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Business Reasons for Predictive Applications

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What are predictive applications, how do they work and how can companies get started using predictive applications?

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Business Reasons for Predictive Applications

  1. 1. Business Reasons for Automated Decisions Lars Trieloff | @trieloff
  2. 2. • They are faster • They are cheaper • They are better • That’s it. Thank you for your attention. Why Automated Decisions make Business Sense
  3. 3. 4%Worldwide average profit margin in retail: 4%
  4. 4. 4‰German average profit margin in retail: 4‰
  5. 5. Your Customer gives you this
  6. 6. All you got to keep is that
  7. 7. — –Libby Rittenberg “Economic profits in a system of perfectly competitive markets will, in the long run, be driven to zero in all industries.”
  8. 8. Physiological Safety Love/Belonging Esteem Self-Actualization
  9. 9. — Abraham Maslov – probably never said this. It’s true anyway. “Data has Human Needs, too”
  10. 10. Collection Storage Analysis Prediction Decision
  11. 11. Collection Storage Analysis Prediction Decision Physiological Safety Love/Belonging Esteem Self-Actualization
  12. 12. — W. Edward Deming “In God we trust, all others bring data”
  13. 13. How Data-Driven Decisions should work Computer Collects Computer Stores Human Analyzes Human Predicts Human 
 Decides
  14. 14. — Daniel Kahneman “Prejudice against algorithms is magnified when the decisions are consequential.”
  15. 15. How Data-Driven Decisions REALLY work Computer Collects Computer Stores Human Analyzes C O M M U N I C AT I O N B R E A K D O W N Human 
 Decides
  16. 16. — Led Zeppelin Communication Breakdown, It's always the same, I'm having a nervous breakdown, Drive me insane!
  17. 17. • Drill-down analysis … misunderstood or distorted • Metrics dashboards … contradictory and confusing • Monthly reports … ignored after two iterations • In-house analyst teams … overworked and powerless How Data-Driven Decisions REALLY work CO M M U N I C AT I O N B R E A K D O W N
  18. 18. How Decisions REALLY should work Computer Collects Computer Stores Computer Analyzes Computer Predicts CO M P U T E R 
 D E C I D E S
  19. 19. — Everyone at Blue Yonder, all the time 99.9% of all business decisions can be automated
  20. 20. How Decisions are Being Made
  21. 21. 90% No Decision is made
  22. 22. — Robin Sharma “Making no decision is a decision. To do nothing. And nothing always brings you nowhere..”
  23. 23. Business Rules for Beginners Not doing anything is the simplest business rule in the world – and also the most popular
  24. 24. 90% No Decision is made
  25. 25. 9% Decision Follows Rule
  26. 26. Advanced Business Rules Computers are machines following rules. This means business rules are programs.
  27. 27. • Business rules are like programs – written by non-programmers • Business rules can be contradictory, incomplete, and complex beyond comprehension • Business rules have no built-in feedback mechanism:“It is the rule, because it is the rule” Business rules are Programs, just not very good ones.
  28. 28. 1% Human Decision making
  29. 29. Human Decision Making has two systems – and only one is rational.
  30. 30. Not quite Almost there That’s it.
  31. 31. Quick: What do you see here?
  32. 32. — Steven Pinker, describing Moravec’s Paradox “The hard problems are easy and the easy problems are hard.”
  33. 33. Quick: Add all even numbers 65 7 1 0 60 63 18 80 547039100
  34. 34. 94 39 37 31 92 70 100 67 4956080 69 20 26 73
  35. 35. 51 60 23 22 5 48 43 14 9525669 23 67 1 43
  36. 36. Correct Result:
  37. 37. Correct Result: 1.024
  38. 38. — Daniel Kahneman “All of us would be better investors if we just made fewer decisions.”
  39. 39. How we are making decisions (Like the big apes we are) Anchoring effect IKEA effect Confirmation bias Bandwagon effect Substitution Availability heuristic Texas Sharpshooter Fallacy Rhyme as reason effect Over-justification effect Zero-risk bias Framing effect Illusory correlation Sunk cost fallacy Overconfidence Outcome bias Inattentional Blindness Benjamin Franklin effect Hindsight bias Gambler’s fallacy Anecdotal evidence Negativity bias Loss aversion Backfire effect
  40. 40. K-Means Clustering Naive Bayes Support Vector Machines Affinity Propagation Least Angle Regression Nearest Neighbors Decision Trees Markov Chain Monte Carlo Spectral clustering Restricted Bolzmann Machines Logistic Regression Computers making decisions (cold, fast, cheap, rational)
  41. 41. • A machine learning algorithm is a system that derives a set of rules based on a set of data • It is based on systematic observation, double- checking and cross-validation • There is no magic, just data – and without data there is no magic either Machine Learning means Programs that write Programs
  42. 42. Better Decisions through Predictive Applications
  43. 43. How Predictive Applications Work Collect & Store Analyze Correlations Build Decision Model Decide &
 Test Optimize
  44. 44. — Warren Buffett “I checked the actuarial tables, and the lowest death rate is among six-year-olds, so I decided to eat like a six-year- old.”
  45. 45. More than half of the apps on a typical iPhone home screen are predictive applications.
  46. 46. Building Predictive Applications Machine Learning ModelPredictive Application Enterprise Integration
  47. 47. Story Time (Not safe for vegetarians)
  48. 48. The Ground Beef Dilemma
  49. 49. How much ground beef are we going to sell on Friday?
  50. 50. How much ground beef are we going to sell on Friday? And how much on Saturday?
  51. 51. Challenge #1 Accurately predict demand
  52. 52. Great. But how much do we need to order each day?
  53. 53. Great. But how much do we need to order each day? Let’s reduce the risk of running out of stock to 20%
  54. 54. Sales Forecasts for Friday SalesProbability 0 0,01 0,02 0,03 0,04 0 4 8 12 16 Friday Sales Amount
  55. 55. Sales Forecasts for Saturday SalesProbability 0 0,01 0,02 0,03 0,04 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Saturday Sales Amount
  56. 56. Great. But how much do we need to order each day? Let’s reduce the risk of running out of stock to 20% So it’s 3 on Friday and 5,5 on Saturday.
  57. 57. Sales Forecasts for Both Days SalesProbability 0 0,01 0,02 0,03 0,04 0 4 8 12 16 Friday Sales Amount Saturday Sales Amount
  58. 58. Bad news…
  59. 59. Bad news… We need to skip the Saturday delivery.
  60. 60. Bad news… We need to skip the Saturday delivery. How big should we make the Friday delivery instead?
  61. 61. If you need 3 on Friday and 5,5 on Saturday to fulfill 80% of the demand, how much do you need to fulfill 80% of the combined demand?
  62. 62. 3 + 5,5 = 8,5 Common Sense isn’t it?
  63. 63. — Albert Einstein Common sense is what tells us the world is flat.
  64. 64. Combined Sales Forecasts SalesProbability 0 0,01 0,02 0,03 0,04 0 4 8 12 16 Combined Sales Amount
  65. 65. If you ordered 8,5 cases, you would waste a lot of meat, the ideal order amount is 8 cases.
  66. 66. Predictive Apps in a Nutshell Batch and streaming data ingestion, batch and streaming delivery (with real-time option) Reduce risk and cost » increase revenue and profit Trend Estimation Classification Event Prediction Optimize Returns Collect Data Predict Results Drive Decisions
  67. 67. — John Maynard Keynes “When my information changes, I alter my conclusions. What do you do, sir?”
  68. 68. One Common Platform for Predictive Applications Multi-Tenant Runtime Environment Link Store Build Run View Link your own and third-party data, easily integrated via API Store your data in high-performance database as a service Build machine learning and application code Automatically run and scale ML models and applications Monitor and inspect resource usage and model quality Secure Micro Cloud Infrastructure Domain Model Predictive Model Application Code
  69. 69. — Kevin Kelly “The business plans of the next 10,000 startups are easy to forecast: Take X and add AI”
  70. 70. How Enterprises adopt Predictive Applications Learn about ADDD Define Target Process Build Predictive App Go Live Make Lots of Money
  71. 71. — Daniel Kahneman “Prejudice against algorithms is magnified when the decisions are consequential.”
  72. 72. How Enterprises REALLY adopt Predictive Applications Learn about ADDD Define Target Process Build Predictive App Make Lots of Money D O U BT S CO N C E R N S O B J E C T I O N S
  73. 73. Decision Quality Status Quo Predictive Prescriptive Automation
  74. 74. — The Economist, May 2015 “The best chess players in the world are‘centaurs’, amalgamated teams of humans and algorithms.”
  75. 75. Decision Quality Status Quo Predictive Prescriptive Automation 'Centaurs'
  76. 76. Ready, set, go for ADDD?
  77. 77. Not so fast
  78. 78. Data Availability Predictability Understandability Executability
  79. 79. Financial Impact of Predictive Apps -120 -90 -60 -30 0 30 60 90 March April May June July August September Break-Even after 3 months
  80. 80. • They are faster • They are cheaper • They are better • That’s it. Thank you for your attention. Why Automated Decisions make Business Sense
  81. 81. Lars Trieloff @trieloff

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