Automated decision making using Predictive Applications – Big Data Paris
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The document discusses the role of big data and automated decision-making in various industries, emphasizing the importance of effective data utilization for better business decisions. It highlights the pitfalls of human decision-making biases and contrasts them with the efficiency of computer-driven decisions, advocating for the automation of business rules and predictions via machine learning. The content further outlines the categorization of analytics and predictive applications aimed at optimizing decision-making processes in real-time.
Automated decision making using Predictive Applications – Big Data Paris
- 2. Automated Decision Making with Big Data Lars Trieloff | @trieloff
- 3. Automated Decision Making with Big Data Predictive Applications Lars Trieloff | @trieloff
- 4. — Daniel Kahneman “Prejudice against algorithms is magnified when the decisions are consequential.”
- 5. What would you do when every decision counts?
- 6. 4%Worldwide average profit margin in retail: 4%
- 7. 4‰German average profit margin in retail: 4‰
- 8. Your Customer gives you this
- 9. All you got to keep is that
- 10. — –Libby Rittenberg “Economic profits in a system of perfectly competitive markets will, in the long run, be driven to zero in all industries.”
- 11. Who is using Big Data Today?
- 12. Where Big Data is Used Effective Use Marketing Finance Everyone Else
- 13. Three Approaches Faster DataMore Data Better Decisions
- 14. Digital Marketing: More Data
- 15. Financial Services: Faster Data
- 16. But what about better Decisions?
- 18. — Abraham Maslov – probably never said this. It’s true anyway. “Data has Human Needs, too”
- 22. — W. Edward Deming “In God we trust, all others bring data”
- 23. How Data-Driven Decisions should work Computer Collects Computer Stores Human Analyzes Human Predicts Human Decides
- 24. 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
- 25. — Led Zeppelin Communication Breakdown, It's always the same, I'm having a nervous breakdown, Drive me insane!
- 26. • 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
- 27. How Data-Driven Decisions REALLY work http://dilbert.com/strips/comic/2007-05-16/
- 28. 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
- 29. — Everyone at Blue Yonder, all the time 99.9% of all business decisions can be automated
- 30. How Decisions are Being Made
- 31. 90% No Decision is made
- 32. — Robin Sharma “Making no decision is a decision. To do nothing. And nothing always brings you nowhere..”
- 33. Business Rules for Beginners Not doing anything is the simplest business rule in the world – and also the most popular
- 34. 90% No Decision is made
- 35. 9% Decision Follows Rule
- 36. Business Rules in Action
- 37. Advanced Business Rules Computers are machines following rules. This means business rules are programs.
- 38. • 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.
- 39. — Mark Twain “It ain’t what we don’t know that causes trouble, it’s what we know for sure that just ain’t so”
- 40. 1% Human Decision making
- 41. Human Decision Making has two systems – and only one is rational.
- 42. Not quite Almost there That’s it.
- 43. — Daniel Kahneman “All of us would be better investors if we just made fewer decisions.”
- 45. 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
- 47. • 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.
- 48. 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)
- 49. • 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
- 50. Better Decisions through Predictive Applications
- 51. How Predictive Applications Work Collect & Store Analyze Correlations Build Decision Model Decide & Test Optimize
- 52. Why Test?
- 53. — Randall Munroe “Correlation doesn’t imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing‘look over there’”
- 54. — 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.”
- 55. More than half of the apps on a typical iPhone home screen are predictive applications.
- 56. Fast DataInsight Big Data Categorizing Analytics Past Present Future No DataHindsight Foresight 1. By Data Volume 2. By Time Horizon
- 57. 1 Categorizing Analytics Descriptive • Focused on gathering and collecting data • Key challenges: data volume and data variety • Key outcome: hindsight • Examples: reports, dashboards • Answers “What happened?” Predictive • Focused on understanding and explaining data • Key challenges: data velocity and complexity • Key outcome: insight • Examples: prediction models • Answers: “Why did it happen and what will happen next?” Prescriptive • Focused on anticipating and recommending action • Key challenges: execution • Key outcome: foresight • Examples: decision support, predictive apps • Answers: “What should we do?” 2 3
- 58. A Categorizing Analytics Explicit • Analytics are a key visible feature of the program • Programs are used by trained analysts and data scientists • Regular interaction during business hours Integrated • Analytics are included in another program • Analytics are consumed in- context by business users • Frequent, but irregular consumption during business hours Embedded • Analytics are invisibly part of a complex process • Decisions are made and executed in the process • Constant and ongoing optimization 24/7 B C
- 59. Analytic Application Matrix 2 3 B C + + = = Predictive Integrated EmbeddedPrescriptive Decision Support systems for infrequent strategic decision- making Predictive Applications for massive, automated decision-making in operational processes
- 60. Building Predictive Applications Machine Learning ModelPredictive Application Enterprise Integration
- 61. 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
- 62. 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
- 63. — Kevin Kelly “The business plans of the next 10,000 startups are easy to forecast: Take X and add AI”