The document lists 22 non-statistical questions for a statistician to consider when building a predictive model, including questions about the desired business outcomes, economic and non-economic impacts of correct and incorrect predictions, representativeness of past data for future predictions, and ensuring the model accounts for multiple possible outcomes rather than just reinforcing preexisting views. It concludes by providing brief information about Flutura, a company that uses data and predictive modeling to help asset-intensive industries improve business outcomes.

1. 1
22 Non Statistical
Questions for a
Statistician

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