Monetizing the Math – are you ready?

comments

By Edward Vandenberg, Industry Consultant at Teradata.

With over 15 years of experience in building and launching AI/ML applications for large organizations, and plenty of giant failures, I offer this guidance to you who may have just created the coolest, most awesome model anyone could ever build. Congratulations are in order, just not yet.

My checklist of the really hard things:

1. Do you have the infrastructure to deploy, maintain and refresh models in short cycles?
2. Do you have the control mechanisms and resources to monitor, maintain and report on the model (and correct defects promptly)?
3. Can your application be maintained without eating resources that are not committed to you?
4. Does your app have a compelling name that the CEO has already mentioned to his direct reports...in a good way (this is really important)?
5. Are your algorithms so simple and transparent that no-one could possibly mis-interpret them or screw them up after you and your team have moved on?
6. Are your training data sets in a place where someone will remember?
7. Have you produced a confusion matrix that you are ready to live with and explain?
8. Does your algorithm also explain the ‘why’ of its prediction or recommendation? (I already know the answer to this so let this one be aspirational).
9. Have you made provisions for when IT rebuilds the transaction system or data warehouse from where you get your run-time data?
10. Have you made provisions for when your external data vendor increases their price, changes their endpoints or goes out of business?
11. Do you have active visible executive sponsorship over IT and Change Management?
12. Have you created and communicated widely a scope and release plan for all of the programmatic components (larger than your dev/deploy project alone)?
13. Have you thought through the operational impact and have the full cooperation of line level managers?
14. Have you shared the story with end users early enough to give them ownership in the app?
15. Have you agreed with Finance on how the ‘monetize’ part will be measured?
16. Have you fully considered impact to customers?
17. Are your ethics and data privacy concerning both customers and employees beyond any doubt or skeptical judgement?
18. Are you ready to speak clearly all along the way, defend yourself technically and operationally to skeptics (and the occasional enemy), address the fears, doubts and uncertainties (FUD) of your stakeholders and keep your team from being demoralized?
19. Have you trained your successor to do the same?
20. Are you ready for the very long time it may take, when the original problem seems to be obscured by new problems?
21. Is organizational adoption strong enough that the app will have a life after the current executive sponsor has moved on?

This is a long and beefy list. If you have checked all of these boxes, congratulations! You can now call yourself both a data scientist and a leader of the ‘Monetizing the Math’ project. Stay humble!

Bio: Edward Vandenberg has over 25 years of experience in the property & casualty insurance industry. For over 15 years, Ted has focused on helping insurers build and deploy advanced analytics models into their core operations, holding practitioner, management and business development roles in consulting firms and insurance companies. Since 2012, he has focused on the design of applications for artificial intelligence for claims processing

Original. Reposted with permission.

Resources:

• On-line and web-based: Analytics, Data Mining, Data Science, Machine Learning education
• Software for Analytics, Data Science, Data Mining, and Machine Learning

Related:

• Math for Programmers
• Math for Machine Learning
• A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more