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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.](https://image.slidesharecdn.com/unlockingthepowerofdata-150317010917-conversion-gate01/75/Unlocking-the-Power-of-Data-Data-Driven-Product-Engineering-Evren-Eryurek-CTO-GE-Healthcare-2-2048.jpg)
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Unlocking the Power of Data: Data Driven Product Engineering, Evren Eryurek, CTO, GE Healthcare
The document outlines the rapid evolution and market growth of data-driven technologies, highlighting a significant shift in product engineering and analytics within technology organizations. Key trends include the rise of mobile health services, the Internet of Things (IoT), big data, and the need for software modernization to leverage these advancements effectively. The text stresses the potential of intelligent machines and analytics to enhance productivity and decision-making across various industries, particularly healthcare and industrial applications.
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Unlocking the Power of Data: Data Driven Product Engineering, Evren Eryurek, CTO, GE Healthcare
- 1. Unlocking the powerof Data: Data Driven Product Engineering Building Technology Organizations of Tomorrow Evren Eryurek, PhD GEHC Software CTO MARCH 2015
- 2. 2 Technology center ofgravity 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. 3 Industrial Internet What HappenedWhen 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 ofenabling 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
- 5.
- 6. 6 Ingredients of Modernization OptimizingSW 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
- 7. 7 GE Approach tothe Industrial Internet
- 8. 8 What is BigData? 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 Diagnosticquality 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
- 11. 11 Radiology Example: Readingprotocols
- 12. 12 GE Machine Learningin 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: TheIntelligent 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 arebrilliant 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
- 15. 15 Transportation Example… CSX –Productivity Velocity NS – Dwell UP – Safety Dwell ProductivitySafety
- 16. 16 Time is now Data TimeSeries Multimodal Interaction based Analytics Statistics & machine learning based Physics-based Sensors Order magnitude growth per machine every 5 yrs Video most underutilized sensor Connectivity Field force automation Autonomous system Applications Asset optimization Operations optimization Data Machines Analytics © General Electric Company, 2014. All Rights Reserved.
- 17. 17 AT GE WE PUTOUR IDEAS TO WORK TAKING THEM OFF THE PAPER OUT OF THE LAB AND INTO THE WORLD ENGINEERS SCIENTISTS TEACHERS LEADERS AND DOERS ALL SHARING A BELIEF THAT THINGS CAN BE MADE TO WORK BETTER IT’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.
- 18.
Editor's Notes
- #3 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)
- #5 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: networked devices 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
- #9 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.
- #14 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.
- #15 14
- #16 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