Automated decision making with predictive applications – Big Data Amsterdam
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Transcript of Automated decision making with predictive applications – Big Data Brussels
— Lars Trieloff, boarding this planeOuch.
Automated Decision Making with Big DataLars Trieloff | @trieloff
Automated Decision Making with Big Data Predictive ApplicationsLars Trieloff | @trieloff
— 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. “
4%Worldwide average profit margin in retail: 4%
4‰German average profit margin in retail: 4‰
Your Customer gives you this
All you got to keep is that
— –Libby Rittenberg
“Economic profits in a system of perfectly competitive markets will, in the long run, be driven to zero in all industries.”
Physiological
Safety
Love/Belonging
Esteem
Self-Actualization
— Abraham Maslov – probably never said this. It’s true anyway.“Data has Human Needs, too”
Collection
Storage
Analysis
Prediction
Decision
Collection
Storage
Analysis
Prediction
Decision
Physiological
Safety
Love/Belonging
Esteem
Self-Actualization
— W. Edward Deming
“In God we trust, all others bring data”
How Data-Driven Decisions should work
Computer Collects
Computer Stores
Human Analyzes
Human Predicts
Human Decides
— Daniel Kahneman
“Prejudice against algorithms is magnified when the decisions are consequential.”
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
— Led Zeppelin
Communication Breakdown, It's always the same, I'm having a nervous breakdown, Drive me insane!
• 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
How Data-Driven Decisions REALLY work
http://dilbert.com/strips/comic/2007-05-16/
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
— Everyone at Blue Yonder, all the time
99.9% of all business decisions can be automated
How Decisions are Being Made
90% No Decision is made
— Robin Sharma
“Making no decision is a decision. To do nothing. And nothing always brings you nowhere..”
Business Rules for Beginners
Not doing anything is the simplest business rule in the world – and also the most popular
90% No Decision is made
9% Decision Follows Rule
Business Rules in Action
Advanced Business Rules
Computers are machines following rules. This means business rules are programs.
• 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.
— 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”
1% Human Decision making
Human Decision Making has two systems – and only one is rational.
Not quite Almost there That’s it.
Quick: What do you see here?
— Steven Pinker, describing Moravec’s Paradox
“The hard problems are easy and the easy problems are hard.”
Quick: Add all even numbers
65 7 1 0
60 63 18 80
547039100
69 20 26 73
94 39 37 31
92 70 100 67
4956080
69 20 26 73
51 60 23 22
5 48 43 14
9525669
23 67 1 43
Correct Result:
Correct Result: 1.024
— Daniel Kahneman
“All of us would be better investors if we just made fewer decisions.”
How we are making decisions (Like the big apes we are)
Anchoring effectIKEA effect
Confirmation bias
Bandwagon effect
Substitution
Availability heuristic Texas Sharpshooter Fallacy
Rhyme as reason effect
Over-justification effect
Zero-risk bias
Framing effect
Illusory correlationSunk cost fallacy
Overconfidence
Outcome bias
Inattentional Blindness
Benjamin Franklin effect
Hindsight bias
Gambler’s fallacy
Anecdotal evidenceNegativity bias
Loss aversion
Backfire effect
• 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.
K-Means Clustering
Naive BayesSupport 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)
• 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
Better Decisions through Predictive Applications
How Predictive Applications Work
Collect & Store Analyze Correlations
Build Decision Model
Decide & Test Optimize
Why Test?
— Randall Munroe
“Correlation doesn’t imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing ‘look over there’”
— 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.”
More than half of the apps on a typical iPhone home screen are predictive applications.
Fast DataInsight
Big Data
Categorizing Analytics
Past Present Future
No DataHindsight
Foresight
1. By Data Volume 2. By Time Horizon
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
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
Automated• Analytics are invisibly part of a
complex process
• Decisions are made and executed in the process
• Constant and ongoing optimization 24/7
B C
Analytic Application Matrix
2
3
B
C
+
+
=
=
Predictive Integrated
AutomatedPrescriptive
Decision Support systems for infrequent strategic decision-making
Predictive Applications for massive, automated decision-making in operational processes
Building Predictive Applications
Machine Learning ModelPredictive Application
Enterprise Integration
Predictive Apps in a NutshellBatch 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
— John Maynard Keynes
“When my information changes, I alter my conclusions. What do you do, sir?”
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
— Kevin Kelly
“The business plans of the next 10,000 startups are easy to forecast: Take X and add AI”
Lars Trieloff @trieloff
Tree Love