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Decision Intelligence: Supercharging Machine Learning to 1000s of new use cases

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So often, we use machine learning for a limited set of use cases, in customer experience, marketing, advertising, and finance.

In this talk, we step back to understand how big data and machine learning can serve a new class of problems: those for which users need to know "what will be the impact of today's decision, tomorrow?"

Published in: Data & Analytics
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Decision Intelligence: Supercharging Machine Learning to 1000s of new use cases

  1. 1. Decision Intelligence: Supercharging Machine Learning to 1000s of new use cases Dr. Lorien Pratt, Chief Scientist, Quantellia Copyright © 2014 Quantellia LLC
  2. 2. Question: If technology could solve one problem for you, that it doesn’t solve today, what would it be?
  3. 3. Question: If technology could solve one problem for you, that it doesn’t solve today, what would it be? Answer: Massive amounts of data, machine learning, other advanced technology, but it’s not getting used for the most important decisions.
  4. 4. Decision Makers GAP Machine Learning Analytics Data
  5. 5. Decision Makers What will be the impact of today’s decision, tomorrow? Machine Learning Analytics Data
  6. 6. Complex interdependencies, with critical consequences
  7. 7. From… To… Source: Tibco Jaspersoft “Isn’t there a better way? I am making big decisions: is there a way to structure all this data, and to use machine learning, to get the most value out of it?”
  8. 8. From… To… Source: Tibco Jaspersoft Many new use cases
  9. 9. Two ways we use data Big Decisions
  10. 10. Data Instrumented Code / Sensors Data Management Analytics Gap between computer and human bridged by Data Visualization Presentation Demarcation between automated (computer-centric) and manual (human-centric) information processing
  11. 11. Gap between computer and human bridged by Data Visualization System Analysis Decision Data Instrumented Code / Sensors Data Management Analytics Presentation Demarcation between automated (computer-centric) and manual (human-centric) information processing
  12. 12. What is a the relationship between data, systems, and decisions? Decision Lever Outcome Outcome Outcome Externals External Factors Decision Lever Intermediates Intermediates External Factors Intermediates Goal Goal Each connector represents a dependency. Goal
  13. 13. Big Decisions
  14. 14. Fa“FcACtTiIvVISisM”m Intervention Impact analysis How can we best deploy security to ensure a fair election? How can we maximize the value of aid to reduce childhood mortality?
  15. 15. “Poor decision making can cost– and, in an industry that invests as much as telecoms, the total cost can be very large indeed.” “Our research reveals that, in the past decade, the average long-term return on investment (ROI) has been just 6%—three percentage points less than the cost of the capital itself.”
  16. 16. “What is critical in today’s complex world is the ability to see over the horizon and around corners to understand the impact of today’s decisions on all of the desired outcomes.”
  17. 17. “We are seeing increasing demand for a C-level executive who understands how to use data and machine learning to support business decisions. This may end up as a role for the CIO, Chief Data Officer (CDO), or a new role may emerge: the Chief Decision Officer: who is in charge of using expertise and evidence to support the company’s most important business decisions” Adam-Bryce, LLC (919) 638-0707
  18. 18. DECISION INTELLIGENCE A new f ield
  19. 19. DECISION INTELLIGENCE A new f ield
  20. 20. Today Big Data Big Decisions A view of the future….
  21. 21. A challenge
  22. 22. TRADITIONAL VIEW What will be the outcome? What decisions can we make? Data, Analytics, Big Data, Reports, Predictive Analytics, Spreadsheets
  23. 23. DECISION INTELLIGENCE VIEW What data, analytics, reports, human expertise, and other assets are relevant? What outcomes do we need or want to reach ? What decisions will get us there?
  24. 24. “…our predictions may be more prone to failure in the era of Big Data. As there is an exponential increase in the amount of available information, there is likewise an exponential increase in the number of hypotheses to investigate. For instance, the U.S. government now publishes data on about 45,000 economic statistics. If you want to test for relationships between all combinations of two pairs of these statistics–is there a causal relationship between the bank prime loan rate and the unemployment rate in Alabama?– that gives you literally one billion hypotheses to test. But the number of meaningful relationships in the data–those that speak to causality rather than correlation and testify to how the world really works–is orders of magnitude smaller.” —Nate Silver Who correctly called the outcomes of the 2012 US Presidential election in all 50 states
  25. 25. Elements Need: 1) A systems model (with systems dynamics, feedback loops, etc.) 2) Machine learning 3) Information from experts for when data is missing 4) Simulation 5) Optimization 6) Crystal clear visualization 7) An agency model to add to the information model 8) Interactivity
  26. 26. Decision Makers What will be the impact of today’s decision, tomorrow? Machine Learning Analytics Data
  27. 27. A DECISION INFLUENCES A SYSTEMS MODEL, WHICH RESULTS IN OUTCOMES Decision Lever Outcome Outcome Outcome Externals External Factors Decision Lever Intermediates Intermediates External Factors Intermediates Goal Goal Each connector represents a dependency. Goal
  28. 28. Decision Makers The CDO’s responsibility is to fill this gap Machine Learning Analytics Data
  29. 29. Lack of consistent service Net Promoter Score Irrelevant proactive notifications Invest in consistent customer data Pain of having to deal with the call center Invest in self service improvement via the smartphone How many people use the call center Invest in improving the call center Churn Levers Outcomes Externals Loyalty Revenue Better proactive resolution of issues Customer calling behavior Customer needs My competitor’s NPS Competitor advertising
  30. 30. Cause Effect Machine Learning rule, from historical data that captures this link
  31. 31. Lack of consistent service Net Promoter Score Irrelevant proactive notifications Invest in consistent customer data Pain of having to deal with the call center Invest in self service improvement via the smartphone How many people use the call center Invest in improving the call center Churn Levers Outcomes Externals Loyalty Revenue Better proactive resolution of issues Customer calling behavior Customer needs My competitor’s NPS Competitor advertising
  32. 32. “If I make this decision today, how will it affect my outcomes in the future?” Decision Lever Outcome Outcome Outcome Externals External Factors Decision Lever Intermediates Intermediates External Factors Intermediates Goal Goal Each connector represents a dependency. Goal
  33. 33. Forward modeling Decision Lever Outcome Outcome Outcome Externals External Factors Decision Lever Intermediates Intermediates External Factors Intermediates Goal Goal Goal
  34. 34. Optimization Decision Lever Outcome Outcome Outcome Externals External Factors Decision Lever Intermediates Intermediates External Factors Intermediates Goal Goal Goal
  35. 35. How do we maximize profit? Should we generate our own renewable energy? If so, when is the best time to do so? Tens of millions of dollars in savings potential per year for a typical large enterprise
  36. 36. How do we best invest in the legal system to set a developing country on a road to avoid future conflict? Should we invest in buildings and books or motorcycles and paralegals? Thousands of lives at stake
  37. 37. “Is my money better spent on more servers or more iPads?”
  38. 38. “Which buildings should I transform to cloud/VOIP first, to maximize business benefit?
  39. 39. “Where should I place network equipment to build the next internet, at the lowest cost and maximum value to customers?” Typical – 72 homes per Node Optimal – 115 homes per Node
  40. 40. “Where should I place wifi hotspots in my town to provide the best customer service and to maximize revenues?”
  41. 41. What to do next? 1. Start thinking about your organization’s “big decisions” 2. Identify outcomes and goals 3. Identify levers 4. Identify externals 5. Build a decision model picture to show how they connect 6. Identify data that can be learned from to analyze cause-and-effect links. 7. Where data is missing, find human expertise 8. Combine existing learned rules as parts of the full decision model 9. Assign someone to be responsible 10. Learn about complex systems analysis 11. Learn about optimization and simulation Learn more on at http://www.youtube.com/quantellia Unified resource: http://www.scoop.it/t/decison-intelligence
  42. 42. Thank You Dr. Lorien Pratt, Chief Scientist, @Quantellia, www.quantellia.com Lorien.pratt@quantellia.com +1 303 589 7476 http://www.scoop.it/t/decision-intelligence Copyright © 2014 Quantellia LLC

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