How to bridge the gap between Statisticans and Business folks ? Flutura outlines 22 non statistical questions for a statistician which can lubricate the friction
Flutura has always believed that when the world of business collides with the world of math, magic unfolds. As these 2 worlds collide, it also presents a set of unique challenges - bridging the semantic language gap between business and math. Modelling complex business outcomes using math requires an interdisciplinary team consisting of business folks, data folks and math folks. While doing so business folks are always at a loss because a language chasm exists. Math folks love their ”geek speak” ( Tanimoto coefficient, chi square, odds ratio) and business folks are focussed on impactful outcomes (Mean time between failure, Next best action etc.).
In a world which is going to be taken over by machines "karma outsourced" through the IOT revolution, algorithms would play an important role. These are some of the key learnings Flutura has had in developing data products for industrial IOT.
Lean six sigma in the age of AI
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An introductory slide for people who are just getting into the field of Data Science to gain the understanding of why data science is important and how data scientist fits in the loop of the importance of Data Science to the industry
Learn how artificial intelligence (AI) and machine learning are revolutionizing industries — this course will introduce key concepts and illustrate the role of machine learning, data science techniques, and AI through examples and case studies from the investment industry. The presentation uses simple mathematics and basic statistics to provide an intuitive understanding of machine learning, as used by firms, to augment traditional decision making.
https://quforindia.splashthat.com/
To cope with privacy laws, big data players face a new need: synthetic data.
At Real Impact Analytics, we specialize in modelling telecom data. Here is how we created our own synthetic data generator.
website realimpactanalytics.com
email info@realimpactanalytics.com
In a world which is going to be taken over by machines "karma outsourced" through the IOT revolution, algorithms would play an important role. These are some of the key learnings Flutura has had in developing data products for industrial IOT.
Lean six sigma in the age of AI
What is six-sigma?
Lean Six-Sigma In The Age Of A
THE BRAIN: INSPIRATION FOR THE NEURAL NETWORK AND DEEP LEARNING
AI and LSS
NEURAL NETWORK
The Ten Lean Six Sigma Metrics
An introductory slide for people who are just getting into the field of Data Science to gain the understanding of why data science is important and how data scientist fits in the loop of the importance of Data Science to the industry
Learn how artificial intelligence (AI) and machine learning are revolutionizing industries — this course will introduce key concepts and illustrate the role of machine learning, data science techniques, and AI through examples and case studies from the investment industry. The presentation uses simple mathematics and basic statistics to provide an intuitive understanding of machine learning, as used by firms, to augment traditional decision making.
https://quforindia.splashthat.com/
To cope with privacy laws, big data players face a new need: synthetic data.
At Real Impact Analytics, we specialize in modelling telecom data. Here is how we created our own synthetic data generator.
website realimpactanalytics.com
email info@realimpactanalytics.com
Copy of presentation delivered at the CHASS 2015 National Forum in Melbourne (October 2015), The Council for Humanities, Arts and Social Sciences in Australia is the peak body supporting more than 75 member organisations in their relationships with Federal and State Government policy makers, Academia and the broader community within Australia.
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ORBiT Annual Members' Meeting, Nov 11th 2014
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How to bridge the gap between Statisticans and Business folks ? Flutura outlines 22 non statistical questions for a statistician which can lubricate the friction
2. 1.Which business outcome are we
attempting to model and predict?
• Mean time between failure of asset – MTBF ?
• Next best action- NBA ?
3. 2.What surgical actions can we drive
once we are able to predict the outcome?
• Preventive replacement of asset
• Stock up on spares
4. 3.What is the quantifiable impact of
these actions?
• Down time reduced by 6 %
• MTBF increased by 12 days
• Reduced HSE incidents by 18 %
5. 4.What is the economic impact of a
correct prediction?
• Cost of following operational metrics translated in $
• Down time reduced by 6 % means $ _____________
• MTBF increased by 12 days means $ ______________
• Reduced HSE incidents by 18 % means $ ______________
6. 5.What is the economic impact of a wrong
prediction (false positives)?
• $ spent into replacing a healthy asset
7. 6.What is the non-economic impact of
a wrong prediction ?
• Decreased customer satisfaction Index
8. 7. Is not predicting outcome a bearable
business option?
• In some rare scenarios status quo is still an option
9. 8. Is the business phenomena we are trying to
predict modellable using the data we have?
• Ambient temperature may not be instrumented to model downtime
10. 9. Is there enough breadth available in data
to explain the predicted behaviour?
• Certain asset attributes like tenure may not be available
11. 10. Is there enough depth available in the
data we are using?
• 2 years ?
• 3 years ?
• 4 years ?
12. 11.Are there causal blind spots in data?
• Ambient causal context?
• Human causal context ?
• Machine causal context?
13. 12. Is the past representative of the future?
• A new design may invalidate historical data of an asset
14. 13.Are we modelling black swan events?
• Black swan events are rare difficult to predict event
15. 14. Do rhythms and patterns exist in
historical data which is correlated to outcome?
• Are their frequent sequences of event prior to asset breaking down ?
16. 15.Was the modelling an armchair exercise
or did the modeller soak in the biz process?
• Modellers who are intimate contact with field can model assets behaviour better
17. 16.Are we focussing only on signals which
reinforce our world view?
• Typically people have Cognitive bias
18. 17.Which real world behaviours are
encoded in vectors?
• Volatility dimension ?
• Velocity dimension ?
• Dispersions ?
• Ranks ?
19. 18. Does the statistical model articulate a
range of possible business outcomes?
• Best case scenario MTBF = 768 days
• Worst case MTBF = 628 days
20. 19. Does the statistical model articulate the
realistic outcome?
• Realistic MTBF = 680 days
21. 20.Are their weak signals if triangulated
which could become a strong signal?
• Combining vibrations +
• Experience of maintenance engineer +
• Asset age
22. 21.Are we mistaking correlation for
causality?
• Vibration frequency is co-relation
• Whereas maintenance engineer could be causal
23. 22. Have we polled multiple models to see
if 2 models reinforce the same outcome ?
• Combinatorial Ensembles
24. About Flutura
Flutura is changing the way companies(Utility, Digital oil fields & Asset intensive industries ) use data to
transform business outcomes.We do this using Cerebra a sensor data platform. Flutura is funded
by The Hive - A pure play big data fund based in SiliconValley. Flutura is head quartered in Palo Alto
and has offices in Houston and Bangalore.We have been featured by Gigaom and CIO Magazine as
the top 20 Global big data startups. .Flutura has a patent pending IP framework to detect signals in
machine data.
• Website : www.Flutura.com
• Blog : blog.fluturasolutions.com
• Twitter : @fluturads
• Linkedin : https://www.linkedin.com/company/flutura
• Slideshare : http://www.slideshare.net/fluturads
25. 25
Nothing delights us more than winning your
Trust!!!
Derick Jose
Email: derick.jose@flutura.com
