IIoT : Old Wine in a New Bottle?
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slides from a Predix-IIoT meetup talk
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IIoT : Old Wine in a New Bottle?
- 1. Predictive Analytics : Why (I)IoT is different ! Venu Vasudevan, PhD! ! Next.io (Consultant IoT | Big Data)! Adjunct Professor, ECE, Rice U.! ! venuv62@gmail.com! @venuv62!
- 2. Me Intrapreneur. balanced diet of IoT & predictive analytics ๏ IIoT for asset management. Key contributions to Zigbee! ๏ Shazam for IoT - IoT accessory Home/Auto! ๏ Iridium predictive fault management! 1Mill measurands/sec. then satellite ~ now thermostat! ๏ Predictive video analytics (acquired by WatchWith)!
- 3. Agenda: Predictive & IIoT • Why in the limelight?! • Now. is it new-and-unique or sum-of-parts! • Next. will it be new-and-unique or sum-of-parts!
- 4. IIoT Market Potential $150B addressable market! by 2020! Low(er) business friction! - IIoT Technology creators ! are also customers!
- 5. Predictive ability : Mandatory, not optional over-doing! processes! expensive! under-doing! processes! catastrophic! rightsizing a! dynamic, predictive process! (time | business context)! e.g. too much ‘routine’ ! maintenance. lightly used ! equipment! e.g. not enough! maintenance. ! high risk equipment! Business Focus : from reliability to optimization
- 6. Predictive Analytics : IoT Challenge Sources. ParStream,IBM IoT surveys! SpottyData!‘Good’predictions!
- 7. Predictive Analytics : IoT Challenge + Opportunity high quality,! high velocity! predictions! with incomplete, untidy data! Source. Keystone Strategy! SpottyData!‘Good’predictions! long runway for predictive!
- 8. Challenge : Data-Insight Gap • There is no ‘free lunch’ : better predictions need more data! • Ways to narrow the gap! • (Volume, Velocity) faster, fatter path from data to decisioning! • (Variability) clever ways to clean data at scale! • Match best algorithm for the data at hand! data maturity! insight! insight ! aspiration! data ! reality! variability!volume! velocity! The ‘gap’ is not unique to IIoT. The reasons for it are ..!
- 9. IIoT vs Consumer Web : Same gap, different reasons Consumer IIoT Capture Hard! (consumers don’t cooperate)! Easy! (‘things’ always cooperate - for a price)! Sanitization Medium! (simpler data types)! Hard! (gnarlier data types)! Modeling & Integration Easy! (e.g. eyeballs, dwell time)! Hard! (complex data models)!
- 10. IIoT+Predictive:more than sum of parts? IoT! Predictive! Analytics! retrospective! descriptive! prescriptive!predictive! What’s the current IIoT+Predictive architecture?! Does it address the data-insight gap?! What architectural changes would close the gap?! depth of insight! scale!
- 11. Now : Cloud-Centric (I)IoT architecture collect! learn! act! sense! store.query.! analyze.predict! automated | human! capture.filter.! cloudedge scale scale
- 12. Next : Edge-heavy IIoT architecture collect! learn! act! sense! store.query.! analyze.predict! automated | human! capture.filter.! edge edge cloud responsiveness scale
- 13. Sensing Data Challenge Option1. data goes to decisioning ! Fatter, faster pipes! Continuous flow! Option 2. decisioning goes to data ! Intelligent Edge ! Periodic updates! sense! getting data and decisioning together!
- 14. Edges make IIoT Faster GE Blog - Edge: A Door to the Data Kingdom! ➡ Edges distribute predictive services (cloud vs edge)! ➡ policy vs behavior ! ➡ long-term vs real-time ! ➡ architectures for flexible (re)distribution of predictive decision logic?!
- 15. Edges make IIoT Faster and Cheaper ➡ Edges distribute predictive services (cloud vs edge)! ➡ policy vs behavior ! ➡ long-term vs real-time! ➡ how will predictive decision logic move to where the data is?! Jasper. The hidden costs of delivering IoT!
- 16. Slow lakes to fast streams • Now. Transition from data lakes to data streams! ‣ 30-100x speed up : streams over lakes! ‣ needed to deal with real- time IIoT traffic! ‣ lambda architectures balance prediction speed and accuracy! • Next ….! untidy data firehose clean analytics fast & good slower & much better Lambda architecture collect! Hadoop! Spark!
- 17. Edge Filtering : Slimming diet for fat streams fitting predictive decisioning logic fit in super-small footprints!
- 18. Opportunity : Machine Learning at unprecedented scale • Machine-learning-as-a-service - rich set of algorithms, solution templates - immediate impact in: ! • problems with established procedures! • and clean data! Source. Cortana Intelligence Gallery learn!
- 19. Challenge : Clean Data • State-of-the-art ML — promises dramatic improvement. But ‘clean data’ hungry! • Deep Learning 3x better than Regression for electricity demand forecasting! • needs 1.5 million data points for training (over 4.5 years)! • Limiting factor is the data quality ! data maturity! insight! insight ! aspiration! data ! reality! variability!volume! veracity! Stanford study. Electricity demand forecasting. Deep learning 3x better than ‘classic’ m/c learning!
- 20. Challenge : Clean Data • State-of-the-art ML — promises dramatic improvement. But ‘clean data’ hungry! • Deep Learning 3x better than Regression for electricity demand forecasting! • needs 1.5 million data points for training! • Limiting factor is the data quality! Source. HP Enterprise Labs study! Training Data Training Time (IoT) signals 3 million frames! days! Vision 14 million images! 3 days w/ 16000 cores!
- 21. 2-Tiered Machine Learning for IIoT • Intelligent IoT data cleansing layer (e.g. Bitstew) - Machine Learning turns dirty data into clean data! • low-level data cleaning pushed to the edge! • semantic integration between data sources in the cloud! • Predictive Layer - Machine Learning turns clean data into clean insights! interfaces between cleansing & prediction? !
- 22. Conclusion Present : Cloudy • embrace. leverage cutting edge cloud and ML services! • extend. adapt to IIoT business processes! Future : Edgy • hyper decentralized intelligence and data! • systems that understand ‘normal’ and ‘deviation’! • predictive systems that have both response velocity and depth of insight!
- 24. Predictive Analytics : IoT Challenge good enough,! high velocity! predictions! with incomplete, untidy data! (hourglass - with decay statistic)! Source. Par stream IoT survey!
- 25. Challenge : Clean Data • State-of-the-art ML — promises dramatic improvement. But ‘clean data’ hungry! • Deep Learning 3x better than Regression for electricity demand forecasting! • needs 1.5 million data points for training (over 4.5 years)! • Limiting factor is the data quality ! Stanford study. Electricity demand forecasting. Deep learning 3x better than ‘classic’ m/c learning! Fast Accurate Clear Naive Bayes Yes! Low! Somewhat! Regression Yes! Medium! Yes! Decision Trees Yes! Medium! Somewhat! Deep Learning No! High! Heck no!