Implementing Data Mesh Architecture as part of solid data foundations

Problem statement 

Data Mesh is well established concept that offers a decentralised approach to data architecture enabling different teams to easily discover, use, and trust data products and assets. However, many organizations face significant challenges in the practical adoption of Data Mesh, including lack of expertise, governance and poor data quality to name a few. According to Gartner 80%, of companies attempting to adopt Data Mesh architecture fail within two years. In short, Data Mesh is difficult to adopt in practice, because it requires coordinated effort in developing expertise, changing key processes, and improving data quality. Investment in technology alone is why most enterprises typically fail.

Mirek shares why strong cloud-native foundations — scalable data lakes and unified governance — are essential for success, with AI acting only as an accelerator, not a substitute for control.

Author’s perspective and expertise

Observations and learnings from recent projects 

Rather than adopting Data Mesh for the sake of creating something novel, our observations have led us to promote the following key principles instead:

• Closing the expertise gap: many teams lack the skills needed to configure and optimize data platforms to meet business end user needs, which slows adoption. As an example, a retail team lacked cloud ETL skills, so they ran workshops with experts, enabling faster and better data platform use aligned to business needs.
• Addressing data quality challenges: missing attributes, inconsistent definition of data domains and lack of standardization across data sources undermine trust in data products. This requires a targeted and collaborative approach. Consider the case of, a finance firm standardized customer segment definitions via a cross-team task force and automated data checks, improving data trust.
• Creating robust governance: siloed Data Quality and Master Data Management initiatives leads to duplication of effort. This requires a unified governance framework across key business functions, and processes. Such as, a healthcare provider combined parallel data quality and MDM teams into one governance group, reducing duplication and improving patient data consistency.
• Embracing organizational change: Lack of data ownership and stewardship across key business domains requires well defined roles, thorough training, and strong collaboration between business and technical stakeholders. To illustrate, a manufacturer assigned clear data steward roles and provided training, boosting accountability and collaboration between business and IT.
• Configuring with precision: Data Platforms that cannot extract, transform or load data are prevalent and must be correctly configured before attempting to build in layers of automation. For instance, an e-commerce company improved its ETL processes by leveraging Large Language Models (LLMs) to automate the parsing and classification of complex product descriptions, enhancing data accuracy and enabling more effective automation downstream.
• Scaling gradually: Attempting a big bang transformation has a great probability of failure than incremental changes. This requires a consolidated approach and effective release management process to running multiple and frequent pilots. Take for example, a telecom data engineering team replaced a big-bang rollout with incremental pilots to validate pipelines and scale infrastructure safely, reducing risk and boosting adoption.

Data Mesh Principles

A word of caution 

Whether its striving towards a more federated data architecture or reverting back to a more centralised version, the transition can be complex and riddle with common points of failure, including but not limited to:

• Lack of integration with Active Directory and Unity Catalogues exposing compliance and security vulnerabilities.
• No golden path for storing, orchestrating, cataloguing, and accessing data stifling self-service behaviour and mindset and creating a dependency on a single point of contact.
• Weak data contracts without standardized schemas, shared identifies, consistent master and meta data making versioning difficult to track, repeatable errors more prevalent and discovery more difficult
• Lack of CI/CD process resulting in manual deployments, slow releases, persistent defects, repeated revisions and frequent outages.
• Siloed data quality and master data management initiatives without a comprehensive governance framework propagating inconsistent naming conventions, and taxonomies across data domains

By addressing these common points of failure the organization can then focus on adding layers of automation using modern tools like Databricks, Microsoft Fabric and Snowflake that come with configurable agents, which we will cover in the next point of view on how agents can enable Data Mesh.

Concluding point 

With deeper expertise in tools such as Databricks, Microsoft Fabric, and Snowflake, successful adoption of Data Mesh architecture will become more prevalent. However, this not something that will be enabled solely through technology, as its all too common to see organization fail to address foremost pitfalls relating to people, processes and data.

Supporting perspectives 

To see how these foundations translate into business outcomes:

• Building the Data Foundations for AI Success: Lessons from Digital Transformation by Adam Rogalewicz shows how solid data quality, master data management, and governance enable scalable and cost-effective data architectures that drive measurable business value. Read Adam’s perspective.
• Beyond PoC: How Strategy, Governance, and the Right Team Turn GenAI into Trusted Results by Natalia Roślik emphasizes the importance of unified governance frameworks, clear ownership, and cross-functional teams to build trust and scale decentralized data initiatives successfully. Read Natalia’s perspective.