What is a Data Mesh?

A Data Mesh is a decentralised approach to sharing and managing analytical data at scale. The Mesh emphasises data as a product, ownership of data by domain experts, federated governance and a shared data platform.

Common data challenges:

Overworked centralized data team causing bottlenecks

Operational databases running analytics causing performance issues

Excessive use of ETL jobs

Poor data quality and governance (a data swamp) that has led to security risks and opportunity cost

Siloed, difficult-to-access data assets

Accidental data architecture

Why now?

The growth in scope and capability of AWS data services means that the technology to realise a data mesh is readily available today.

Who can benefit?

Organizations experiencing challenges managing and scaling data.

Organizations planning significant migration and modernization programs.

Companies looking to unlock innovation and GenAI use cases can gain significant advantages by getting their data strategy on a solid foundation.

How it works:

Discovery: The accelerator begins with a discovery workshop. Following this, we work with you to understand the data landscape of your organization and identify pilot data products and business use cases.

Pilot Phase: During the pilot phase, we deploy our Mesh Accelerators to rapidly build out a self-service platform on top of AWS native data services. We work collaboratively with internal customer teams to define data contracts and enable the creation of pilot data products. This is an iterative and transparent process focused on delivering the identified business use cases.

Rollout: The learnings from the pilot phase are used to develop a full incremental rollout and adoption plan for the Data Mesh tailored to your organization as a key deliverable from the accelerator.

Post-Accelerator: We support the incremental rollout and wider organizational adoption of the mesh through strategic consultancy and/or hands-on implementation.

Benefits:

Improved data quality: Data is published and described by decentralized expert teams.

Increased agility: Autonomous domains can run faster with higher agility.

Improved data observability: The platform monitors data production and consumption metrics.

Improved data provenance: The platform can track lineage and improve auditability.

Improved governance and security: The platform controls access policies and contracts centrally.