Data Mesh Information Architecture
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Case Study: Data Mesh at a Leading European Retailer

Recognizing digital transformation as a key to competitiveness and growth, the retailer embraced it early on. Their commitment to becoming a data-driven organization stems from the wealth of customer, product, and transaction insights gleaned across various business processes, both internal and client-facing.

Due to their existing analytics stack, the retailer faced significant hurdles in their quest to become more data-driven. The traditional business intelligence system, built on Microsoft SQL Server and Reporting Services, was showing its limitations. Plagued by scalability challenges, it consisted of multiple distinct data and BI silos, each managed by separate data science teams. This fragmented structure, coupled with the complex nature of data and the extensive business expertise required from the data science teams, severely limited the company’s ability to expand its reporting and analytical capabilities, particularly as the volume and complexity of data sets grew.

To revolutionize its analytics capabilities, the company introduced a “modern data stack” to align with its already established decoupled, decentralized software architecture, developed in collaboration with CID. This strategic shift was designed to offload the data teams from business-centric tasks, placing data ownership and responsibility directly within the business units where the data originated. Such a change promised to minimize friction and inefficiencies in communication between business and technical teams, and it would significantly increase analytics capacity.

A critical component of this transformation was establishing self-service features, enabling business users to autonomously create dashboards and ad-hoc reports, fostering acceptance, and increasing adoption across the organization.

Evolving Data Culture Demands Next-Gen BI

A growing team data culture propelled a bottom-up demand for a more efficient, self-service data access solution. This led to defining key objectives and desired results for a next-gen analytics platform that would facilitate more informed and timely decisions by business users. Anticipated features included self-service functionalities through tools like Tableau, creating business user-friendly data marts, and real-time analytics.

The envisioned solution promised to revolutionize several use cases, including:

  • HR: Enabling more timely and efficient sourcing needs assessment
  • Sales: Mitigating return rates across various geographies
  • Logistics: Streamlining order and tailoring processes
  • Online Sales: Offering personalized product recommendations to online customers

Data Mesh: Building a Scalable Information Architecture

CID offers critical experience in establishing modern information architecture, including Data Mesh. This is how we typically do it.

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Data Mesh Information Architecture

A State-of-the-Art Information Architecture

The resolve to transition to a “modern data stack” instigated several pivotal decisions. The commitment to a micro-architecture facilitating decentralized process and data ownership promised to alleviate the prevailing scalability issues. Concurrently, a structured approach was adopted to model and describe data products, fostering a shared understanding of term definitions and data semantics.

The inception of a Data Mesh was a pivotal point in this transformation. As a sophisticated, decentralized system, the Data Mesh would offer scalable data products, integrating seamlessly with the already existing decoupled software services architecture championed by CID.

In alignment with the revolutionary blueprint, the enterprise migrated to a cloud data platform, opting for Snowflake on Microsoft Azure over the on-premises Microsoft SQL environment. Following a thorough evaluation, the company selected Tableau to enable business users to create dashboards and ad-hoc reports as a self-service. Moreover, Kafka was chosen to stream data from the operational systems into the data mesh, allowing up-to-the-minute analyses.

Conclusion

Building upon the existing software architecture, the foundational layer of the Data Mesh and the inaugural set of data products and data marts were successfully deployed. The swift implementation began a new era where business processes, analytics, and decision-making are continually enhanced through an increasing array of data products and insights. The organization is now on a progressive path, leveraging a state-of-the-art analytics system to foster a culture of informed decision-making, efficiency, and innovation.

Key benefits

  • More comprehensive and timely self-service insights for business users
  • Better scalable analytics performance overall, supporting more data-driven processes and decision-making across the organization
  • Improving data culture and data literacy, fostering an even more substantial use of data while laying the foundation for increasing benefits from AI

Retail Media: Leveraging Data as a Product

In the evolving landscape of retail business, chains, including supermarkets, are perpetually in the quest for lucrative opportunities to spur growth and enhance profitability. A frontrunner in this innovation race is leveraging customer data, not just as a tool for internal improvements but as a product with immense market potential.

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Retail Media

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