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This article details how an engineering team leveraged Lakehouse architecture and high-volume AI-generated code to rapidly unify fragmented customer data across hardware, software, and connectivity sectors. The project demonstrates a practical application of scaling AI development to solve legacy data silo problems that previously hindered cross-selling opportunities.

Source coveringAppDirect

What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Four Weeks to a Single Customer View: How a Lakehouse Architecture Solved an Old Problem for Advisors

AppDirectOctober 19, 20266 min read

TL;DR A small AppDirect engineering team built a unified view of customers, orders, and quotes across three business units in four weeks, with no manual coding. Advisors now have one place to see every customer across hardware, software, and connectivity. It runs on a Lakehouse architecture, and a person reviews every duplicate match before any records merge. A project like this would normally be...

A small AppDirect engineering team built a unified view of customers, orders, and quotes across three business units in four weeks, with no manual coding. Advisors now have one place to see every customer across hardware, software, and connectivity. It runs on a Lakehouse architecture, and a person reviews every duplicate match before any records merge. A project like this would normally be scoped in quarters. AppDirect advisors serve customers who buy hardware, software, and connectivity, but the systems behind those product lines don't talk to each other. A single customer can look like three different customers depending on which screen an advisor is on. Cross-sell opportunities hide in plain sight. Basic questions go unanswered. A small AppDirect engineering team…

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