Data Mesh

The Future of Data Management and Sharing

The cradle of Data Mesh

Building a data-driven business remains one of the top strategic goals of organizations and executives. And while it may seem logical for companies to embrace the idea of being data-driven, it’s far more difficult to execute on that idea.

Changing landscape of data expectations

Data expectations used to be limited to Business Intelligence (BI) with reporting and dashboards. Today’s data however demand:

  • the integration of machine learning and real-time 
  • data-driven optimizations
  • streamlining operations (e.g. real-time logistics)
  • democratization of data 
  • continuous, frictionless access to the latest data

Traditional and monolithic data architectures make it harder to build and maintain large, complex systems, and can limit the ability of organizations to respond quickly to changing business requirements. The central data team becomes a bottleneck when the number of data sources increases and they have to handle more and more diverse data use cases themselves, in the face of continuous change and increased business complexity. This is where the principles of data mesh come into play.

Data Mesh explained

Revolutionize Your Data Strategy

Data mesh answers the challenge of managing data by focusing on building a decentralized, self-serve data ecosystem. It aims to embed data-driven innovation within each department or team so that everyone in the organization is concerned and thinking about how to make data reusable and leverage it for new products and services within or across departments.

By allowing greater flexibility for data stakeholders and lessening the burden on teams, it addresses many common challenges and problems associated with traditional data management and data sharing practices: 

  • Unleash siloed data

    In many organizations, data is stored and managed in isolated silos, making it difficult to access and share data across teams and departments. A data mesh aims to break down these silos and enable data to flow freely and easily across the organization.

  • Stop playing catch-up

    Tired of playing catch-up with your data in a complex and volatile business environment?  Data meshes allow you to manage changes to data gracefully. They align business, tech and data, while reducing the accidental complexity of pipelines.

  • Reduce extensive coordination

    Reduce heavy coordination by delegate responsibilities to autonomous domains and their data product owners. Automate governance policies as embedded code and introduce explicit data contracts between data products to ease coordination.

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How does Data Mesh work?

Data mesh is an approach towards data management within organizations. The idea is to increase the accessibility, availability and usability of data to business users and reduce the interdependence between data teams. It is based on the principles of domain-oriented ownership, data as a product, federated computational governance, and a self-serve data platform. Data is treated as a product to be reused and this data product is a modular first-class citizen with its own independent change lifecycle and management structures. 

  • Team autonomy with minimal dependencies
  • Transparency for cross-functional use between teams
  • Improved agility and flexibility in data management
  • Enhanced data governance
  • Breaking up data silos by data sharing & reusability
  • Seamless integration with emerging technologies

Traditional data approach vs data mesh

Decentralized approach

Imagine a company with different teams for different business domains such as HR, sales, finance, logistics  and marketing. A data mesh architecture would allow each team to have its own autonomy over the quality and the management of the data specific to that team. They can access the data they need through API’s and the data is published in a shared data catalog that can both be easily accessed and updated by all teams. This creates a shared understanding of data and eliminates data silos, making it easier for teams to collaborate, share data and make faster data-driven decisions. The ownership is decentralized with the domain teams, and they are empowered by a shared self-serve data platform.

Multi-disciplinary approach

Data Mesh is considered a multi-disciplinary approach because it involves several disciplines to implement, some examples:

Customer Success Stories

Data Mesh Blogposts

Data Mesh News

Want to know more about Data Mesh?

Want to identify your Data Mesh opportunities? Interested in finding out more about Data Mesh? Contact our Data Mesh expert Tom De Wolf for more information or send us a message via our website chat.

Tom De Wolf
Senior Software Architect, Innovation Engineer and Technical Coach