Chosen Theme: Case Studies on Successful Data-Driven Decision-Making
Retail Turnaround: From Gut Feeling to Data-Led Growth
Customer journeys revealed drop-offs at cart and shipping stages, while inconsistent markdowns confused loyal shoppers. Leaders debated causes without evidence. A decision was made to quantify friction, attribute losses precisely, and define a shared truth from behavioral, pricing, and fulfillment data.
Healthcare Decisions that Save Lives: Reducing Readmissions
Technical teams unified EHR events, discharge notes, medication fills, and neighborhood-level social determinants. Data engineers prioritized data quality checks clinicians could understand, surfacing definitions, lineage, and bias considerations to ensure decisions were made on solid, transparent foundations.
Supply Chain Resilience Through Forecasting
Forecasts incorporated localized weather, retailer promotions, and anonymized search patterns. These early signals flagged demand surges days sooner than legacy methods. Planners finally saw cause-and-effect relationships, enabling proactive moves rather than reacting to past week’s shortages.
Marketing data spanned search, social, display, email, affiliates, and offline. A clean taxonomy and consistent identifiers enabled an MMM that accounted for adstock, saturation, and seasonality. This unified lens replaced channel silos with holistic accountability.
Marketing ROI Reimagined with MMM and Experiments
Lift studies ran across matched regions, holding spend steady elsewhere. Results validated strong incrementality for search during peak intent, while certain social campaigns underperformed outside new product launches. Credible confidence intervals simplified executive decisions under uncertainty.
Marketing ROI Reimagined with MMM and Experiments
Predictive Maintenance in Manufacturing
Streaming Telemetry and the First Data Wins
Vibration, temperature, and power signals streamed into a time-series platform. Early exploratory analyses identified telltale patterns preceding faults. The initial win was operational: standardized sensor placement and calibration reduced noise enough to make models useful quickly.
Models at the Edge, Alerts in the Plant
Compact models ran on edge devices for latency, while maintenance dashboards prioritized alerts by cost and safety. Engineers received playbooks with likely causes and recommended checks, turning predictions into consistent actions at the line level.
Safety, Savings, and a Culture Shift
Failure-related incidents dropped, and scheduled micro-stoppages replaced chaotic breakdowns. Savings justified more sensors and training. Most importantly, technicians began proposing analytic ideas, proving that data-driven decision-making grows strongest where hands-on expertise leads the conversation.
Smarter Urban Mobility: A City’s Data-Driven Pilot
Sensors, Fare Data, and Citizens’ Feedback
The project fused traffic sensors, transit fare taps, and crowdsourced delay reports. Data governance addressed privacy from day one. A shared dashboard let transportation staff, bus operators, and community groups see the same objective progress in real time.
A Corridor Pilot with Rapid Iterations
Rather than citywide upheaval, the team piloted bus lanes, signal priority, and stop consolidation on one corridor. Each change ran as an experiment with predefined metrics. Weekly adjustments kept momentum and sustained public trust through visible, measured improvements.
Transparent Results that Built Trust
Average bus travel times dropped, variance shrank, and rider satisfaction rose. Publishing results—including misses—invited constructive feedback and new partners. The city expanded pilots, proving that data-driven decision-making can be both accountable and deeply collaborative.