Five Trends in Analytics - How to Take Advantage Today - StampedeCon 2013

Five Key Trends in Business Analytics
How to Take Advantage Today
StampedeCon 2013
St. Louis, MO
July 30, 2013
John Lucker, Principal
Global Advanced Analytics & Modeling Market Leader
JLucker@Deloitte.com - Twitter: @JohnLucker
Deloitte Consulting LLP

- 2 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved.
About Today’s Speaker – John Lucker
John Lucker
Principal
Deloitte Consulting
(860) 725-3022
JLucker@deloitte.com
•  Deloitte’s Global Advanced Analytics and Modeling Market
Leader and a leader of the Deloitte Analytics Institute
•  Provides clients with end-to-end strategy, business, operational,
and technical consulting services in the areas of advanced
business analytics, predictive modeling, data mining, scoring
and rules engines, and numerous other advanced analytics
business solution approaches. His clients are in many
industries including insurance, banking and financial services,
retail, consumer products, telecomm, healthcare, life sciences,
media, hospitality and others.
•  Author of more than 50 articles and papers on a variety of
advanced analytics, predictive modeling and analytic business
and technology topics
•  Speaker at numerous global conferences on a variety of
analytics and business solutions topics and published in a
number of journals and professional publications.
•  Co-inventor of US Patent 8,036,919 for a “Licensed Professional
Scoring System and Method” and US Patent 8,145,507 for a
“Commercial Insurance Scoring System and Method” and US
Patent 8,200,511 for a “Method & System for Determining the
Importance of Individual Variables in a Statistical Model” and
two pending patents
•  Education; University of Rochester – B.A.; University of
Rochester – Simon School – M.B.A.

Today’s Agenda – 5 Key Trends in Business Analytics
1.  Behavioral Economics
2.  Big Data Pragmatism
3.  Data Visualization
4.  Mobile Analytics
5.  Strategic Analytics

Analytics Helps Create a More Fact Based vs Gut Based Culture
The problem is not that baseball professionals are stupid; it is that they
are human. Like most people, including experts, they tend to rely on
simple rules of thumb, on traditions, on habits, on what other experts
seem to believe. -- Cass Sunstein & Richard Thaler review of Moneyball
The most difficult subjects can be explained to the most slow-witted man
if he has not formed any idea of them already; but the simplest thing
cannot be made clear to the most intelligent man if he is firmly
persuaded that he knows already, without a shadow of doubt, what is
laid before him.” – Leo Tolstoy

Cognitive Bias In Action – Availability Heuristic
Every summer – Watch Out for Sharks!
However:
“It is more likely you will be killed by slipping on a wet floor”
“During a 342 year period from 1670 to 2013 there were 1,022 US shark attacks”
“During the same 342 year period there were only 36 fatal US shark attacks”
Source: http://www.flmnh.ufl.edu/fish/sharks/statistics/GAttack/mapusa.htm

Cognitive Bias In Action – Life Insurance Applications
When applying for life insurance, do you engage in “risky activities”
Cause of Death Odds of Dying (1 in)
Swimming 56,587
Cycling 92,325
Running 97,455
Skydiving 101,083
Soccer 103,187
Hang-Gliding 116,000
Tennis 116,945
Marathon Running 126,626
Scuba Diving 200,000
Ping-Pong 250,597
Rock Climbing 320,000
Source: http://www.medicine.ox.ac.uk/bandolier/booth/Risk/sports.html

Cognitive Bias In Action – Segment of One? – Sunk Cost Processes
Fact:
I haven’t obtained a new credit card in 23 years.
Question:
Over the past 30 months, I have received 140+
credit card offers from just two banks?
Typical Response:
We are very successful doing things as we
have evolved them. We have made significant
investments in customer segmentation. You are
an exception and this doesn’t happen to most
people.

Cognitive Bias Examples from my Consulting Experience
Examples
•  Comment from a Financial Services executive in a pricing meeting: “I recommend
we increase interest rates (APR) on our accessory motor lending products by 30
basis points this year because this summer will be very hot and the demand for
motor “toys” will increase”.
•  How does the executive know what the weather will be like in a few months? Can
she be sure that demand for motor “toys” will go up?
•  During a price optimization exercise a food retailer decided not to price different
flavors of a product differently despite empirical evidence that some flavors were
significantly more sensitive to price. They (wrongly) discussed that other food
products were not priced that way. By pricing all flavors the same the company was
unable to obtain a gross margin benefit of more than $4 million in the first year.
•  Comment from a Retail executive: “we don’t care as much about the needs of men
in our catalog and stores” because 80% of our customers are women.
•  But what if sales are saturated for women and there could be a latent opportunity to
sell more to men? If the infrastructure is there to sell to men (since 20% of sales is to
men) then why not see if there is a way to increase male traffic to stores and
catalog?

Behavioral Economics & Cognitive Biases – Redefining Success

What Is ‘Big Data’?
•  Some people think of BD as anything that doesn’t fit in an Excel spreadsheet!
•  In some simple contexts, BD is used to refer to any form of advanced statistical
analytics or predictive modeling using diverse internal/external data sources
•  In many contexts, BD is shorthand for the granular and numerous data sources
used for analytics projects - big, small, old, new, structured, unstructured, etc.
•  In more esoteric data contexts, some examples include geospatial, social/
sentiment, audio/video, mobile information, telematics, telephonic, internet
searching, web logs, etc.
Big Data refers to internal and external data that is multi-
structured, generated from diverse sources in real-time and
in large volumes making it beyond the ability of traditional
technology to capture, manage and process within a
tolerable amount of elapsed time

Big Data – How Much Data is Generated Every Minute?
•  It isn’t just the rate of creation of data that’s increasing, it’s consumption rate too
•  This chart also shows one Big Data’s important facets – much of it is unstructured
•  If you added up all the data we created or replicated last year, converted it to text
and printed it as paperback-sized books, you’d get a pile stretching from the Earth
to Pluto and back more than 16 times

Big Data, Big Noise – But Perhaps Not Enough Focus on Strategy & Value
Understanding Big Data as completely as possible and clearly identifying its links to various
value levers is critical before designing any solutions - Value, Strategy, Decisions,
Analytics, Execution, Assets
InformationDemandInformationSupply
Value-driven
Strategies
Value Chain
Execution
Information
Assets
Data Warehouse
Insights,
Analytics &
Measurement
Value Drivers
Improvement Levers
Data Sources
Decisions
Governance &
Management

Big Data’s Potential for High Return on Investment
Big Data budget and investments are set to increase in the coming years although the returns
on investments on Big Data investment are yet to be established,
15
Source: The Big Returns from Big Data, and Big Data Hits the Jackpot, Nucleus Research, April 2013; Big Data Executive
Survey, NewVantage Partners, 31 December 2013
•  According to Nucleus Research, organizations can earn an incremental ROI of 241% by using Big Data
capabilities
•  Nucleus research cites increased business adoption is improved by business processes, efficiencies
and decisions as one of the drivers and the ability to monitor the factors that impact a company, such as
customer sentiment across customer segments – End-To-End Implementation and Integration
•  In November 2013, alone, venture capital, and private equity invested $180 million in Big Data and analytics
vendors
24%
36%
48%
36%
24% 22%
5% 6%
Today 3 Years from now
Less than $100,000 $100,000 - $1 Million
$1 Million - $10 Million More than $10 Million
Budget for big data

The Importance of the Big Data Trend & How to Capitalize On It
16
Source: British Retail Consortium (BRC) Analysis
While Big Data has driven IT spending, hype should not drive the attention away
from data quality, relevance, redundancy, and most importantly, insights
Data Quality
Data cleaning might
take more effort than
data analysis. The
magnitude of bad data
will get amplified with
the increase in type of
sources, and volume
of data – but keep
80:20 Rule in mind
Interpretability
Substantial amount of
data is unstructured
and hence liable to
different interpretation
by different people
and machines
Business decisions
stand the risk of being
based on biased
interpretations
Relevance
The amount of data
stored will have
contextual relevance
with individuals
analyzing the data.
‘One Man’s Signal is
another Man’s Noise’
Privacy Issues
As organizations capture
more volumes of data
from various sources,
they are more susceptible
to disturbing privacy
concerns
Especially as more and
more consumer data is
being used, organizations
will have to be sensitive
about the data they use
Redundancy
There are chances,
organizations are
capturing same data
from multiple sources,
multiple times
e.g., Tweets, updates
Organizations should
be wary they are not
investing in capturing
one data point from
multiple sources
Novelty
Most of the time, a lot of
data captured from Big
Data sources is already
captured in existing data
available with the
enterprise
Big Data investments
should focus on finding
new insights
Dilutes Value
Focus
With Big Data hype, a lot
of attention is going into
collection, storage, and
access of Big Data
This has diverted
attention from analysis
and ultimate use of data
Knowing what
organizations want to do
with the data might also
be an important question
to consider
Avoid “Hoarding”
Complex
Big Data is still confusing
to many professionals
After the hype subsides,
efforts into making it less
complex, and user
friendly should ensue

Big Data Potential Roadmap
A Big Data Roadmap begins with the decision makers and their strategic “crunchy” questions and then
proceeds to the data sources and technologies that are required to address the needs.
Identify
Opportunities
Assess Current
Capabilities
Identify and Define
Use Cases
Implement Pilots and
Prototypes
Adopt strategic ones in
Production
§  Identify strategic priorities
and ask crunchy questions
§  Assess
§  Data and application landscape including archives
§  Analytics and BI capabilities including skills
§  Assess new technology adoptions
§  IT strategy, priorities, policies, budget and investments
§  Current projects
§  Current data, analytics and BI problems
§  Based on the assessments and
business priorities identify and
prioritize big data use cases
§  Identify tools, technologies and
processes to implement pilots.
Define and compute metrics on
incremental value added
§  Prioritize and
implement
successful, high
value initiatives in
production
1
2
3
4
5

What’s Important About Data Visualization Capabilities?
•  What’s the business problem to be solved?
•  Presents results of sophisticated analysis on large and complex data
sets in understandable & actionable formats
•  Makes insights accessible to a much broader audience based on user
experience, appetite, aptitude, ineptitude
•  Helps allocate the scarcest resource a decision maker has: attention
•  Increases communications impact with key stakeholders
(C-Suite, Boards, media, analysts, customers, etc.)
•  Know Your Audience - is not for everyone – should allow easy
interaction with data for those who want to dive deeper
•  Capability requires appropriate information architecture, governance,
security, software, staffing, training

Visualization Possibilities are Limited Only by Imagination
Streamgraph Force-directed graphs Tree Maps Sunburst
Word Tag Cloud Bubble Chart Many Eye Bubble Chart Time Series Analysis
Geospatial Parallel chord Calendar View Heat Maps

Some Visualization Examples: SOMs
1.  SOMs help make sense of high
dimensional and complex data
2.  A SOM places a high number of
variables into a 2-D visual map where
similar observations are plotted next to
each other and grouped into segments.
Here is a portion of a SOM where 700
variables describing purchase patterns
from 10,000 stores have been analyzed
to identify 46 segments.
3.  Overlays from variables of interest
identify segments of opportunity. Here
S17 was found to be an untouched
segment.

Some Visualization Examples: Network Relationship Maps
Each doctor is a blue
circle whose size is
proportional to the
amount of drugs
prescribed.
Doctors are linked if
they share an
organizational
affiliation or have
common patients.
The thickness of the
edge is proportional to
the number of shared
patients.
The three red dots are individuals thought to be key influencers.
This network graph shows that others may be equally or more influential.

Some Visualization Examples: Pricing Waterfalls
•  Highlights which cost-to-serve elements can be reduced in order to
keep a larger portion of the list price
•  Visually allows sales reps to determine what elements they can adjust
in negotiations with a customer
List
Price
$6.00
Order Size
Discount Competitive
Discount
$5.78
Invoice
Price
Payment
Terms
Discount
Annual
Volume
Bonus Off-Invoice
Promotions
$4.47
Pocket
Price
Co-op
Advertising Freight
$0.10 $0.12
$0.30
$0.37
$0.35
$0.20
$0.09
25.5%
off list!

Some Visualization Examples: Infographics
http://www.youtube.com/watch?v=g5rGm6veAhg&feature=player_embedded&safety_mode=true&persist_safety_mode=1#!

Some Visualization Examples: Heat Maps

Should be a Governance Process for Visualization Pragmatism
•  Stay alert for GIGO – try numerous approaches – fail often, learn fast
•  Part Science / Part Art – need staff with refined right / left brains
•  Tufte Type Rules – simplicity, color, proportion, substance, etc.
•  Data Preparation – data needs to be in shape for visualization
•  Limit Tool Investment – no requirement for big dollars
•  Borrow Ideas – numerous examples and canned visualizations
•  Avoid Excessive Diversity – many visualization types can doom effort
•  Know Your Viewer – understand absorption / learning styles
•  Eye on Value – as with any effort, look for ways to articulate value

Must Pay Attention to the Mobilization of Analytics
•  Shift access to insight from
“anytime anywhere” to “every time
everywhere”
•  Enable real-time collaboration and
fact-based decision-making
•  Improve field sales and service
performance
•  Drive more effective customer, market,
employee interactions
•  Point: If I can make stock market trades
from my smartphone or tablet any time
and anywhere, I should be able to
perform analytics too.

Mobile Analytics Pragmatism
§  Integrate with overall analytics infrastructure
§  Enable application building across popular mobile platforms
§  Connect to back-end data sources made of internal, external,
synthetic data sources
§  Use native mobile device behaviors that all are familiar with
§  Customize by role – e.g. executives won’t have the same
requirements as salespeople, technicians, or managers
§  Cultural considerations:
§  Use a business-driven value roadmap to define success
§  Constantly monitor implementation to meet business needs
§  Be sure executives get distilled insight, not just mountains of data

The “Crunchy Question” Approach to Realizing Analytic Value
Crunchy questions are practical, detailed inquiries into tough business issues—
roll-up-your-sleeves questions. Crunchy questions are designed to lay the
groundwork for action
Why Crunchy Questions?
They crystalize the business challenges/issues that are at the heart
of any Business Analytics Initiative
Characteristics of Crunchy Questions
§  Specific
§  Relate to a particular business process and aligned with strategic
goals
§  Focus on optimizing or innovating, not merely informing
§  Consider change relative to other indicators or processes
§  Leverage and integrate internal and external inputs
§  More forward or inward looking than backward looking
§  More about differentiation than just comparison
§  Consider various scenarios
§  Actionable, i.e. more about “do it” than “prove it”
§  Require advanced tools and techniques to answer
“If its not at the heart of
the business, it doesn’t
count”

Realizing Value from Big Data & Analytics is an End-To-End Process
End-to-End
Business
Value
1. Business
Strategy
2. Big Data
Analytics
3. Biz & Ops
Implement &
Integration
4.
Technology
Integration
5. Org &
Change
Management
6.
Performance
Management

Some Big Data Reading For You
Email Me At: JLUCKER@DELOITTE.COM and I will send these to you

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