We live in a data-rich world - almost everything we do is being captured and stored somewhere. There are algorithms crunching the data every millisecond and conveying unknown and untapped information. At an enterprise level, data analytics provides us a 360-degree view of our customers, products and the business landscape to make effective, smart decisions. This presentation delves into how the traditional business philosophy of ‘proximity to customer’ will lose its significance and how data will drive product decisions.
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Unlocking the Power of Data: Data Driven Product Engineering, Evren Eryurek, CTO, GE Healthcare
1. Unlocking the power of Data:
Data Driven Product Engineering
Building Technology Organizations of Tomorrow
Evren Eryurek, PhD
GEHC Software CTO
MARCH 2015
3. 3
Industrial Internet
What Happened When
1B People
Became Connected?
What Happens When
50B Machines
Become 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. 4
A convergence of enabling technologies is
setting 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
4
Internet
of Things
1
Intelligent
Machines
2
Big 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-service
models drives data collect and
analysis
6. 6
Ingredients of Modernization
Optimizing SW portfolio to
maximize customer success
User
Experience
Data
Science
Advanced
Research
Commercial
Strategy
Cloud
Services
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. 8
What is Big Data? And how to take advantage of it?
Volume
Data Quantity
Variety
Data Types
Velocity
Data Speed
Value
Data Impact
9. 9
Industrial big data – fast and vast
*Source: IDC
50BMachines will be
connected on the
internet by 2020
2XIndustrial data
growth within
next 10 years
*Source: IDC
CRM, ERP,
etc. Logs
Social network
data
Geo-location
data
9MM
Data points
per hour for each
locomotive
500GB
Data per blade
by gas
turbines
Sensor
data
Content
(images, videos,
manuals, etc.)
Historian
data
Machine
data
35GB
Data per day
from each
Smart Meter
50X
Data growth
in healthcare
(2012 – 2020)
1TB
Data per
flight
In practice only
3%of potentially useful
data is tagged
and even less
is analyzed*
10. 10
Intelligent Hospital
Customer challenges
Diagnostic quality
Patient-centric care
System profitability
Chronic Disease Management
29%
Healthcare spend wasted
each year
$260B
Annual 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. 12
GE Machine Learning in Action
Smart 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. 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. 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 everything
A standard way to develop & deploy machine software
Slide 4
Analytics
Business Analytics Market to Reach $50.7B by 2016 – (source http://www.eweek.com/c/a/Enterprise-Applications/Business-Analytic-Market-to-Reach-507B-by-2016-on-Big-Data-Hype-IDC-179369/)
15.2 percent year-over-year
OT Security
GE - GE is the largest - at $110 billion in revenue and counting (source – IT OT Convergence blog)
Lifecycles are measured in years - at an average of 18 years (same source as above)
Good news is we are getting new players which we can deploy…
Our new players are enabling technologies…transforming our industry.
Internet of things:
Hyper-connectivity: a living network of people machines and data
More devices tap into Internet than people on Earth to use them
Intelligent machines:
Increasing system intelligence through embedded software
Rise of machines: networkeddevices overtook the global
Big Data:
Common types of physiologic monitoring systems include the following:
Bedside physiologic monitors with various physiologic parameters, including the following: Electrocardiogram (ECG) – Pulse oximetry – Blood pressure
Telemetry monitors
Central station monitors
Home health sensors,
Patient quantified-self monitoring
Analytics:
Generating data-driven insights
Enhancing performance by detecting & predicting forecasts
Algorithms on IB
Big Data is defined as a function of 4 key components:
Volume represents Datasets that are Large in terms of size of the data (MegabytesGigabytesTerabytesPetabytes Exabyte ZetabyteYottabytes Brontobytes)
For example, in 2000years the world generated two Exabytes of new information. That is 2 x 1018 bytes of information. We now generate that much data in one day! It has been said, 5 Exabytes may be equal to all of the words ever spoken by mankind. Simply put, the volume of data is increasing.
Variable: Different types of data (EHR, Imaging, pharma, labs, etc.) which are often locked in silos. Big Data initiatives require making this data fluid.
Velocity: Rate at which data is generated from different sources (devices, patients, claims, etc.). The pace is accelerating as devices get smarter, patients take control of their well being in this connected world
Value: Data alone is not beneficial. Converting this data to insights that can affect action is where the value comes from. Big Data analytics enables the process of analyzing the data from multiple sources to derive at some insights which can then be used to affect some actions to improve outcomes resulting in value to the enterprise.
Pipeline “picture” with key lines coming out of it – talk to each bullet
Keep this printed out
Some diagram of Jeff’s triangle value drivers / behavior changes / Intelligent Pipeline functionality – this is what I’m doing differently
Ascertain the Condition of Each Segment:
The Digitized Asset Register and Closed Loop Work Order in near real-time will improve speed to close of repairs and as-builts, integrate ILI findings faster and improve accuracy.
Validate Asset Data:
The Digitized Asset Register and Data Validation Score will increase speed and accuracy of validation due to a single consistent data set and faster access to records and ILI findings.
Prioritize Replacement / Repair Sequencing and Determination of Required Remediation Materials:
The Digitized Asset Register, Risk Analytics and Closed Loop Work Order will improve risk assessment speed, frequency and accuracy with an on-demand, more comprehensive and granular data set of atypical risk inputs, non-typical risk exposure / coincident feature analytics, awareness of asset, materials and work locations and near real-time updated records to enable prioritization.
Create Dig Sheets:
The Digitized Asset Register, Closed Loop Work Order and Data Validation Score will expedite dig sheet creation due to electronic access of data (minimal manual data collection), faster close of construction/repair records and knowledge of data accuracy on the platform.
Integrity Validation Plan for Non-Piggable Data:
The Digitized Asset Register and Risk Analytics will improve accuracy by using a more comprehensive and granular asset register coupled with typical and atypical risk inputs allowing for an enhanced risk view of the asset and prioritizing risk by segment on a daily basis.
Determine MAOP:
The Digitized Asset Register and Data Validation Score will allow for faster and more accurate calculation due to near real-time integration of all asset condition and risk weighted data validation.
Break-in Work (exposed pipe/soil slippage, etc.) Analysis and Remediation:
The Digitized Asset Register and Data Validation Score will improve speed and accuracy due to minimizing manual data collection, ease of access to validated asset and parts data needed for repairs in a single location and coordination of operations and maintenance business units.
14
BNSF:
Biggest customer, hit hardest by volume increase and velocity reduction
Tried to sell Movement Planner in ‘06 and ‘10 – lost
Today: winning…what’s different?
Yesterday: specs and point solutions
Today: outcomes and systems solution (rOS)
Now…taking this winning formula to all of our customers:
NS:
Bid on automotive yard lost and forgotten,
Four months later, in SR showing vision and roadmap (rOS) and investment stream behind it
Next day – phone call “why aren’t you doing this with us”…$20M
UP:
Toughest customer – worst customer relationship
Low support for removing labor
Brought to San Ramon – showed vision, integration solution (rOS) and investment stream behind it
Senior VP pulled me aside, “similar challenge (Energy Mgmt example) – we have 19 wind blow-overs in last 5 years – velocity goes to zero”
90 Day Fast Works project
Now biggest customer advocate – replace labor with data & analytics capability
CSX:
New MSA with RailConnect Technology partnership as part of deal for co-development and to deliver outcome of productivity
They had been working with world-class robotics company to do dull/dirty/dangerous yard work
Asked us to bid – we proposed an integration systems solution: software, end of arm affectors, vision and maintenance
GE was unanimous choice – opening up a new market
CONCLUSION:
We already had all the building blocks for success
It is starting with an outcome, and pulling the blocks together with an integrated solution that unlocked all of these deals
Changing the face of our service business
This is Services 2.0