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Digital landscape representation, symbolized by Data Mesh.

Data Mesh: Improved data architecture

From unstructured data lake to structured data mesh
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DERTOUR
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BMW Group Logo
DeutscheBahn_logo-2
Creditreform Logo
DERTOUR
jochen-schweizer
Dräger Logo
kuka
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Mercedes
Volkswagen Logo
DEKRA
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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

What is Data Mesh and why does it give you control over your data?

Improved efficiency and scalability - Data Mesh is a modern approach to organizing data. It is based on a decentralized data architecture in which responsibility is distributed to individual domains or teams. Clear standards and governance mechanisms ensure the seamless interaction of data. Data Mesh also facilitates the integration of new technologies and promotes flexibility and innovation.

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We offer solutions

The data mesh structure contains many components. Together we will find out whether and how this type of modeling fits your company and your challenges. We tailor our service to your current level of data maturity and your existing or planned architecture.

How we achieve our goal

From 4-hour workshops and data thinking in 2 days to 5-day pilot projects to finding and setting up an initial data domain - we adapt to your needs.

This is our key

We underpin data mesh with three disciplines: cloud architectures, enterprise architecture management and data governance and also assign your project to a suitable data maturity level.

A small selection of our customers

The key principles: How Data Mesh works in your company

An efficient data architecture plays a decisive role in the success of your company. Traditional approaches often reach their limits, which is why more and more organizations are turning to innovative solutions such as Data Mesh to improve their data structure.

Decentralization and autonomy

Decentralization distributes responsibility for data to teams that act autonomously. This promotes your agility and enables you to adapt quickly to changing business requirements.

Clear standards and governance

Data Mesh sets clear standards and governance mechanisms to ensure that data can interact seamlessly with each other. This ensures consistency and quality across your company's entire data landscape.

Why you should rely on Data Mesh - the weaknesses of Data Lake and Data Fabric

Data is key to a company's success, so choosing the right data architecture is an important strategic decision. Data Mesh overcomes the weaknesses of traditional models such as Data Lake and Data Fabric. In particular, Data Mesh prevents an unstructured data lake by providing a structured and organized data infrastructure. Take the step towards an agile, scalable and innovative data architecture to successfully master the challenges of the digital era.

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Agile and scalable data infrastructure

Data Mesh enables an agile and scalable data infrastructure. In contrast to rigid structures such as data lakes, which lose efficiency as data volumes increase, data mesh offers a decentralized architecture that can respond more easily to changing requirements and growth

Autonomy of domains

Better data quality and consistency

Data protection and end user orientation

Innovative spirit

The Maibornwolff Data Mesh Blueprint offers you these added values

Scalability and flexibility

Changes can be responded to iteratively and incrementally by standardizing tools across departments

Autonomy and decentralization

Each team has control and responsibility over its own data

Higher data quality

Clear separation of tasks through decentralization of data management and focus on data products

Better customer orientation

Target group-oriented consideration of end user needs through federal governance

MaibornWolff Data Mesh Blueprint

We rely on a data strategy that is characterized by scalable and flexible structures, autonomous and domain-specific decentralization as well as improved customer orientation and higher data quality.

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Wire model of a head in bright colors, symbolizing Data Mesh.
The architecture assessment with MaibornWolff was crucial in identifying the necessary steps to establish effective data governance and to develop us into a data-driven company with a data mesh approach in the long term.
Thorsten Mohr, Senior Data Analytics Specialist / Digital Transformation Unit, WEPA

Our references & projects

A reference is worth more than a thousand words. Luckily, we have dozens of them. Click through a selection of our most exciting projects and see for yourself!

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

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

    To the STARTRAIFF reference

    Data bundling & analysis with Amazon Bedrock

    To the STARTRAIFF reference

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

    To the STARTRAIFF 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 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
  • 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

Our approach: Data thinking brings data mesh to your company

Our approach takes a holistic view of data mesh by considering technical, functional and organizational aspects. We combine data mesh with well thought-out data governance and digital data asset management, supplemented by clear data responsibilities (data ownership).

Our approach is divided into two phases:

  • Data Thinking:
    Stocktaking & detailed analysis - Only those who understand the technicalities can make well-founded technical decisions. That's why we start by looking at your business area and your data products. Based on this, we help you to establish the right data domains and make data products available to others.
  • Migration roadmap:
    Implementation plan & recommendations - as a result, you receive a well-founded catalog of measures, with recommendations and concrete steps for all parties involved. We prioritize this migration roadmap together with you. You can also benefit from our comprehensive, cross-industry expertise in the implementation of transformation and migration projects.
Person speaking in front of monitors with MaibornWolff logo, symbolizing presentation on Data Mesh.

Data Mesh with MaibornWolff

Our data mesh experts adapt to your individual needs and offer a broad tech spectrum - from consulting to development and implementation.

Contact us now

Film off: Data Thinking Workshop with Bayernwerk

When transforming an unstructured data lake into a structured data mesh, the big picture must be considered from the outset. The video shows how this inventory analysis can be carried out with the help of the Data Thinking Workshop.

FAQ: Frequently asked questions about Data Mesh

  • How do I successfully implement a data mesh in my company?

    Implementing a successful data mesh in your company requires a structured approach. This requires a comprehensive understanding of the business requirements and the existing data landscape. Establishing effective metadata management and providing the necessary technological infrastructure are crucial steps. Ensure that teams develop the necessary skills through training and resources and rely on agile methods for continuous adaptation.

  • What role does a data lake play in the context of data mesh?

    Within the data mesh architecture, the data lake acts as a central data reservoir that is used by the various domains to create and manage their data products. The integration of a data lake in Data Mesh enables efficient central storage of raw data. Each domain has access to the data lake in order to use relevant data and create its own data products. The clear definition of interfaces and standards plays a crucial role in ensuring that data can be exchanged seamlessly between the domains and the data lake.

  • What best practices exist for the integration of Data Mesh and Data Fabric?

    Seamless integration of Data Mesh and Data Fabric requires a clear definition of interfaces and standards. These ensure smooth interaction between the decentralized domains of Data Mesh and the centralized structure of Data Fabric. A federated governance structure ensures uniform guidelines and security measures across all data areas. Effective metadata management and targeted training enable teams to leverage the benefits of both approaches. The promotion of collaboration and agile adaptability ensure that data integration and management remain flexible and efficient.

  • What distinguishes Data Mesh from Data Lake and Data Fabric?

    Data mesh, data lake and data fabric represent different approaches to data management. Data Mesh emphasizes the decentralization of data management. To this end, it assigns responsibility and quality assurance for their own data products to teams in different business areas. This is done in a federated architecture with a focus on data domains.

    A data lake, on the other hand, is a central, comprehensive repository for raw data in various formats. It allows large amounts of data to be stored in their original form, which enables more flexible analyses. Data lakes provide easy access to a variety of data for different use cases.

    Data Fabric focuses on creating a unified and consistent data view across distributed systems. It facilitates the integration of data and offers functions for orchestrating data processes. Metadata management plays a central role in ensuring transparency and understanding of data sources, flows and usage.

    Data Mesh and Data Lake and Data Fabric are not mutually exclusive. Data Mesh uses the Data Lake as a structured file repository. In this context, Data Fabric can be seen as a kind of "connecting link". It enables the integration and orchestration of data across Data Mesh and Data Lake by providing a unified view of the data. In this way, the advantages of data mesh and data lake can be optimally utilized while ensuring a high level of data consistency and transparency.

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