— The Thinking of Bill Schmarzo —
Applied analytics case studies spanning healthcare predictive modelling, IoT and operational efficiency, sales transformation, and real-world data science deployment.
What distinguishes IoT from conventional big data, Schmarzo argues, is not the accumulation of sensor readings in a data lake — a practice already well established — but the capacity to filter that torrent of largely redundant data at the point of origin. A drill-bit sensor emitting ten identical te
The gap between holding vast data and acting on it is illustrated by a grocery chain that accumulates two decades of individual purchasing behaviour — every coupon, basket item, and store visit — yet responds with a generic weekly flyer. Bill Schmarzo argues that organisations cross into genuine dat
What this exposes is a gap between the volume of actionable data hospitals already possess and the degree to which that data is being systematically deployed. A hospital CIO recently described to Schmarzo a fall-risk scoring model — weighting patient age, existing conditions, and mobility history —
The gap between industries absorbing Big Data is starker than commonly assumed. Healthcare providers remain mired in the foundational challenge of simply unifying patient records across disparate systems — a prerequisite for any meaningful analysis — while online retailers have long since cleared th