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Data Mesh

Effectively creating value from data requires the interplay of numerous technologies, platforms, and methods. Data mesh is an analytical data architecture that can address this issue.

Data is capable of adding tremendous value to companies. The challenge, however, lies in being able to draw the right conclusions from it. Its sheer quantity, complexity, and diversity place high demands on the tools, people, and methods deployed to gain the relevant insights.

Data platform architecture as a basis for extracting added value from data

An important role in leveraging data is played by data platforms and architectures. These form a kind of “workbench” which serves as the framework for the processes involved. During our many years of experience, we have supported customers in setting up data warehouses, data lakes and cloud-based platforms. Although even simply integrating and combining different data can provide companies with a great deal of added value, we repeatedly observe challenges that make it difficult for companies to utilise data sustainably and effectively:

  • Tremendous effort is required on the part of the central data teams involved in preparing the various content sources
  • Data routes and pipelines are left “orphaned” due to lack of owners and/or uncommunicated source-side changes
  • A disparity exists between redundant technical knowledge (due to repeated processing) and fragmented technical knowledge (where each person has only an incomplete picture)

Data Mesh: Taking data (products) to the next level

Data mesh addresses the typical challenges of data platforms. First described in 2019, the paradigm addresses the typical difficulties involved in using centralised architectures and processes – particularly in larger companies. Data mesh takes a holistic, end-to-end approach to the development of services and the associated (analytical) data products.

Data Mesh Concept Diagram

inovex has long been a byword for deploying successful projects in application development, IT engineering, and data management and analytics – and particularly for engineering integrated solutions in these areas. For us, data mesh is a combination of promising principles which build seamlessly on our successful track record and expertise:

  • Consistent expansion of the “shift left” approach from DevOps to data products
  • Analytical data as the focus of the life cycle of an application and an integral part of the agile development process (“You build it, you run it, you measure it”)
  • Location of data expertise and responsibility closer to the services and systems generating it
  • Creation of a data ecosystem whose added value increases as participation does

Data mesh is based on established practices and technologies, such as self-service infrastructure and continuous delivery, and unfolds its potential through their further organizational, methodological, and cultural development.

inovex as a holistic partner for integrated data architecture

Venn Diagramm Data Mesh Organisation

We see data mesh as an opportunity and a guide for which there is no one-size-fits-all solution or platform due to the wide variety of different prerequisites. Although data mesh offers a lot of potential, no standard implementation has yet been established. We therefore create custom implementations for each of our customers.

This approach enables existing structures (data warehouses, data lakes, data lakehouses, etc.) to be gradually integrated – without everything being introduced with a “big bang”. As experienced IT project providers, we are able to support our customers holistically in their projects as well as acting as sounding boards.

As a reliable partner who supports companies in their data mesh journey, we provide:

Deep knowledge of existing data platform architectures in a variety of companies and many years of experience in their further development

Understanding of the different corporate and social cultures existing in the various areas involved (application development, IT engineering, analytics)

Outstanding expertise in all the disciplines involved, as well as the comprehensive understanding required for working in cross-functional teams

Good networking within the emerging data mesh community

Practical experience in the implementation of data mesh approaches

Methodological expertise in supporting the change process

Data Mesh in our podcast

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Get in touch!

Dominik Benz

Head of Data Engineering