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Automated decision making with predictive applications – Big Data Amsterdam
The document discusses the role of automated decision-making in business through big data and predictive applications, highlighting the reliance on qualitative decision factors over quantitative data. It outlines various forms of decision processes, from human-driven to fully automated systems, and emphasizes the importance of analytics in improving decision-making efficiency. Additionally, it addresses the challenges and biases associated with both human and algorithmic decisions, asserting that businesses can significantly enhance decision accuracy through machine learning and predictive applications.
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Automated decision making with predictive applications – Big Data Amsterdam
- 2. Automated Decision Making withBig Data Lars Trieloff | @trieloff
- 3. Automated Decision Making withBig Data Predictive Applications Lars Trieloff | @trieloff
- 4. — Holger Kisker,Forrester Research “Even after more than 20 years of using BI, they still base nearly 45% of business decisions on qualitative decision factors instead of quantitative, fact-based evidence.“
- 5. 4%Worldwide average profit marginin retail: 4%
- 6. 4‰German average profitmargin in retail: 4‰
- 7. Your Customer givesyou this
- 8. All you gotto keep is that
- 9. — –Libby Rittenberg “Economicprofits in a system of perfectly competitive markets will, in the long run, be driven to zero in all industries.”
- 10.
- 11. — Abraham Maslov– probably never said this. It’s true anyway. “Data has Human Needs, too”
- 12.
- 13.
- 15. — W. EdwardDeming “In God we trust, all others bring data”
- 16. How Data-Driven Decisions shouldwork Computer Collects Computer Stores Human Analyzes Human Predicts Human Decides
- 17. — Daniel Kahneman “Prejudiceagainst algorithms is magnified when the decisions are consequential.”
- 18. How Data-Driven Decisions REALLYwork 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
- 19. — Led Zeppelin CommunicationBreakdown, It's always the same, I'm having a nervous breakdown, Drive me insane!
- 20. • 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
- 21. How Data-Driven Decisions REALLYwork http://dilbert.com/strips/comic/2007-05-16/
- 22. How Decisions REALLYshould work Computer Collects Computer Stores Computer Analyzes Computer Predicts CO M P U T E R D E C I D E S
- 23. — Everyone atBlue Yonder, all the time 99.9% of all business decisions can be automated
- 24. How Decisions areBeing Made
- 25. 90% No Decisionis made
- 26. — Robin Sharma “Makingno decision is a decision. To do nothing. And nothing always brings you nowhere..”
- 27. Business Rules forBeginners Not doing anything is the simplest business rule in the world – and also the most popular
- 28. 90% No Decisionis made
- 29.
- 30. Business Rules inAction
- 31. Advanced Business Rules Computersare machines following rules. This means business rules are programs.
- 32. • Business rulesare 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.
- 33. — Mark Twain “Itain’t what we don’t know that causes trouble, it’s what we know for sure that just ain’t so”
- 34. 1% Human Decisionmaking
- 35. Human Decision Makinghas two systems – and only one is rational.
- 36. Not quite Almostthere That’s it.
- 37. — Daniel Kahneman “Allof us would be better investors if we just made fewer decisions.”
- 39. How we aremaking 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
- 41. • Abraham Lincolnand 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.
- 42. K-Means Clustering Naive Bayes SupportVector 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)
- 43. • A machinelearning 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
- 44. Better Decisions through PredictiveApplications
- 45. How Predictive Applications Work Collect& Store Analyze Correlations Build Decision Model Decide & Test Optimize
- 46.
- 47. — Randall Munroe “Correlationdoesn’t imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing‘look over there’”
- 48. — Warren Buffett “Ichecked the actuarial tables, and the lowest death rate is among six-year-olds, so I decided to eat like a six-year- old.”
- 49. More than halfof the apps on a typical iPhone home screen are predictive applications.
- 50. Fast DataInsight Big Data CategorizingAnalytics Past Present Future No DataHindsight Foresight 1. By Data Volume 2. By Time Horizon
- 51. 1 Categorizing Analytics Descriptive • Focusedon 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
- 52. A Categorizing Analytics Explicit • Analyticsare 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
- 53. Analytic Application Matrix 2 3 B C + + = = PredictiveIntegrated EmbeddedPrescriptive Decision Support systems for infrequent strategic decision- making Predictive Applications for massive, automated decision-making in operational processes
- 54. Building Predictive Applications MachineLearning ModelPredictive Application Enterprise Integration
- 55. Predictive Apps ina 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
- 56. — John MaynardKeynes “When my information changes, I alter my conclusions. What do you do, sir?”
- 57. One Common Platformfor 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
- 58. — Kevin Kelly “Thebusiness plans of the next 10,000 startups are easy to forecast: Take X and add AI”
- 59.