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22 Non Statistical Questions for a

Statistician

1. Which business outcome are we attempting to model and predict?

• Mean time between failure of asset – MTBF ?

• Next best action- NBA ?

2.What surgical actions can we drive once we are able to predict the outcome?

• Preventive replacement of asset

• Stock up on spares

3. What is the quantifiable impact of these actions?

• Down time reduced by 6 %

• MTBF increased by 12 days

• Reduced HSE incidents by 18 %

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 $ ______________

5. What is the economic impact of a wrong prediction (false positives)?

• $ spent into replacing a healthy asset

6. What is the non-economic impact of a wrong prediction ?

• Decreased customer satisfaction Index

7. Is not predicting outcome a bearablebusiness option?

• In some rare scenarios status quo is still an option

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

9. Is there enough breadth available in data to explain the predicted behaviour?

• Certain asset attributes like tenure may not be available

10. Is there enough depth available in the data we are using?

• 2 years ?

• 3 years ?

• 4 years ?

11. Are there causal blind spots in data?

• Ambient causal context?

• Human causal context ?

• Machine causal context?

12. Is the past representative of the future?

• A new design may invalidate historical data of an asset

13. Are we modelling black swan events?

• Black swan events are rare difficult to predict event

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 ?

15. Was the modelling an armchair exerciseor did the modeller soak in the biz process?

• Modellers who are intimate contact with field can model assets behaviour better

16. Are we focussing only on signals which reinforce our world view? • Typically people have Cognitive bias

17. Which real world behaviours are encoded in vectors?

• Volatility dimension ?

• Velocity dimension ?

• Dispersions ?

• Ranks ?

18. Does the statistical model articulate a range of possible business outcomes?

• Best case scenario MTBF = 768 days

• Worst case MTBF = 628 days

19. Does the statistical model articulate the realistic outcome?

• Realistic MTBF = 680 days

20. Are their weak signals if triangulatedwhich could become a strong signal?• Combining vibrations +

• Experience of maintenance engineer +

• Asset age

21. Are we mistaking correlation for causality?

• Vibration frequency is co-relation

• Whereas maintenance engineer could be causal

22. Have we polled multiple models to see if 2 models reinforce the same outcome ?

• Combinatorial Ensembles

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 Silicon Valley. 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

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Nothing delights us more than winning your

Trust!!!

Derick Jose

Email: derick.jose@flutura.com