Real-Time M&A Intelligence for 18,000+ Dealerships
Databricks
Python (Django)
React
AWS S3
Gemini
Tech Stack
Client Profile
Industry
Automotive
Region
North America
Technology
Databricks
Overview
A leading automotive advisory firm that provides M&A and investment insights for the U.S. car dealership market struggled to leverage its raw data, coming from over 18,000 dealerships spanning decades. Each record had roughly 150 fields drawn from Polk, Helix, demographic and population datasets and other open sources and APIs. This had issues of inconsistent formats, missing common identifiers that prevented easy merging, and large gaps. These problems slowed extraction of actionable insights: full data refreshes took more than a week and blocked timely, strategic decisions such as dealership valuations.
To resolve the client's data challenges, Shorthills AI developed JumpIQ, an AI-powered platform that ingests and processes raw data from Polk, Helix, and other open APIs directly into Databricks. A robust data engineering pipeline was built for intelligent merging (using techniques like fuzzy matching and address normalization), cleaning, mapping, and formatting to create a unified “golden record” for each dealership. On this refined data foundation, advanced AI/ML models were deployed for predictive analytics, including revenue forecasting, sales efficiency, dealership valuation, and performance scoring—all accessible through a web-based dashboard offering detailed analytical reports and visual insights.
As a result, the client reduced data processing time from over a week to just 8 hours, gained a single clean and accurate database, and obtained significantly stronger predictive insights that enable faster, more confident strategic decisions.
Modernizing omnichannel retail analytics on Azure—streaming web + POS into a governed Databricks lakehouse to cut 4-hour processing to real-time.
Industry
Retail
Region
APAC
Technology
Databricks
Transforming omnichannel retail analytics on Azure—streaming web and POS data into a databricks lakehouse to cut 4-hour processing to real-time reporting.
Industry
Retail
Region
APAC
Technology
Databricks
Executive Summary
A fast-growing omnichannel electronics retailer outgrew its on-prem systems: transaction spikes from e-commerce and hundreds of in-store POS machines strained Oracle, slowed upserts to 3–4 hours, and kept analytics teams from building recommendations, churn models, and personalization. Shorthills delivered a modern Azure data lakehouse with Databricks to stream online and in-store transactions. Phase 1 onboarded historical data; Phase 2 enabled real-time ingestion so teams access a single source of truth for business intelligence(BI) and machine learning(ML). Result: data availability moved from hours to real-time, unlocking governed self-service analytics and a foundation for personalization and predictions.
Tech Stack
Azure Ecosystem- Azure Databricks, Event Hub, Logic App, Service Bus, ADLS Gen2
Power BI
Apache Nifi
Executive Summary
A fast-growing omnichannel electronics retailer outgrew its on-prem systems: transaction spikes from e-commerce and hundreds of in-store POS machines strained Oracle, slowed upserts to 3–4 hours, and kept analytics teams from building recommendations, churn models, and personalization. Shorthills delivered a modern Azure data lakehouse with Databricks to stream online and in-store transactions. Phase 1 onboarded historical data; Phase 2 enabled real-time ingestion so teams access a single source of truth for business intelligence(BI) and machine learning(ML). Result: data availability moved from hours to real-time, unlocking governed self-service analytics and a foundation for personalization and predictions.
Tech Stack
Azure Ecosystem- Azure Databricks, Event Hub, Logic App, Service Bus, ADLS Gen2
Power BI
Apache Nifi
Modernizing Leading U.S. Automotive M&A with Databricks—unifying data from 18,000+ dealerships into golden records to deliver explainable valuations, standardized forecasts, and 8-hour refreshes
Industry
Automotive
Region
North America
Technology
Databricks
Databricks
Python (Django)
React
AWS S3
Gemini
Tech Stack
Executive Summary
A leading U.S. automotive advisory firm struggled to turn decades of raw data from 18,000+ dealerships—spread across Polk, Helix, demographic datasets, and multiple APIs—into actionable insights. The fragmented and inconsistent data made full refreshes take over a week, delaying critical decisions like dealership valuations. Shorthills AI developed JumpIQ, an AI-powered platform that ingests this data into Databricks, creating unified “golden records” through intelligent cleaning, mapping, and merging. Advanced AI/ML models then deliver predictive analytics via a web dashboard with detailed reports and visual insights. The result: data processing dropped from over a week to 8 hours, the client gained a single accurate database, and predictive insights now support faster, more confident decisions.
Modernizing Leading U.S. Automotive M&A with Databricks—unifying data from 18,000+ dealerships into golden records to deliver explainable valuations, standardized forecasts, and 8-hour refreshes
Industry
Automotive
Region
North America
Technology
Databricks
Tech Stack
Databricks | Python (Django) | React | AWS S3 | Gemini
Executive Summary
A leading U.S. automotive advisory firm struggled to turn decades of raw data from 18,000+ dealerships—spread across Polk, Helix, demographic datasets, and multiple APIs—into actionable insights. The fragmented and inconsistent data made full refreshes take over a week, delaying critical decisions like dealership valuations. Shorthills AI developed JumpIQ, an AI-powered platform that ingests this data into Databricks, creating unified “golden records” through intelligent cleaning, mapping, and merging. Advanced AI/ML models then deliver predictive analytics via a web dashboard with detailed reports and visual insights. The result: data processing dropped from over a week to 8 hours, the client gained a single accurate database, and predictive insights now support faster, more confident decisions.
Challenges
Omnichannel retailers juggle surging e-commerce and in-store POS data that legacy, siloed systems struggle to handle. Slow upserts and fragmented feedOmnichannel retailers juggle surging e-commerce and in-store POS data that legacy, siloed systems struggle to handle. Slow upserts and fragmented feeds delay reporting and block personalization, churn models, and timely decisions. With a unified, real-time lakehouse, teams get a single source of truth for BI and ML at scale. s delay reporting and block personalization, churn models, and timely decisions. With a unified, real-time lakehouse, teams get a single source of truth for BI and ML at scale.
Unscalable infrastructure
On-premise architecture couldn’t handle surging online + POS transactions; upserts took 3–4 hours.
Fragmented
data
Website and store data existed in silos, preventing a unified view of customers and transactions.
Limited analytics access
Slow, restricted data access blocked recommendations,
churn analysis, and personalization.
Challenges
On-premise architecture couldn’t handle surging online + POS transactions; upserts took 3–4 hours.
Unscalable infrastructure
Website and store data existed in silos, preventing a unified view of customers and transactions.
Fragmented data
Slow, restricted data access blocked recommendations, churn analysis, and personalization.
Limited analytics access
Omnichannel retailers juggle surging e-commerce and in-store POS data that legacy, siloed systems struggle to handle. Slow upserts and fragmented feeds delay reporting and block personalization, churn models, and timely decisions. With a unified, real-time lakehouse, teams get a single source of truth for BI and ML at scale.
What Shorthills Did
We brought website and in-store POS data into one trusted place and making sure it is updated in real time. Historical records were cleaned and loaded first; then live e-commerce and POS events started streaming straight into the lakehouse. From there, teams get ready-to-use datasets for dashboards and data science, with secure, role-based access so everyone sees the same numbers.
Historical data migration
We moved legacy transactions into an Azure Databricks lakehouse, storing cleansed records in Delta tables for reliability and fast updates.
Unified real-time ingestion
We built NiFi→Event Hub pipelines to stream e-commerce and POS events into the lake, eliminating delays and unifying sources.
High-performance processing on Databricks
We autoscaled PySpark jobs standardized data and produced curated gold datasets for BI and ML.
We set-up encryption in transit/at rest, Azure Key Vault, and Unity Catalog RBAC for fine-grained, role-based access.
Security & governance
What Shorthills AI Did
We brought website and in-store POS data into one trusted place and making sure it is updated in real time. Historical records were cleaned and loaded first; then live e-commerce and POS events started streaming straight into the lakehouse. From there, teams get ready-to-use datasets for dashboards and data science, with secure, role-based access so everyone sees the same numbers.
Historical data migration
We moved legacy transactions into an Azure Databricks lakehouse, storing cleansed records in Delta tables for reliability and fast updates.
Unified real-time ingestion
We built NiFi→Event Hub pipelines to stream e-commerce and POS events into the lake, eliminating delays and unifying sources.
High-performance processing on Databricks
We autoscaled PySpark jobs standardized data and produced curated gold datasets for BI and ML.
Security & governance
We set-up encryption in transit/at rest, Azure Key Vault, and Unity Catalog RBAC for fine-grained, role-based access.
Our Solutions
Data Foundation: Lakehouse & Entity Resolution
We stood up a Databricks-powered lakehouse with medallion layers (bronze → silver → gold) and survivorship rules to reconcile conflicts. Fuzzy matching plus brand/state heuristics created a durable golden dealer record across renames, mergers, and closures—an analytics-ready backbone with end-to-end lineage.
Signals & Feature Engineering
On unified records, we built a reusable catalog of 150+ signals per dealership spanning performance, market, and macro indicators. Features are standardized across brands/states and versioned over time, so valuations, forecasts, and benchmarks stay fair and reproducible.
Valuation & Forecasting Engines
A model suite blends store performance with market signals to produce explainable valuations and forward-looking forecasts. Scenario/sensitivity views test brand, geography, and macro assumptions—accelerating buy/no-buy calls with consistent methodology.
Delivery Experience: Analyst App for M&A Workflows
A secure analytics app streamlines real M&A tasks: search/filter/compare, geospatial views, and exportable diligence summaries. Built on governed tables and shared definitions, it keeps every stakeholder aligned—from board decks to deep dives.
Outcomes
A fast-growing electronics retailer was waiting 3–4 hours for Oracle upserts, with web and POS data stuck in silos—slowing reports and blocking personalization. With Shorthills AI’s data engineering capabilities, transactions now land in real time, giving leaders and analysts a single, reliable source of truth. Automated Power BI reporting replaces manual extracts, and unified customer and order histories enable immediate performance checks by channel, store, and SKUs. Because curated datasets refresh continuously, data science can launch churn models, recommendations, and targeted campaigns on current signals instead of the historical data. Governance is built in—encrypted storage and role-based access—so speed doesn’t compromise control. Net result: hours-long waits become instant insight, teams align on one view of the business, and the retailer gains a solid foundation for personalization and ML-driven growth.
Real-time availability
Processing moved from 3–4 hours to real-time.
360° customer view
Unified omnichannel data for instant, reliable reporting.
ML-ready foundation
Platform can now support personalization, churn prediction, and targeted marketing.
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Outcomes
A fast-growing electronics retailer was waiting 3–4 hours for Oracle upserts, with web and POS data stuck in silos—slowing reports and blocking personalization. With Shorthills AI’s data engineering capabilities, transactions now land in real time, giving leaders and analysts a single, reliable source of truth. Automated Power BI reporting replaces manual extracts, and unified customer and order histories enable immediate performance checks by channel, store, and SKUs. Because curated datasets refresh continuously, data science can launch churn models, recommendations, and targeted campaigns on current signals instead of the historical data. Governance is built in—encrypted storage and role-based access—so speed doesn’t compromise control. Net result: hours-long waits become instant insight, teams align on one view of the business, and the retailer gains a solid foundation for personalization and ML-driven growth.
Real-time availability
Processing moved from 3–4 hours to real-time.
360° customer view
Unified omnichannel data for instant, reliable reporting.
ML-ready foundation
Platform can now support personalization, churn prediction, and targeted marketing.