Same-side network effects · Data coordination

Coordinating data from every Teamplay scanner creates compounding value — with Siemens as the hub

Marshall Van Alstyne's theory: B2B network effects are data-driven, not user-count driven. Siemens can take lessons from the airline and farming industries' coordinated data approach to improve predictive maintenance and reduce machine downtime. The installed base is already there. The flywheel is ready to spin.

Fleet data outputs

What Siemens can compute that no hospital can alone

Cross-hospital benchmarks

Aggregate scan time, image quality scores, and maintenance reports across the installed base. Each hospital sees how they perform vs. anonymized peers.

UNC Health: top 18% for CT throughput

Predictive maintenance signals

Fleet utilization + error log patterns across all scanners predict failure before it occurs. Downtime reduction is measurable and coordinated by Siemens through Teamplay Fleet.

↓ 34% unplanned downtime

Protocol optimization data

Which scan protocols produce the best outcomes by modality and patient type? Pooled data surfaces this. No single hospital can compute it alone.

Fleet of 38,000+ scanners required

The data flywheel

Strategic outcomes

What the flywheel unlocks

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Developer magnet

Anonymized data access is the incentive that attracts developers to build on the marketplace — not revenue share alone.

Cross-side network effect

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Visible ROI dashboard

Every data output maps to a measurable KPI hospitals can show procurement. Makes the value of staying on Teamplay quantifiable.

Retention driver

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Marginal value compounds

The 38,000-scanner installed base means each new provider gets better ML immediately. No startup can replicate this starting point.

Durable moat

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Counterpoint — why Siemens will succeed where GE failed

GE Predix had the same data thesis and failed — not because the thesis was wrong

GE never got enough machines connected to generate useful data before they ran out of patience and capital. The vision was right; the installed base wasn't there. Siemens already has 38,000+ scanners generating data today. The flywheel is ready to spin — the only question is whether Siemens builds the platform to capture it.

Same-side network effects: how data coordination creates compounding value

Van Alstyne's B2B framework applied — each new hospital makes the network more valuable for every existing hospital

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Hospital joins Fleet

Connects scanners. Begins contributing utilization, maintenance, and dose data to the anonymized pool.

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Data flows through Siemens hub both directions

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Pool gets richer

Every additional hospital improves benchmarks, maintenance signals, and protocol recommendations for all existing members.

✈ Airline industry analogy

Each airline shares engine performance data through GE Aviation's coordinated fleet. Every airline benefits from predictive maintenance insights from every other airline's engine hours.

🌾 Farming industry analogy

John Deere's Operations Center aggregates sensor data from across its tractor fleet. Each farmer benefits from soil, yield, and weather patterns computed across thousands of farms they never see.

🏥 Siemens opportunity

38,000+ installed scanners already generating data. Cross-hospital benchmarks on scan time, image quality, and downtime — impossible for any single hospital to compute alone. The substrate is built.