Data Mesh: Improved data architecture
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.
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
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.
Agile and scalable data infrastructure
Autonomy of domains
The autonomy of domains in Data Mesh offers clear advantages. Each team has control over its own data products, which leads to faster innovation cycles and better adaptability.
Better data quality and consistency
The clear standards and governance principles of Data Mesh improve data quality and consistency, an aspect that is often neglected in traditional approaches such as Data Lake. Decentralization also leads to better data quality, as domain-specific testing is possible. The result is quality-tested data products.
Data protection and end user orientation
Data Mesh enables federated governance that not only complies with data protection and security standards, but also enables targeted consideration of end user needs. In addition, data mesh leads to a domain single source of truth, which further improves data integrity. This is particularly important for developing customer-centric solutions.
Innovative spirit
Data Mesh promotes innovation through its decentralized structure and enables companies to react more flexibly to new technologies. In comparison, traditional approaches can often slow down innovation.
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.
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.
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:
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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.
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.
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.