The document provides an overview of business analytics and optimization. It discusses how analytics has evolved from descriptive analytics which examines past events to predictive analytics which forecasts future events and prescriptive analytics which recommends decisions. It also outlines IBM's business analytics portfolio and capabilities in areas like predictive modeling, optimization, and decision management. Finally, it discusses applications of analytics in various industries and functions like marketing, supply chain, finance, and operations.

1. Business Analytics and Optimization: A Technical Introduction
Oleksandr Romanko, Ph.D.
Senior Research Analyst, Risk Analytics – Business Analytics, IBM
Adjunct Professor, University of Toronto
Toronto SMAC Meetup
September 18, 2014

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Making the world work better – pioneering the science
2008
1973
1969
1981

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IBM Centennial: 100 Years of Innovation

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 Analytics Jobs

5. Created by: Dennis Buttera

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Data science
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 Business Analytics

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Predictive Analytics
What will happen?
Descriptive Analytics
What has happened?
Prescriptive Analytics What should we do?
What is analytics?
Data
Insight
Action
Decide
Analyze
Business Value
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Analytics is the scientific process of deriving insights from data in order to make decisions

10. Business analytics and optimization (video)

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IBM Business Analytics portfolio
IBM Business Analytics
Financial
Services Public Sector Distribution Industrial Communications
Customer Risk
Industry
Solutions
Finance Operations
Risk
Analytics
Business
Intelligence
Software
Categories
Predictive
Analytics
Performance
Management
Functional
Solutions
Core
Capabilities
REPORT MODEL COLLABORATE PREDICT
Budgeting &
Forecasting
Financial
Consolidation
Disclosure
Management
Risk
Identification
Risk & Control
Assessment
Resource
Optimization
Social Media
Analytics
Profitability
Modeling &
Optimization
Production
Planning
Asset
Management
Customer
Acquisition
Customer
Lifetime
Value
Customer
Loyalty
& Retention
Risk Mitigation
Planning
Risk Aware
Decisioning
Sales Performance
Management
ANALYZE PLAN
Visualize Discover
Forecast Mine
Govern
Score Decide
Simulate Contribute
Survey
Decision
Management

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Operations research
Operations Research (O.R.) is the discipline of applying advanced analytical methods to help make better decisions
Analytical techniques:
Simulation – giving you the ability to try out approaches and test ideas for improvement
Optimization – narrowing your choices to the very best when there are virtually innumerable feasible options and comparing them is difficult
Probability and Statistics – helping you measure risk, mine data to find valuable connections and insights, test conclusions, and make reliable forecasts
Mathematical Modeling – algorithms and software

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Our planet is a complex, dynamic, highly interconnected $54 Trillion system-of-systems (OECD-based analysis)
Communication
$ 3.96 Tn
Transportation
$ 6.95 Tn
Leisure / Recreation / Clothing
$ 7.80 Tn
Healthcare
$ 4.27 Tn
Food $ 4.89 Tn
Infrastructure
$ 12.54 Tn
Govt. & Safety $ 5.21 Tn
Finance $ 4.58 Tn
Electricity $ 2.94 Tn
Education
$ 1.36 Tn
Water
$ 0.13 Tn
Global system-of-systems $54 Trillion (100% of WW 2008 GDP)
Same Industry Business Support IT Systems Energy Resources Machinery Materials Trade
Legend for system inputs
Note: 1. Size of bubbles represents systems’ economic values 2. Arrows represent the strength of systems’ interaction
Source: IBV analysis based on OECD
This chart shows ‘systems‘ (not ‘industries‘)
1 Tn

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Economists estimate, that all systems carry inefficiencies of up
to $15 Tn, of which $4 Tn could be eliminated
Global economic value of
System-of-systems
$54 Trillion
100% of WW 2008 GDP
Inefficiencies $15 Trillion
28% of WW 2008 GDP
Improvement
potential
$4 Trillion
7% of WW 2008 GDP
How to read the chart:
For example, the Healthcare system‘s
value is $4,270B. It carries an estimated
inefficiency of 42%. From that level of 42%
inefficiency, economists estimate that
~34% can be eliminated (= 34% x 42%).
Source: IBM economists survey 2009; n= 480
System inefficiency as % of total
economic value
Improvement potential as
% of system inefficiency
Education
1,360
Building & Transport
Infrastructure
12,540
Healthcare
4,270
Government & Safety
5,210
Electricity
2,940
Financial
4,580
Food & Water
4,890
Transportation (Goods
& Passenger)
6,950
Leisure / Recreation
/ Clothing
7,800
Communication
3,960
Analysis of inefficiencies in the
planet‘s system-of-systems
Note: Size of the bubble indicate absolute
value of the system in USD Billions
42%
34%
This chart shows ‘systems‘ (not ‘industries‘)
15%
20%
25%
30%
35%
40%
15% 20% 25% 30% 35% 40% 45%

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History of analytics

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History of business analytics

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 Business Analytics Examples

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Pit stop analytics
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Calculations showed that time spent changing tires and refilling the tank was more
than offset by the improved performance of the car on the track.
1. Softer tires stuck to the track better during turns than their harder cousins,
though they wore out more quickly.
2. Less gas in the tank translated into a lighter, and therefore faster, car.
Optimized F1 pit teams can change four tires in two seconds

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Movies

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Smarter Cities

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We can collect information from almost everything to make better decisions
Camera phones in existence able to document accidents, damage, and crimes
1 billion
RFID tags embedded into our world and across entire ecosystems
30 billion
Of new automobiles will contain event data recorders collecting travel information
85%
Instrumented
Interconnected
Intelligent

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Big data
Big data are datasets that grow so large that they become awkward to work with using on-hand database management tools. Difficulties include capture, storage, search, sharing, analytics, and visualizing. Source: Wikipedia

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Big social data

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Applications of big data analytics
Homeland Security
Finance
Smarter Healthcare
Multi-channel sales
Telecom
Manufacturing
Traffic Control
Trading Analytics
Fraud and Risk
Log Analysis
Search Quality
Retail: Churn, NBO

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Police use analytics to reduce crime (video)

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Marketing and supply chain analytics (video)

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Marketing analytics

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Intelligent transport systems
 Real time monitoring & forecasting of congestion in cities enables real time action to
reduce traffic and emissions
– Can charge drivers at point of use for access to city centers
 Stockholm Congestion Tax Project
– Involves 18 barrier-free control points
– Allows differentiated pricing by time of day, congestion level, and potentially emissions level
– Results:
• Traffic reduced by 100,000 vehicle passages per day (25%)
• Public transportation passengers increased by 40,000 / day
• Congestion during peak hours and CO2 emissions were dramatically reduced

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Analytics for green vehicles and technology (video)

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Artificial intelligence
Source: A Brief Overview and Thoughts for Healthcare Education and Performance Improvement by Watson Team

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Artificial intelligence
Source: A Brief Overview and Thoughts for Healthcare Education and Performance Improvement by Watson Team
On 27th May 1498, Vasco da Gama landed in Kappad Beach
On 27th May 1498, Vasco da Gama landed in Kappad Beach
celebrated
May 1898
400th anniversary
arrival in
In May 1898 Portugal celebrated the 400th anniversary of this explorer’s arrival in India.
Portugal
landed in
27th May 1498
Vasco da Gama
Temporal Reasoning
Statistical Paraphrasing
GeoSpatial Reasoning
explorer
On 27th May 1498, Vasco da Gama landed in Kappad Beach
On the 27th of May 1498, Vasco da Gama landed in Kappad Beach
Kappad Beach
Para- phrases
Geo- KB
Date
Math
India
Search Far and Wide
Explore many hypotheses
Find Judge Evidence
Many inference algorithms

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Artificial intelligence (video)

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Watson Analytics

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Watson Analytics

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 Cloud

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Bluemix
www.bluemix.net

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Bluemix

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 Business Analytics Education

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IBM Academic Initiative program
Cognos SPSS ILOG

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Master of Business Analytics programs – top 20 universities

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Industry support for Master of Business Analytics programs

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Business Analytics programs – curriculum
Applied Statistics and Probability
Fundamentals of Computational Mathematics
Data Mining and Knowledge Discovery
Simulation Modelling
Optimization
Financial Decision Making
Computational Methods for Business Data Analysis
Computational Finance and Risk Management
Visual Analytics and Knowledge Representation
Mathematical Modelling for Business
Machine Learning, Cognitive Computing and Artificial Intelligence
Marketing Analytics
Strategies for Managing Innovations
Analytics of Web, Social Networks and Business News

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 Applied Statistics

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What kind of data are we dealing with?
Types of data
•Quantitative
•Categorical (ordered, unordered)
Data collection
•Independent observations (one observation per subject)
•Dependent observations (repeated observation of the same subject, relationships within groups, relationships over time or space)
Type of data drives the direction of your analysis
•How to plot
•How to summarize
•How to draw inferences and conclusions
•How to issue predictions
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Quantitative data
Examples: temperature, age, income
Quick check: “Does it makes sense to calculate an average?”
Appropriate summary statistics:
–Mean and Median
–Standard Deviation
–Percentiles
More advanced predictive methods: Regression, Time Series Analysis, …
Plot your data!
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Summarizing quantitative data
One-number summaries
–Mean Average, obtained by summing all observations and dividing by the number of obs.
–Median The center value, below and above which you will find 50% of the observations.
Summarizing your data with one number may not tell the whole story:
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Median = 19.8
Median = 19.8
Median = 10.5

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Flaw of averages
“Plans based on average assumptions are wrong on average”
Average depth 3 ft

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“Most observations fall within ±2 standard deviations of the mean.”
Standard deviation

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If the data is normally distributed
95 % of observations
Standard Deviation = 4.2
~95% of observations between 11.4 and 28.2

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Descriptive statistics - example
Random sample of 5000 customers of a credit card company
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Amount spent on primary card last month
Debt to income ratio (x100)
N
Valid
5000
5000
Missing
0
0
Mean
1683.7340
9.9578
Median
1690.0670
8.8000
Std. Deviation
210.26680
6.42317
Minimum
.00
.00
Maximum
2482.72
43.10

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Percentiles
Generalizations of the median (50th percentile).
The pth is the data point below which p percent of the observations fall.
Often used to compare a single observation to a general population.
Examples:
–Standardized test scores If you scored in the 93th percentile, your score was higher than that of 93% of test takers.
–Child growth percentiles
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Percentiles - example
Percentiles can be another way of describing how spread out data values are. Example: 5-Number Summary Minimum – 25th percentile – Median – 50th percentile - Maximum
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Amount spent on primary card last month
Debt to income ratio (x100)
Minimum
.00
.00
Percentiles
25
1567.4658
5.1250
50
1690.0670
8.8000
75
1814.5430
13.5000
Maximum
2482.72
43.10

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Distributions: Normal distribution
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Distributions
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Distributions
Estimate of the probability distribution of global mean temperature resulting from a doubling of CO2 relative to its pre-industrial value, made from 100000 simulations

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Questions?