Automated decision making with big data – Big Data Vienna
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Transcript of Automated decision making with big data – Big Data Vienna
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Automated Decision Making with Big DataLars Trieloff | @trieloff
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What would you do when every decision counts?
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4%Worldwide average profit margin in retail: 4%
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4‰German average profit margin in retail: 4‰
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Your Customer gives you this
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All you got to keep is that
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— –Libby Rittenberg
“Economic profits in a system of perfectly competitive markets will, in the long run, be driven to zero in all industries.”
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Who is using Big Data Today?
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Where Big Data is Used
Effective Use
Marketing
Finance
Everyone Else
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Where Big Data is Used
Effective Use
Marketing
Finance
Everyone Else
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Where Big Data is Used
Potential Use
Marketing
Finance
Everyone Else
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Three Approaches
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Three Approaches
Faster DataMore Data Better Decisions
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M O R E D ATAD I G I TA L M A R K E T I N G ’ S A P P R O A C H :
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M O R E D ATAD I G I TA L M A R K E T I N G ’ S A P P R O A C H :
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Digital Marketing: More Data
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Digital Marketing: More Data
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Financial Services: Faster Data
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But what about better Decisions?
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Physiological
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Physiological
Safety
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Physiological
Safety
Love/Belonging
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Physiological
Safety
Love/Belonging
Esteem
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Physiological
Safety
Love/Belonging
Esteem
Self-Actualization
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— Abraham Maslov – probably never said this. It’s true anyway.“Data has Human Needs, too”
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Collection
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Collection
Storage
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Collection
Storage
Analysis
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Collection
Storage
Analysis
Prediction
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Collection
Storage
Analysis
Prediction
Decision
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Collection
Storage
Analysis
Prediction
Decision
Physiological
Safety
Love/Belonging
Esteem
Self-Actualization
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— W. Edward Deming
“In God we trust, all others bring data”
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How Data-Driven Decisions should work
Computer Collects
Computer Stores
Human Analyzes
Human Predicts
Human Decides
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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
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— Led Zeppelin
Communication Breakdown, It's always the same, I'm having a nervous breakdown, Drive me insane!
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• 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
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How Data-Driven Decisions REALLY work
http://dilbert.com/strips/comic/2007-05-16/
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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
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— Everyone at Blue Yonder, all the time
99.9% of all business decisions can be automated
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How Decisions are Being Made
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90% No Decision is made
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— Robin Sharma
“Making no decision is a decision. To do nothing. And nothing always brings you nowhere..”
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Business Rules for Beginners
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Business Rules for Beginners
Not doing anything is the simplest business rule in the world – and also the most popular
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90% No Decision is made
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9% Decision Follows Rule
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Business Rules in Action
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Advanced Business Rules
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Advanced Business Rules
Computers are machines following rules. This means business rules are programs.
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• 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.
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1% Human Decision making
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Human Decision Making has two systems – and only one is rational.
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Not quite Almost there That’s it.
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— Daniel Kahneman
“All of us would be better investors if we just made fewer decisions.”
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How we are making decisions (Like the big apes we are)
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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
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• 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.
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• 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.
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Computers making decisions (cold, fast, cheap, rational)
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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)
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• 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
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Better Decisions through Predictive Applications
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How Predictive Applications Work
Collect & Store Analyze Correlations
Build Decision Model
Decide & Test Optimize
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Why Test?
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— Randall Munroe
“Correlation doesn’t imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing ‘look over there’”
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Story Time
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Story Time(Not safe for vegetarians)
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The Ground Beef Dilemma
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The Ground Beef Dilemma
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The Ground Beef Dilemma
Yesterday Today Tomorrow Next Delivery Next Day
In Stock Demand
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• 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
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Challenge #1 Accurately predict demand
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1. Estimate Demand (with Probability)
0
22,5
45
67,5
90
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
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2. Account for Package Sizes
0
22,5
45
67,5
90
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
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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%
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4. Weigh Costs by Probability
0
22,5
45
67,5
90
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
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5. Aggregate Costs
0
22,5
45
67,5
90
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
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6. Find Minimum Cost
0
22,5
45
67,5
90
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
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Small Changes have Large Effects
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Small Changes have Large Effects
022,5
4567,5
90
0% 20% 40% 60% 80% 100%0
20406080
0% 20% 40% 60% 80% 100%0
20406080
0% 20% 40% 60% 80% 100%
020406080
0% 20% 40% 60% 80% 100%
020406080
0% 20% 40% 60% 80% 100%0
22,545
67,590
0% 20% 40% 60% 80% 100%0
20406080
0% 20% 40% 60% 80% 100%
020406080
0% 20% 40% 60% 80% 100%0
22,545
67,590
0% 20% 40% 60% 80% 100%
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— John Maynard Keynes
“When my information changes, I alter my conclusions. What do you do, sir?”
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Automate Replenishment
Collect Stock and Sales Predict Demand Trade Off Costs Create Orders
in ERP SystemOptimize &
Repeat
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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
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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
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Lars Trieloff @trieloff