Skip to main content

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.
Labels:

Comments

Post a Comment

Popular posts from this blog

Can Agentic AI Make Customer Service Truly Real-Time?

  For years, enterprises have tried to make customer service faster — automating workflows, tightening SLAs, launching 24/7 chatbots. Yet customers still wait — not only for responses, but for reassurance that someone understands. Speed alone doesn’t feel like care anymore. Because real-time isn’t defined by seconds — it’s defined by intelligence that understands intent and acts with empathy. That’s the new frontier of customer experience emerging through Agentic   AI for customer service  — a system of intelligent agents that doesn’t just respond instantly but reasons, learns, and collaborates with humans to make service truly real-time. Are We Solving Problems or Just Replying Faster? Most customer service journeys still begin the same way they did a decade ago — a ticket raised, a call logged, an email sent. Every step that follows is a reaction. Agentic AI for customer service redefines that flow. Instead of waiting for a customer to report an issue, intelligent agent...
Post a Comment
Read more

Advanced AI Capabilities in Azure AI Services – 14 questions every CIO should ask

  Recently, Microsoft announced new features in Azure AI Services at the   Ignite 2023   event. As a Microsoft partner, we are following these updates closely and exploring how the new features will unlock more value for enterprises.  The slew of updates is more focused on empowering businesses with enterprise-grade generative AI applications – from leveraging cutting-edge foundation models to building AI applications to enhancing user experiences on those AI applications.   Developing generative AI applications that work exclusively on your enterprise data, for your enterprise is a complex process. You need powerful GPUs to finetune large language models (LLMs). Making this process easier for enterprises, Microsoft launched  Model as a Service  (MaaS) in the Azure AI model catalog. Using MaaS, you can finetune LLMs and build generative AI applications using inference APIs. You will be charged for the number of tokens used as part of the pay-as-yo...
Post a Comment
Read more

An Ultimate Guide to Measure Real ROI of AI Assistants in Business

We are almost at the end of the 2025 second quarter, and the CIO forums' discussions have shifted from experimenting with AI to incorporating AI into the core. The discussions have evolved from virtual assistants to  AI assistants . Today, the competitive advantage lies not in experimenting with AI, but in quantifying its value and proving its impact across sales, HR, IT, and customer support. For business leaders, ROI is the ultimate lens that distinguishes between hype and the true AI transformation. The primary step to move up the ladder from AI pilots to strategic ROI is to define the potential use case. This article explores how to define, measure, and communicate the ROI of AI assistants through frameworks, KPIs, and real-world examples, so executives can lead AI adoption with clarity and confidence. We have also decoded a Boardroom-ready equation for the ROI. Why ROI matters more than anything else? For today’s CIOs and business leaders, ROI is the ultimate proof point. It’s...
Post a Comment
Read more