Data Mesh vs Data Lake – Driving Business Insights at Scale

Data is now the soul of every digital business, and the pandemic has accelerated the adoption of Analytics and AI as a business function. Over the past few years, organizations had to rapidly move to new data technologies, modern data architectures, and infrastructure to drive innovations such as personalized product recommendations and predictive analytics. Despite such changes, collection, integration, and governance of data is still the main inhibitor to Analytics and AI success, says Deloitte Research.

The evolution of business insights platforms can be fragmented into three generations, as per Zhamak:

Organizations deployed traditional data warehouses in the first generation to generate reports as per the need. This was very expensive and lacked a centralized approach.
As Big data and analytics gained popularity in the second generation, data warehouses were replaced by a central data lake. Though this became very popular, a few bottlenecks like data volumes, scalability, domain-specific data highlighted the need for a decentralized approach.
The current third-generation platforms address a few gaps and garnered attention towards product thinking with data, self-service platforms design, and distributed domain-driven architecture. All these gave way to a Data Mesh architecture.

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