Automated decision making with big data – Big Data Vienna
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My Big Data Vienna presentation on automated decision making with big data, predictive appl
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Automated decision making with big data – Big Data Vienna
- 2. Automated Decision Making with Big Data Lars Trieloff | @trieloff
- 3. What would you do when every decision counts?
- 4. 4%Worldwide average profit margin in retail: 4%
- 5. 4‰German average profit margin in retail: 4‰
- 6. Your Customer gives you this
- 7. All you got to keep is that
- 8. — –Libby Rittenberg “Economic profits in a system of perfectly competitive markets will, in the long run, be driven to zero in all industries.”
- 9. Who is using Big Data Today?
- 10. Where Big Data is Used Effective Use Marketing Finance Everyone Else
- 11. Where Big Data is Used Effective Use Marketing Finance Everyone Else
- 12. Where Big Data is Used Potential Use Marketing Finance Everyone Else
- 13. Three Approaches
- 14. Three Approaches Faster DataMore Data Better Decisions
- 15. M O R E D ATA D I G I TA L M A R K E T I N G ’ S A P P R O A C H :
- 16. M O R E D ATA D I G I TA L M A R K E T I N G ’ S A P P R O A C H :
- 17. Digital Marketing: More Data
- 18. Digital Marketing: More Data
- 19. Financial Services: Faster Data
- 20. But what about better Decisions?
- 22. Physiological
- 27. — Abraham Maslov – probably never said this. It’s true anyway. “Data has Human Needs, too”
- 29. Collection
- 37. — W. Edward Deming “In God we trust, all others bring data”
- 38. How Data-Driven Decisions should work Computer Collects Computer Stores Human Analyzes Human Predicts Human Decides
- 39. 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
- 40. — Led Zeppelin Communication Breakdown, It's always the same, I'm having a nervous breakdown, Drive me insane!
- 41. • 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 C O M M U N I C AT I O N B R E A K D O W N
- 42. How Data-Driven Decisions REALLY work http://dilbert.com/strips/comic/2007-05-16/
- 43. How Decisions REALLY should work Computer Collects Computer Stores Computer Analyzes Computer Predicts C O M P U T E R D E C I D E S
- 44. — Everyone at Blue Yonder, all the time 99.9% of all business decisions can be automated
- 45. How Decisions are Being Made
- 46. 90% No Decision is made
- 47. — Robin Sharma “Making no decision is a decision. To do nothing. And nothing always brings you nowhere..”
- 48. Business Rules for Beginners
- 49. Business Rules for Beginners Not doing anything is the simplest business rule in the world – and also the most popular
- 50. 90% No Decision is made
- 51. 9% Decision Follows Rule
- 52. Business Rules in Action
- 54. Advanced Business Rules Computers are machines following rules. This means business rules are programs.
- 55. • 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.
- 56. 1% Human Decision making
- 57. Human Decision Making has two systems – and only one is rational.
- 58. Not quite Almost there That’s it.
- 59. — Daniel Kahneman “All of us would be better investors if we just made fewer decisions.”
- 61. How we are making decisions (Like the big apes we are)
- 62. 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
- 66. • Abraham Lincoln and John F. Kennedy were both presidents of the United States, elected 100 years apart. • Both were shot and killed by assassins who were known by three names with 15 letters, John Wilkes Booth and Lee Harvey Oswald, and neither killer would make it to trial. • Lincoln had a secretary named Kennedy, and Kennedy had a secretary named Lincoln. • They were both killed on a Friday while sitting next to their wives, Lincoln in the Ford Theater, Kennedy in a Lincoln made by Ford.
- 67. • Abraham Lincoln and John F. Kennedy were both presidents of the United States, elected 100 years apart. • Both were shot and killed by assassins who were known by three names with 15 letters, John Wilkes Booth and Lee Harvey Oswald, and neither killer would make it to trial. • Lincoln had a secretary named Kennedy, and Kennedy had a secretary named Lincoln. • They were both killed on a Friday while sitting next to their wives, Lincoln in the Ford Theater, Kennedy in a Lincoln made by Ford.
- 68. Computers making decisions (cold, fast, cheap, rational)
- 69. 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)
- 70. • 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
- 71. Better Decisions through Predictive Applications
- 72. How Predictive Applications Work Collect & Store Analyze Correlations Build Decision Model Decide & Test Optimize
- 73. Why Test?
- 74. — Randall Munroe “Correlation doesn’t imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing‘look over there’”
- 75. Story Time
- 76. Story Time (Not safe for vegetarians)
- 77. The Ground Beef Dilemma
- 78. The Ground Beef Dilemma
- 79. The Ground Beef Dilemma Yesterday Today Tomorrow Next Delivery Next Day In Stock Demand
- 80. • Order too much and you will have to throw meat away when it goes bad. You lose money and cows die in vain • Order too little and you won’t serve all your potential customers. You lose money and customers stay hungry. The Ground Beef Dilemma
- 81. Challenge #1 Accurately predict demand
- 82. 1. Estimate Demand (with Probability) 0 22,5 45 67,5 90 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
- 83. 2. Account for Package Sizes 0 22,5 45 67,5 90 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
- 84. 3. Cost of Write-Offs & Lost Sales 0 22,5 45 67,5 90 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
- 85. 4. Weigh Costs by Probability 0 22,5 45 67,5 90 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
- 86. 5. Aggregate Costs 0 22,5 45 67,5 90 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
- 87. 6. Find Minimum Cost 0 22,5 45 67,5 90 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
- 88. Small Changes have Large Effects
- 89. Small Changes have Large Effects 0 22,5 45 67,5 90 0% 20% 40% 60% 80% 100% 0 20 40 60 80 0% 20% 40% 60% 80% 100% 0 20 40 60 80 0% 20% 40% 60% 80% 100% 0 20 40 60 80 0% 20% 40% 60% 80% 100% 0 20 40 60 80 0% 20% 40% 60% 80% 100% 0 22,5 45 67,5 90 0% 20% 40% 60% 80% 100% 0 20 40 60 80 0% 20% 40% 60% 80% 100% 0 20 40 60 80 0% 20% 40% 60% 80% 100% 0 22,5 45 67,5 90 0% 20% 40% 60% 80% 100%
- 90. — John Maynard Keynes “When my information changes, I alter my conclusions. What do you do, sir?”
- 91. Automate Replenishment Collect Stock and Sales Predict Demand Trade Off Costs Create Orders in ERP System Optimize & Repeat
- 92. 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
- 93. One Common Platform for Predictive Applications Your own and third- party data, easily integrated via API Link Build Machine Learning and application code Build Automatically run and scale ML models and applications Run Monitor and inspect resource usage and model quality View Your data stored in high-performance database as a service Store