47+ projects.
Measurable outcomes.

A global manufacturer operated 12 disparate data systems with no unified analytics layer, causing 48-hour reporting delays and an inability to correlate production, supply chain, and financial data.

A Fortune 500 retailer ran 340 overnight batch ETL jobs with 200+ manual intervention points per week. Data freshness lagged 24 hours, blocking same-day markdown and inventory decisions.

A regional bank ($12 B AUM) ran 180 SSIS packages on end-of-life on-premise SQL Server 2008 hardware. Rising maintenance costs, zero scalability, and an imminent EOL support deadline created critical business risk.

A healthcare provider network (800 facilities) ran its analytics on an aging 15 TB on-premise SQL Server data warehouse. Critical BI reports took 8 hours to render, blocking clinical and operational decision-making.

A 50M-user e-commerce platform ran fraud detection on 4-hour batch cycles, losing an estimated $2M/month in preventable fraudulent transactions that slipped through the detection window.

An energy conglomerate stored operational data across 30+ isolated systems with no enterprise analytics capability, leaving compliance teams unable to satisfy regulatory reporting and executives unable to correlate KPIs.

A top-10 P&C insurance carrier aggregated data from 25 policy and claims systems with incompatible schemas. Consolidated reporting lagged 2 weeks, preventing actuarial teams from pricing risk accurately.

A top-20 US commercial bank had 2,000+ unmanaged Databricks notebooks, no data lineage, and no reproducibility — posing material risk ahead of a scheduled Fed regulatory exam.

An FDA audit uncovered 34 data quality violations in clinical trial data for a drug with a $50M approval at stake. The company had no automated quality monitoring and relied entirely on manual validation.

A 15M-subscriber streaming platform suffered ACID violations and silent data corruption in its Hive Metastore, leading to $400K/month in manual data correction and erroneous recommendation signals.

A state agency with 15,000 employees ran analytics across 400+ disconnected Excel reports. There was no version control, no data governance, and a 3-week turnaround for ad-hoc analysis requests.

A 3PL processing 500K shipments/day received GPS events out-of-order and with duplicates. The inability to compute accurate real-time ETAs was costing the business $12M in SLA penalty exposure.

An automotive parts manufacturer operated 75 global plants on SQL Server 2005 databases with 20 years of undocumented stored procedures. End-of-support and security vulnerabilities required an urgent migration.

Analysts at a global investment bank spent 40% of their time searching for data. With 150+ source systems and no lineage, the firm faced GDPR and CCPA compliance gaps that regulators had flagged.

A national specialty retailer with 800 stores operated disconnected POS, inventory, and e-commerce systems. No unified customer view existed, causing fragmented personalization and inventory blind spots.

A top-5 US retail bank faced a 45-minute average call center wait time, 68% first-call resolution rate, and $120M annual call center operating cost — all while customer satisfaction scores were declining.

A heavy machinery manufacturer ($8B revenue) experienced unplanned equipment downtime costing $180K/hour. With 2,000 machines and a purely reactive maintenance model, critical failures were impossible to anticipate.

A B2B wholesale distributor with 1.2M SKUs relied on sales reps manually suggesting products. Cross-sell attachment sat at 12%, leaving an estimated $800M in annual cross-sell revenue untapped.

A media conglomerate maintained 3 petabytes of content assets and analytics data on aging on-premise infrastructure costing $15M/year in maintenance. Systems could not scale for streaming growth.

A global retail chain operating in 60 countries ran 60+ separate country-level analytics systems with no unified global insight layer. Cross-border analysis required weeks of manual data wrangling.