Genera&ng Value from Big Data
in the Internet of Things
THT10421
Cheng Kian Khor
Global Industry Solu&on Leader -‐ IoT/M2M for CSPs
Communica&ons & Media Industry Solu&ons Group
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The Internet
of Things
Moving from M2M to the Internet of Things
Device Cloud & Horizontal Enablers
Focus on
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IoT Ecosystem
Enablers
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Process Enablers & Information
Mashup Enablers
Ve rtic al A pp lica tio ns Ve rtic al A pp lica tio ns Ve rtic al A pp lica tio ns Ve rtic al A pp lica tio ns Ve rtic al A pp lica tio ns Ve rtic al A pp lica tio ns Ve rtic al A pp lica tio ns Ve rtic al A pp lica tio ns Ve rtic al A pp lica tio ns Ve rtic al A pp lica tio nsReduced Time and Cost to Market with Common PlaWorm
Rich Process Interac&on, Informa&on Aggrega&on
Vert
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Vert
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Large scale ver&cal applica&ons
Deep, transforma&ve industry func&onality and ecosystem
IoT Analy&cs – Genera&ng Value from Big Data
UTILITIES
AUTOMOTIVE
HEALTHCARE
MANUFACTURING
Reduced break-‐fix
&me “from 90 to 30 days”
“75% fewer truck rolls” to fix the same number of broken
meters
USD 6M Recovered in losses in one year
Proac&ve iden&fica&on of problema&c meters
50% Reduc&on in field crew cost
Predict wear and tear replacement intervals
Remote, pro-‐ac&ve and guided diagnos&cs
Traffic / Driving Behaviour analy&cs
Usage Based Insurance (UBI)
Remote asset fix rate
“greater than 50%”
Reduced field service dispatch
Reduced down-‐&me through pro-‐ac&ve detec&on of
equipment failures, saving “…hundreds of thousands”
Reduced healthcare / insurance costs
Improved adherence/compliance to treatment and
monitoring e.g. CPAP for OSA
Analy&cs for pa&ents/healthcare providers / popula&on
healthcare managers
Ac&onable
Events
Streaming Engine
Data Reservoir
Enterprise Data & Repor&ng
Discovery Lab
Ac&onable
Informa&on
Ac&onable
Data Sets
Input
Events
Execu&on
Innova&on
Discovery
Output
Data
Structured
Enterprise
Data
Oracle Approach -‐ Conceptual View
Real-‐Time Analy&cs -‐ Event Processing with Spa&al /
Loca&on / Geofencing
Analyze, Visualize and Mone&ze Automo&ve Big Data
Example Automo&ve Dataset from
Telema&cs Data:
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Number of trips recorded
•
Number of kilometers logged
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Travel by Loca&on
•
Petrol consump&on by model
•
Diagnos&c Trouble Codes by model
•
Driver Aggression Profile (e.g. RPM Profile)
•
Distance by model / holiday travel
Traffic Palern Visualiza&on
Smart Metering Analy&cs -‐ Collect, Analyze and Act to
Increase Efficiency / Reduce Cost
Monthly register reads don’t easily
reveal slowing consump&on…
Daily data reveals there is a trend, but
is it unusual, or weather driven?
(Yellow = temperature)
Comparison to rate class behaviour
(Red – rate class aggregate) reveals
that the palern is specific to the meter
Inclusion of meter flag / event data
seals the deal: meter is highly likely to
be failing
Smart City -‐ Parking Data: Collect, Analyze, Act and Expose as a
Service
SFpark: Putting Theory Into Practice / 89
Lessons learned
Creating the technical infrastructure for SFpark’s data needs has been a large undertaking. The following lessons have emerged thus far:
tDon’t do it yourself. Most internal IT organizations do not maintain the staffing levels or skill sets to implement the technology necessary for a SFpark-style program. Bring on an experienced team to build the technical infrastructure and integrate it with existing systems.
tMake sure your technology implementation team is involved in the first stages of the project management life cycle, beginning with contracting and procurement, long before it comes time to purchase servers. Have that team work with your existing IT team to ensure that technology choices fit in with your organization’s existing IT standards and direction.
tDon’t let product vendors (sensors and meters) determine the technical infrastructure. Create a data system that can interface with multiple vendors and will provide maximum control over how the data is managed and turned into information. Insist that project plans be expressed in terms of business deliverables. Vendors will want to give you a construction plan, but you want a feature implementation plan.
tExpect to spend more time in requirements discovery, business process reengineering, and off-plan work than expected. None of the vendors has ever done this type of project before, so workarounds and detours are commonplace.
tThe technological maturity of vendor products is much less than was anticipated.
tMost vendors do not have mature software
development, testing, and change control procedures.
Business intelligence tool automated report example
94/ Ch. 5: Parking technology
Mobile applications
SFpark provides on- and off-street parking availability and rate information via an iPhone app and soon an Android app.
Screen shots of iPhone app
