A robotic arm places precise digital data in a futuristic, dark room.
    Research: AI-supported robotics for employees with physical limitations
    See robotics reference
    Embedded Systems & RoboticsIndustry 4.0Manufacturing

    Customized assistance robots for people with physical disabilities in production

    See robotics reference

    Integration of AI for automated adaptation of robots to people's capabilities

    See robotics reference

    Effective empowerment of people with physical disabilities

    See robotics reference
  • A technician in a green Siemens jacket sits in front of a computer on a factory floor with industrial equipment in the background.
    Siemens: AI demand prediction platform for industrial production planning
    See Siemens reference
    CloudData/Data PlatformsIndustry 4.0

    Machine learning for time series forecasting

    See Siemens reference

    AutoML for automated adaptation of models to different data

    See Siemens reference

    Unified, scalable solution, optimized inventory costs, efficiency gains

    See Siemens reference
  • Two women are standing in a workshop. One woman is holding a tablet in her hands.
    TÜV NORD GPT: Development of AI assistance
    See TÜV NORD reference
    AppsWeb & Portal PlatformsPublic/Administration

    Secure operation of AI in the European MS Azure cloud environment

    See TÜV NORD reference

    Frontend & backend via MS Azure App, "Chat with your PDF" for TÜV employees

    See TÜV NORD reference

    Quick implementation of new technologies (AI), strengthening knowledge management

    See TÜV NORD reference
  • VW drives through tunnel at night
    VW: Digitization of key production figures with the iProcess app
    See VW reference
    Data/Data PlatformsAppsIndustry 4.0

    Replacement of analog, error-prone activities with a digital app solution

    See VW reference

    Digital design, cloud-native technologies, UX concept, UI design, front- & backend

    See VW reference

    More transparency in production processes, higher production OEE, across plants

    See VW reference
  • Digikoo_Apple_vision_Pro_Header
    digikoo GmbH: Apple Vision Pro for city planners
    See digikoo reference
    Digital Design/UX DesignData/Data PlatformsApps

    Immersive 3D visualization of complex energy data on the Apple Vision Pro

    See digikoo reference

    Augmented reality, spatial computing, 3D map with detailed data & KPIs

    See digikoo reference

    Foundation for intuitive understanding of energy scenarios & well-informed decisions

    See digikoo reference
  • Large rollers on conveyor belt in factory.
    Planning systems: Optimizing the capacity utilization of pressing plants
    See reference
    Data/Data PlatformsIndustry 4.0Manufacturing

    Centralized planning of component manufacturing for cost- & resource-optimized production capacity worldwide

    See reference

    Conversion from local processing with fat clients to a client-server application, migration to the cloud

    See reference

    Data-based planning & calculation of different manufacturing scenarios & site-specific production costs

    See reference
  • Header_Global-Requirements-Planning-System-for-Workforce-2-16-9
    Global workforce planning system
    See reference
    CloudData/Data PlatformsPublic/Administration

    Centralized web-based IT system to replace individual isolated solutions

    See reference

    Event sourcing for planning & analytics, domain-driven design, cloud migration

    See reference

    Easy updates, expansion, maintenance, optimized security

    See reference
  • Man with tablet in front of KUKA industrial robots
    KUKA: UI/UX design for an app for load data analysis for industrial robots
    See KUKA reference
    Digital Design/UX DesignData/Data PlatformsApps

    Web app to replace legacy systems for easier interaction between users & system

    See KUKA reference

    Conversion from local processing with fat clients to a client-server application & migration to the cloud

    See KUKA reference

    Data-based planning & calculation of different manufacturing scenarios & site-specific production costs

    See KUKA reference
  • Server room with green planting, demonstrating data platform for the Azure Cloud.
    digikoo: A data platform for the Azure Cloud
    See digikoo reference
    CloudData/Data PlatformsIT Consulting & Strategy

    Data-based information for planning & implementing the climate transition for the public sector & energy providers

    See digikoo reference

    Scalable foundation data platform on MS Azure for migrating & automating differently formatted geo-data into a structured data schema

    See digikoo reference

    Quality-checked data, provision in the form of the target data model, robust, scalable database & infrastructure

    See digikoo reference
  • A slender robotic arm in a production hall, picking up coins and placing them in a piggy bank-shaped cloud, while console screens in the background display cost-waste diagrams.
    Supply chain management: Reducing cloud operating costs by 50 percent with FinOps
    To the FinOps reference
    CloudData/Data PlatformsIT Consulting & Strategy

    Reduction of costs caused by over-dimensioning & manual processes, establishment of transparency

    To the FinOps reference

    Targeted process modernization, automation & rightsizing

    To the FinOps reference

    Annual cloud operating cost reduction: 400,000 EUR, scalability, reliability

    To the FinOps reference
The hands of different people coming together symbolize diversity, inclusion, and equity.

Your Next Step Toward a Robust Data Architecture

Silos, duplication of effort, and stalled AI projects cost time and money. So does an unclear data architecture. MaibornWolff combines deep engineering expertise with real-world experience in complex data landscapes —vendor-neutral, without proprietary products, and without a hidden agenda. We recommend the architecture that fits your goals, not the one that benefits us.

With over 800 large-scale systems implemented and more than 10,000 person-years of experience in software engineering, we know how to build and operate sustainable data architectures in practice. Talk to our experts and learn how a clear data architecture makes your AI and cloud projects more predictable.

Contact us now

FAQ: Frequently Asked Questions About Data Architecture Consulting

  • What is data architecture?

    Data architecture describes how data is collected, stored, integrated, made available, and used within an organization—including the underlying structures, standards, and responsibilities. It serves as the link between business strategy and technical implementation in platforms and applications.

  • How does data architecture differ from a data platform?

    The data architecture describes the target state and the principles—that is, which structures, data flows, and governance rules should apply. The data platform is the concrete technical implementation of this. Good consulting starts one level before the platform, at the foundation.

  • What is Data Mesh, and when is it useful?

    Data Mesh is a decentralized architectural approach in which individual business domains make their data available as standalone, quality-assured data products—rather than consolidating everything into a central data warehouse. This is particularly useful for large, heterogeneous organizations with many domains. Whether centralized, decentralized, or hybrid: The right choice depends on your organization and your goals—and that’s exactly what we’ll clarify during our consultation.

  • Why Do AI Projects Often Fail Because of Data Architecture?

    Because the foundation is missing. According to the Lünendonk study “Data & AI Services 2025/2026,” only 62% of companies have a unified data management system. Without integrated, quality-assured, and governed data, AI initiatives get stuck in the conceptual phase—in accordance with the principle “Enabling Data Layer before Use Case.”

  • What is the process for a data architecture consultation at MaibornWolff?

    In five steps: data inventory, refining the data strategy and vision, designing the target architecture (centralized or data mesh), establishing data governance, and creating a prioritized roadmap that includes enabling your teams. Depending on your current situation, we’ll start at the appropriate point.

Find what suits you best
Refine your search
clear all filters