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Backward Engineering: Plan Machine Learning Deployment in Reverse

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Predictive Analytics World 2019

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Backward Engineering: Plan Machine Learning Deployment in Reverse

  1. 1. @jamet123 #decisionmgt © 2019 Decision Management Solutions James Taylor CEOBackwards Engineering: Plan Machine Learning Deployment in Reverse
  2. 2. @jamet123 #decisionmgt © 2019 Decision Management Solutions 2 Typical Approach To Predictive Analytics and ML Assemble Data Do Analysis Score Data Improve Results ?
  3. 3. @jamet123 #decisionmgt © 2019 Decision Management Solutions 3 This Approach Isn’t Working Very Well ▪ Studies show that many organizations are not getting great results this way. ▪ 70% of organizations say analytics is really important but only 2% have delivered on the promise. ▪ There are far more companies failing to deliver high impact (80%) than succeeding in generating impact. Broken links: Why analytics investments have yet to pay out” ZS and the Economist Information Unit, Ltd. June 2019 Raising returns on analytics investments in insurance” by McKinsey “Analytics efforts is very/extremely important” 70% of organizations say …but only 2% have delivered on the promise. 80% of organizations are failing to have a broad impact with their analytic efforts.
  4. 4. @jamet123 #decisionmgt © 2019 Decision Management Solutions 4 Because the Last Mile is Hard Broken links: Why analytics investments have yet to pay out” ZS and the Economist Information Unit, Ltd. June 2019 Problem definition/framing Solution approach/design Data integration/preparation Scoping/triage/supplier selection Analysis execution Interpretation and synthesis Presentation/communication Action/change management 43% 47% 39% 21% 18% 15% 8% 47%
  5. 5. @jamet123 #decisionmgt © 2019 Decision Management Solutions 5 Because the Last Mile is Hard Source: McKinsey - “Raising returns on analytics investments in insurance” Failure Mode Limited adoption of integration Lack of strategic alignment and direction Poor data quality Other Description Inability to integrate analytics solutions into work flows Limited frontline adoption Lack of stakeholder alignment or support Lack of clear road map Missing or incomplete data Data quality or accuracy issues Data fragmentation Missing team skills or capabilities Unclear case scope Inability to articulate value 19 17 26 38
  6. 6. @jamet123 #decisionmgt © 2019 Decision Management Solutions 6 “Most important, breakaway companies target much of [their] spending toward the biggest challenge companies face in extracting value from analytics— the last mile...” Leaders Understand This “Most important, breakaway companies target much of this spending toward the biggest challenge companies face in extracting value from analytics—the last mile, or embedding analytics into the core of all workflows and decision- making processes (more on this later). Nearly 90 percent of breakaway organizations devote more than half of their analytics budgets to this effort, versus only 23 percent of all other organizations that do so.” Breaking away: The secrets to scaling analytics, May 2019 By Peter Bisson, Bryce Hall, Brian McCarthy, and Khaled Rifai
  7. 7. @jamet123 #decisionmgt © 2019 Decision Management Solutions 8 Why Is The Last Mile Important? Why Is The Last Mile Hard? Succeed By Putting The Last Mile, First
  8. 8. @jamet123 #decisionmgt © 2019 Decision Management Solutions 9 Why Is The Last Mile Important?
  9. 9. @jamet123 #decisionmgt © 2019 Decision Management Solutions 11 Because Analytic Value Requires Business Value valuable analytic Adjective-Noun Pair: an analytic model of any type (regression, classification, ensemble, machine learning, neural network etc.) that has caused the organization that paid for its development to change its behavior in a way that adds business value to that organization.
  10. 10. @jamet123 #decisionmgt © 2019 Decision Management Solutions 12 Because Predictive Analytics Don’t DO Anything Acting on the prediction doesHaving a prediction doesn’t change behavior Simply having a prediction is not enough to change behavior and so improve results. Organizations must act on those predictions, they must change the way they behave because of the prediction, if that prediction is to have value.
  11. 11. @jamet123 #decisionmgt © 2019 Decision Management Solutions 13 Because Delivering Value Means Automated Decisions  Partially or fully automated  Digital Decisioning Whether the decision-making in those systems is completely automated or just partially automated and reliant on an external human to participate does not matter. Unless the decision-making in your systems is changed by your analytics, delivering value will prove impossible.
  12. 12. @jamet123 #decisionmgt © 2019 Decision Management Solutions 15 Why Is The Last Mile Hard?
  13. 13. @jamet123 #decisionmgt © 2019 Decision Management Solutions 16 Because IT Approaches Are Not Analytic Approaches Data Structure Deterministic Requirements Testing Data History Probabilistic Possibilities Simulation They worry about the structure of data, not history and analysis. They care about the fields and allowed values but not about the history or distribution of those values. They focus on deterministic results not probabilistic ones. They assume that a solution will continue to be correct, not thinking that new data may undermine assumptions. They focus on specific, known requirements not the possibilities – the if only – of prediction and insight. And they focus on testing what is built to see if it meets requirements rather than simulating the business impact of the possibilities.
  14. 14. @jamet123 #decisionmgt © 2019 Decision Management Solutions 17 Because You Need A Three Legged Stool Success IT Business Operations Analytics Success in the last mile involves coordinating three groups – IT, Analytics and Business Operations. IT methodologies don’t work for analytics, analytics teams don’t like thinking about systems and integration, and the business does not really understand either of them.
  15. 15. @jamet123 #decisionmgt © 2019 Decision Management Solutions 18 Because The Value Delivery Landscape Is complex  Many Tools  Many Platforms  Multiple UIs  Complex Processes There’s a tremendous amount of complexity in the delivery landscape. This allows both the IT and analytics teams to geek out over their own tools and platforms. Plus there are often many user interfaces impacting those involved and complex processes that will need to change.
  16. 16. @jamet123 #decisionmgt © 2019 Decision Management Solutions 19 Succeed By Putting the Last Mile First
  17. 17. @jamet123 #decisionmgt © 2019 Decision Management Solutions 20 1. Put Decisions First For Business Value What (business) measures? What (business) decisions have an impact? Which decision should we improve? What does improve mean? Customer satisfaction Pricing, claims handling, renewal Claims handling Increase STP rate without more fraud or waste Make the project about the Decision, not the Analytic
  18. 18. @jamet123 #decisionmgt © 2019 Decision Management Solutions 21 “Breakaway companies … have identified and prioritized the … decision-making processes in which to embed analytics.” Because Leaders Prioritize Decision-Making “Breakaway companies are almost twice as likely to have identified and prioritized the top ten to 15 decision-making processes in which to embed analytics.” Breaking away: The secrets to scaling analytics, May 2019 By Peter Bisson, Bryce Hall, Brian McCarthy, and Khaled Rifai 1.75X more likely to prioritize top decision- making processes 31 55 % of respondents who strongly agree that they have clearly prioritized the top 10-15 key decision-making processes in which to embed analytics insights
  19. 19. @jamet123 #decisionmgt © 2019 Decision Management Solutions 22 External Data Big Data 2. Deliver Analytics To The Last Mile With Decision Services Analytics, ML and AI Business Rules • Business Rules are quick to change • Good for regulations, policies, flash updates • Less insight-rich than analytics • Analytics are insight-rich but often opaque, especially ML and AI • Good for patterns, trends, categorization • Must be fed new data and continuously improved A decision service encapsulates analytics, ML and AI to deliver automated decisions across the last mile Data about business outcomes and decisions made is integrated with external data to close the loop and improve both rules and analytics
  20. 20. @jamet123 #decisionmgt © 2019 Decision Management Solutions 23 Because Decisions Are Easier To Integrate Than Scores Integrate Decisions, not Scores Consistent integration Can explain the use of the score Focus on improving decision-making Analytics, business, IT collaborate Every environment different Can only explain the score itself Focus on improving the math IT on point
  21. 21. @jamet123 #decisionmgt © 2019 Decision Management Solutions 24 3. Put Analytics in a Decision Context Decisions involve more than just your analytic score. They need to apply regulations, best practices, policies and business domain knowledge also. All these elements need to be mixed and matched to create a decisioning solution. Decision Modeling shows all the elements of a decision and enables you to mix and match the right technologies to automate enough of the decision to take advantage of your predictive analytic models and machine learning. Mix and match Decision Technology
  22. 22. @jamet123 #decisionmgt © 2019 Decision Management Solutions 25 Because Business Rules Make Analytics Actionable ▪ While analytics can improve decision-making, they often must be combined with business rules that define thresholds or constraints. ▪ Decision models explicitly document what the different elements of a decision are and how they interact. ▪ This allows analytics, business rules, ML and AI to be combined to make a better business decision. Business Rules Descriptive Analytic Machine Learning Model
  23. 23. @jamet123 #decisionmgt © 2019 Decision Management Solutions 26 New Approach To Succeed With Machine Learning: Put Decisions First Results Improve Decisions Build Analytics Find Data Focusing on operational decisions first ensures a strong business context for predictive analytics and machine learning projects. Starting with the business goals of the project, the decisions that have an impact on those goals can be identified. Understanding these decisions will reveal which analytics are required and then the data needed to build these analytics can be found, organized, cleaned and prepared.
  24. 24. @jamet123 #decisionmgt © 2019 Decision Management Solutions 27 “Most companies start their analytics journey with data. Almost by definition, that approach will limit analytics’ impact.” “To achieve analytics at scale, companies should … start by identifying the decision-making … they could improve to generate additional value” Be A Leader “Most companies start their analytics journey with data; they determine what they have and figure out where it can be applied. Almost by definition, that approach will limit analytics’ impact. To achieve analytics at scale, companies should work in the opposite direction. They should start by identifying the decision-making processes they could improve to generate additional value in the context of the company’s business strategy and then work backward to determine what type of data insights are required to influence these decisions and how the company can supply them.” Breaking away: The secrets to scaling analytics, May 2019 By Peter Bisson, Bryce Hall, Brian McCarthy, and Khaled Rifai
  25. 25. @jamet123 #decisionmgt © 2019 Decision Management Solutions 28 Make the project about the Decision, not the Analytic Integrate Decisions, not Scores Mix and match Decision technology Make sure it will be actionable, before you build it
  26. 26. Thank You For more on Decision Management, go to: decisionmanagementsolutions.com @jamet123 #decisionmgt © 2019 Decision Management Solutions If you have further questions or comments: james@decisionmanagementsolutions.com +1 650 400 3029

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