Original source: SiliconANGLE theCUBE
This video from SiliconANGLE theCUBE covered a lot of ground. 3 segments stood out as worth your time. Everything below links directly to the timestamp in the original video.
Two industries both claiming to embrace Big Data are actually operating in entirely different eras of it — one still trying to see its own patients clearly, the other already optimising for next quarter's sales.
Healthcare Lags Retail in Big Data Readiness as Waste and Fraud Drive Urgency
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 that hurdle and are now deploying social data to drive cross-selling, precision targeting, and real-time trend detection. Industry contacts cited by Bill Schmarzo estimate that between 60 and 70 percent of healthcare spending is wasted through overtreatment, undertreatment, and outright fraud, losses that consolidated data infrastructure could expose almost immediately.
What this exposes is that 'Big Data adoption' is not a single phenomenon but a stratified one, where a sector's regulatory history and data fragmentation determine whether analytics is a competitive tool or simply an unrealised prerequisite.
"If that data is buried in eight different systems, it's hard to catch. If I bring it together, that stuff surfaces right away."
Big Data Splits Into Two Distinct Markets: Customer Intelligence vs. Operational Efficiency
A structural divide is emerging in how organisations deploy Big Data, one that maps less to company size than to business model. Consumer-facing businesses are prioritising the absorption of social media data into existing customer relationship systems, building what Schmarzo describes as social graphs that enable sharper targeting and richer behavioural insight. Operationally driven organisations — utilities, healthcare networks, financial infrastructure operators — are instead focusing on machine-generated data streams from devices such as ATMs, servers, and wind turbines to enable predictive maintenance and capacity planning.
The structural issue here is that these two trajectories demand different architectures, different skill sets, and different investment timelines, meaning that vendor strategies built around a single Big Data narrative risk misreading half their prospective market.
"The B2C space is really understanding more about their customers. The operational space is: how do I leverage this machine-generated data to really operationalise and make more effective my operations."
Enterprises With Legacy BI Systems Face Integration Imperative as Big Data Matures
Organisations that committed heavily to traditional business intelligence platforms face a strategic bind: sunk costs and internal politics make wholesale replacement implausible, yet those retrospective, dashboard-oriented systems increasingly fail to meet demands for real-time and predictive insight. Schmarzo's argument is that the opportunity lies not in abandonment but in augmentation — building bridges between established BI environments and newer, higher-velocity data sources, effectively transforming hindsight-oriented reporting into forward-looking analytical capability.
The real question is not whether legacy BI survives, but rather who captures the integration layer between old infrastructure and new analytical paradigms — a position that carries significant commercial value as enterprises navigate the transition.
"There's a big opportunity for people who are already in the BI space to take those backward-looking BI environments and make them much more predictive in real time."
Summarised from SiliconANGLE theCUBE · 15:34. All credit belongs to the original creators. Streamed.News summarises publicly available video content.
