Does Data Mesh provide the Amperage & Voltage for digital transformation?.

We are going through a phase of data modernization unlike anything in the past. And to embrace this change many organizations are looking at ways to modernize their data architecture.

Developed by Thoughtworks’ Zhamak Dehghani, the Data Mesh is a simplified data platform architecture that acknowledges the universality of data in the enterprise making it self-service and domain driven.

Data Mesh will be critical to the success of the digital transformation that we see unfold.

In today’s digital era, enterprises are using hybrid architecture that have multi-cloud monolithic aspects to the enterprise architecture, microservices and ingestion layer, etc. which makes it difficult for enterprises to seamlessly manage the complexity.

To be data driven adhering to Data Mesh provides a decentralized architecture view which allows configuration and management of a variety of data resources through the data hub.

Through Data Mesh enterprise get a single, consolidated view of data and applications performance through a unified Digital consumption layer making dashboards and self-service more personalized.

Just as Ampacity is the maximum current that a semi-conductor can carry continuously under the conditions of use without exceeding its temperature rating, similarly a well thought data strategy can handle maximum load, throughput and multi-processing. Data Mesh recommended architecture guidance provides the correct right fit size for data to traverse across, if not the sheer volume of data and the processing throughput will reduce the data capacity and performance of the architecture, which will in turn improve democratization and scalability.

Similarly, as voltage is the measurement of electricity passing thru, in Data Mesh, this will co-relate to how data gets processed from the source layer through ingestion, cleaning, prepping and aggregation, etc, only difference is in a well architected Data Mesh solution data is productized and distributed to individual Lines of Business (LoB’s) and each individual domain will have the freedom to consume data per their desired/usage needs.

Lets' look in a recent case study a multinational car manufacturing had challenges with their data environment as data was often duplicated in a decentralized landscape with many legacy systems. Capabilities such as Data Cataloging, Governance, etc. were partially available and being data rich, the architecture was information poor.

An enterprise level data mesh architecture was established to make data and business value across the organization and were API driven. Data Mesh Architecture successfully provided cross-functional values plus suitability wise addressed both operational and analytical purposes. Last but not least also also allowed the onboarding of new demands and business models, from a MVP into a final data asset.

Having implemented many distributed architecture solutions for Key customers, here's best practice approach to implementing Data Mesh.

Indeed, Data Mesh provides both Amperage and the Voltage for modern enterprise platform, there has been a lot of discussion going around on how best to implement it and deploy.

As you are on your journey getting started with Data Mesh, ensure to avoid architecture pitfalls such as data redundancy and de-duplication, doing so adhering to common best practices is highly recommended.

If you're still wondering if data mesh is the right choice and interested to learn more about Data Mesh distributed architecture and getting value driven real results for your organization, glad to connect on a free thought leadership workshop session.