Unlocking the power of Data:
Data Driven Product EngineeringBuilding Technology Organizations of Tomorrow
Evren Eryurek, PhD
GEHC Software CTO
MARCH 2015
2
Technology center of gravity is shifting
Old IT
Stack
Analytics
• Business Analytics Market to
reach $50.7B
by 2016
• 15.2% year-over-year
• Market $7B devices
• Mobile app development
Mobile
• Market $13 trillion over
15 yrs
• Consumer Grade
• NoSQL, Hadoop movement
• Industrial-strength
Industrial
Internet ][
• Market $9B growing at CAGR 12%
• Embedded in smart devices
Intelligent
Sensors
New
Platform
• Lifecycles are measured in years - at an average of 18 years
• Points of integration between IT & OT
OT
Security
• Market $11B in size and growing at
33%
• Volume, Velocity, Variety, Veracity
Big
Data
• Market $24B in size and growing
at 23%
• Private and hybrid
Cloud
Computing
© General Electric Company, 2014. All Rights Reserved.
3
Industrial Internet
What Happened When
1B People Became Connected?
What Happens When
50B MachinesBecome Connected?
Operating Time is Virtualized
Analytics Become Predictive
Machines Self-Heal with Automation
Monitoring & Maintenance is Mobilized
Productivity/Decision-making Increase
Enables dramatic improvements in outcomes by combining analytics with new
forms of collaboration above isolated machines, workflows and data
Entertainment is Digitized
Social Marketing Emerged
Communications Mobilized
IT Architecture Virtualized
Retail & Ad Transformed
4
A convergence of enabling technologies issetting the stage for industry transformation
1 $27B by 2017 for Mobile health services:
The market for mHealth services has now entered the commercialization phase and will reach $26 billion globally by 2017 according to new “Global Mobile Health Market Report 2013-2017” by
research2guidance. The report is one of the leading publications in the mHealth market. Companies that have purchased previous editions of the report includes: Agfa Healthcare, DTAG, Fresenius,
Fujitso, GE Healthcare, LG, Nokia, Novartis, Pfizer, Qualcomm, Roche, Roland Berger, Sanofi Aventis and many more.
Analytics
4Internetof Things
1IntelligentMachines
2Big Data
3
“Hospital of Things” plethora of devices
Accelerating Bio-sensor market/use
Mobile healthcare explosion –$27B by 20171
Machines protecting and treating patients
Devices for new care givers and settings
Algorithms as updatable content
High volume of data from physiology monitoring
Care shift from population median to high-def
individual
Forecasting and predicting future health
End of fee-for-servicemodels drives data collect and
analysis
6
Ingredients of Modernization
Optimizing SW portfolio to maximize customer success
UserExperience
DataScience
AdvancedResearch
CommercialStrategy
CloudServices
Architecture
New business, operations and technology models
Promoting rapid integration of new research into solutions
Unifying service-based SW on protected automated
environ
Persona and context driven for increased adoption
Automated DevOps environ with Scaled Agile processes
Descriptive, predictive, and prescriptive analytics
Development
Security strategies to prevent, detect and address risks
Cyber Security
8
What is Big Data? And how to take advantage of it?
VolumeData Quantity
VarietyData Types
VelocityData Speed
ValueData Impact
9
Industrial big data – fast and vast
*Source: IDC
50BMachines will beconnected on theinternet by 2020
2XIndustrial datagrowth withinnext 10 years
*Source: IDC
CRM, ERP,etc. Logs
Social networkdata
Geo-locationdata
9MMData points
per hour for eachlocomotive
500GBData per blade
by gasturbines
Sensordata
Content(images, videos,manuals, etc.)
Historiandata
Machinedata
35GBData per day
from eachSmart Meter
50XData growthin healthcare(2012 – 2020)
1TBData per
flight
In practice only
3%of potentially useful
data is taggedand even lessis analyzed*
10
Intelligent Hospital
Customer challenges
Diagnostic quality
Patient-centric care
System profitability
Chronic Disease Management
29%Healthcare spend wasted
each year
$260BAnnual value creation
through healthcare IT
59%US lives covered in value-
based care model by 2015
Clinical
Quality Financial Performance
Operational Efficiency
Configurable
Workflows
12
GE Machine Learning in ActionSmart Reading Protocols
Data Snapshot
Info Fusion
Text Mining
Inference Engine
The Challenge
• Extremely complex &
error prone to configure
what images to display
where for radiologist
interpretation
• Hospitals spending $$$
in lost productivity on
non-value-add work
• Entire industry
struggling with this for
20 years
The Outcome
• 50% time savings
for exam
preparation
• Robustness &
accuracy
• Ease of use
• Ease of
maintenance
The Process
13
O&G Example: The Intelligent pipeline
Efficient dig &
excavation activities
Enhanced, digital
assessment for
pipelines
More complete and
near real-time MAOP
Automated creation
of dig sheets
Data-driven prioritization of repairs /
replacements
More accurate
validation of asset
data
Faster condition
assessment & closure
Delivering Safe & Efficient Outcomes in Oil & Gas
14
GE’s SDMs are brilliant machines
1. More uptime, due to ‘hot’ software
upgrades
5. Resiliency and efficiency, with standard way to develop and
deploy machine apps
3. Unlimited compute, with standard distributed architecture
from edge to cloud
2. Automated software updates, without change in
hardware
4. Interoperable machines, with standard interfaces that apply
across machines
Aviation Example: Software defined everythingA standard way to develop & deploy machine software
15
Transportation Example…
CSX – Productivity
Velocity NS – Dwell
UP – Safety
Dwell
ProductivitySafety
16
Time is now
DataTime Series
Multimodal
Interaction based
AnalyticsStatistics & machine learning based
Physics-based
SensorsOrder magnitude growth per machine every 5 yrs
Video most underutilized sensor
ConnectivityField force automation
Autonomous system
ApplicationsAsset optimization
Operations optimization
Data
Machines
Analytics
© General Electric Company, 2014. All Rights Reserved.
17
AT GEWE PUT OUR
IDEAS TO WORK
TAKING THEM OFF THE PAPER
OUT OF THE LAB AND INTO
THE WORLDENGINEERS SCIENTISTS TEACHERS LEADERS AND DOERS
ALL SHARING A BELIEF
THAT THINGS CAN BE MADE
TO WORK BETTERIT’S WHY WE GET UP IN THE MORNING
IT’S WHY WE COME TO WORK
EVERY DAY
TO BUILD CURE POWER
AND MOVE THE WORLD
WE ARE AT WORK
MAKING THE WORLD
WORK BETTER
© General Electric Company, 2014. All Rights Reserved.