5. Big Data is what you call data
when it becomes hard to deal
with.
6. — widely quoted, still questionable
“The volume of business data
worldwide doubles every 1.2
years, according to estimates.”
7. What about Moore’s law? I’ve
heard computers get twice as
fast every 18 month.
8. — Gordon Moore, 1965
“The complexity for minimum component
costs has increased at a rate of roughly a
factor of two per year. Certainly over the short
term this rate can be expected to continue, if
not to increase. Over the longer term, the rate
of increase is a bit more uncertain, although
there is no reason to believe it will not remain
nearly constant for at least 10 years.”
9. CPUs are not getting much
faster anymore
But they still get
smaller, cheaper, less
power-hungry, allow
more parallel
operations, etc.
Why can’t we use this?
10. Big Data is just data that is too
big for your computer.
11. If computers are getting so
small and cheap, can’t we just
use more computers?
28. • 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
29. 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
30. — Everyone at Blue Yonder, all the time
99.9% of all business decisions
can be automated
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.
51. — Daniel Kahneman
“All of us would be better
investors if we just made fewer
decisions.”
52.
53. 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
54.
55. 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)
56. • 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
65. Hierarchical Model
Basic Demand per
Store and Publication
Weekly
Sales
Publication
Name
# of
Customers
Geo-
Location
Month
Type of
Store
Demo-
graphics
Demand Forecast
Month
Last year’s
sales
Recent
sales
Promotion
71. 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
72. — John Maynard Keynes
“When my information
changes, I alter my conclusions.
What do you do, sir?”
73. One Common Platform for
Predictive Applications
Multi-Tenant Runtime Environment
Link Store Build Run View
Link your own and
third-party data, easily
integrated via API
Store your data in
high-performance
database as a service
Build machine
learning and
application code
Automatically run
and scale ML models
and applications
Monitor and inspect
resource usage and
model quality
Secure Micro Cloud Infrastructure
Domain Model Predictive Model Application Code
74. — Kevin Kelly
“The business plans of the next
10,000 startups are easy to
forecast: Take X and add AI”
75. How Enterprises adopt
Predictive Applications
Learn about
ADDD
Define Target
Process
Build
Predictive App
Go Live
Make Lots of
Money
77. How Enterprises REALLY adopt
Predictive Applications
Learn about
ADDD
Define Target
Process
Build
Predictive App
Make Lots of
Money
D O U BT S
CO N C E R N S
O B J E C T I O N S
