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 healthcare analytics for a U.S. payer—leveraging an Azure Databricks lakehouse to unify fragmented data and achieve 40% lower storage cost.
Industry
Healthcare
Region
North America
Technology
Databricks
Executive Summary
A U.S.-based healthcare payer network needed to modernize fragmented clinical, claims, and payer data to improve outcomes and control cost. Legacy systems, mixed healthcare formats (HL7/CCDA/CSV), and poor data quality blocked analysis like risk scoring, cost estimation, and proactive care. Shorthills built a modern Azure data lakehouse on Databricks, migrated multi-terabytes of historical data, and implemented ongoing ingestion with custom parsers, governed by Unity Catalog and Key Vault. Results: Advanced analytics now generate patient risk scores, cost estimates, and readmission predictions, delivered via secure Power BI dashboards. Outcomes include multi-million-dollar cost savings, ~40% storage cost reduction.
Tech Stack
Delta Lake
Power BI
Custom HL7/CCDA Parsers
Azure Data Lake Storage - ADLS Gen2, Azure Databricks, Azure Key Vault
Executive Summary
A U.S.-based healthcare payer network needed to modernize fragmented clinical, claims, and payer data to improve outcomes and control cost. Legacy systems, mixed healthcare formats (HL7/CCDA/CSV), and poor data quality blocked analysis like risk scoring, cost estimation, and proactive care. Shorthills built a modern Azure data lakehouse on Databricks, migrated multi-terabytes of historical data, and implemented ongoing ingestion with custom parsers, governed by Unity Catalog and Key Vault. Results: Advanced analytics now generate patient risk scores, cost estimates, and readmission predictions, delivered via secure Power BI dashboards. Outcomes include multi-million-dollar cost savings, ~40% storage cost reduction.
Tech Stack
Azure Data Lake Storage - ADLS Gen2, Azure Databricks, Azure Key Vault
Delta Lake
Power BI
Custom HL7/CCDA Parsers
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.
Challenges
Oracle history, Admit, Discharge, Transfer(ADT) events, and payer feeds lived in silos with inconsistent formats (HL7/CCDA/CSV).
Legacy systems & fragmented data
Poor data quality & scalability limits
Inconsistent, low quality data, costly on-premise warehouse, and limited scale undermined credibility and analytics.
Limited advanced analytics
No reliable foundation for advanced analytics like risk scoring, cost estimation, or proactive identification of high-risk patients.
Healthcare networks juggle fragmented clinical, claims, and payer data across legacy systems and mixed formats, making timely, reliable insight hard to achieve. Inconsistent quality and siloed feeds slow risk scoring, cost estimation, and readmission prevention—driving up spend and delaying care. With a governed, real-time lakehouse, teams move from manual wrangling to proactive, data-driven decisions at scale.
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.
What Shorthills AI Did
We pulled clinical, claims, and payer data into one trusted Databricks lakehouse and cleaned it up so every member has a single, reliable record. HL7/CCDA files are parsed and standardized; duplicate entries are fixed. From this foundation, dashboards show the latest metrics, and models score risk, estimate cost of care, and flag likely readmissions. Access is role-based and encrypted, so teams can use the data confidently and cut the manual wrangling.
We migrated terabytes from legacy Oracle to ADLS Gen2 + Databricks Delta Lake, establishing an ACID, high-performance single source of truth.
Modern Azure Lakehouse Architecture
We built pipelines to ingest HL7, CCDA, and CSV data into ADLS, adding custom parsers and quality checks before publishing to Delta tables.
Unified Ingestion with Custom Parsing
We standardized model inputs to enable patient risk scoring, cost-of-care estimates, and readmission predictions—helping teams move from reactive to proactive care.
Advanced Analytics for Proactive Care
We set-up encryption in motion/at rest, Azure Key Vault, Unity Catalog RBAC for least-privilege access, and Power BI dashboards for administrators.
Security, Governance & BI
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.
Outcomes
A U.S. healthcare payer was stuck stitching together siloed clinical and claims feeds, which slowed risk scoring and drove up storage and operating costs. With Shorthills AI’s data engineering capabilities, data now lives in one clean source of truth that refreshes reliably and feeds risk, cost, and readmission models. Storage spend drops by ~40% after retiring the legacy warehouse, contributing to multi-million-dollar savings. Automated pipelines replace manual pulls, so analysts focus on insights, not cleanup, and management get consistent Power BI views. Because high-risk members are flagged earlier, care teams can intervene sooner and plan premiums more accurately. In short: unified data, lower cost, and proactive decisions that improve outcomes at scale.
Cost reduction at scale
Phased out the legacy data warehouse achieving ~40% reduction in storage costs by leveraging Delta Lake.
Proactive patient management
Risk-based insights reduced readmissions and improved premium planning.
Operational efficiency
Automated pipelines and a governed lakehouse eliminated manual work and enabled reliable, timely analytics.
Frequently Asked Questions
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