Creating Business Value From Big Data, Analytics & Technology

Business Value Consulting for a PREDICTIVE and AGILE Enterprise
STRATEGY + ANALYTICS + TECHNOLOGY
ENABLING BIG DATA TRANSFORMATIONS FOR CONTINUOUS ADVANTAGE
™
rightedge
™
rightedge.com

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C r e a t i n g B u s i n e s s Va l u e f r o m
B i g D a t a , A n a l y t i c s & Te c h n o l o g y

AGENDA
①  Big Data Phenomena (10 mins)
②  What's Disruptive with Big Data (10 mins)
③  Cases (25 mins)
•  Battery Performance
•  Casino Gaming
④  Cases (25 mins)
•  Rail Sensor Data Analytics
•  Advertising Analytics
⑤  Foundation Series Bootcamps (15 mins)
⑥  Closing Thoughts (5 mins)
⑦  Q & A (30 mins)

5© Copyright 2013 Pivotal. All rights reserved.
‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED.
5
Big Data Phenomena

The Perfect Storm
①  LOTS OF DATA
②  COMPUTE POWER
③  MEMORY & STORAGE
④  INTERNET & CLOUD
⑤  SOCIAL+ LOC. + MOBILE

What is Big Data?
Structured
Largely
Unstructured
Semi-structured
Source: IBM and Oxford Survey: Getting Closer to Customers Tops Big Data Agenda, October 17, 2012
ü  People
ü  Machines
ü  Markets

It’s a Big Data World
Chart based on IDC and UC Berkeley Data Growth Estimates, Source: IDC & CosmoBC.com:
http://techblog.cosmobc.com/2011/08/26/data-storage-infographic/
Petabyte
PC Internet
Time
MobileMainframe
Terabyte
Data
Volume
Exabyte
Zettabyte
Machine
2011
Transactions
M 2 M
Interactions
2.0 Zettabytes in
Enterprise Data
Apps
Patterns
Information
Insights Internet of Things
Industrial Internet
U G C
Social Networks
Sales of Goods
& Services

Velocity VarietyVolume
Ability to Make Sense of Data in Real-Time To Take Action
What is Big Data Analytics?
Tens of Billions
of Events
Terabytes to
Petabytes to Exabytes
Structured
Semi-Structured
Unstructured
Binary
Business
Value
Actionable Insights
Leading To Superior
Outcomes
$
Adapted from Sources: Gartner, Cetas Analytics

Real-time Analytics
Engine
Unstructured
Structured
Semi-
structured
How To Make Sense of Data?
Transac'ons
Logs
E-‐mails

Social
Audio
Photo
&
Video

In-‐Apps
Sensors

Actionable Insights
Products

Inventory

Correlate

Predict

Recommend

•  Statistical Models
•  Machine Learning
•  Graph Algorithms
•  Key Performance Indicators
….…. ….
Ø  Volume
Ø  Velocity
Ø  Variety
Ø  Variance

Optimization
Genetic Algorithm
(Compressions)
Classification
Neural Network
(Models)
Segmentation
Machine Learning
(Clusters)
The Real-Time Engine
InsightVisualization
Dashboard
(Views)
Big Data
S + SS + US
Age
Gender
Income
…….
FB
Updates
Tweets
Real-time Business Analytics Engine
ProductA
ChannelX
OfferP
Analyst /Decision Maker
Computing @ Scale @ Speed Statistical & Machine Modeling
Data Mining Human Intelligence

12
What’s Disruptive w/ Big Data?

Why is Big Data “Disruptive”?
①  Consumerization is “exponential” producer of Unstructured Data
②  Major cultural impact just as the Industrial & Internet Revolution
③  Real-time Customer/Market Knowledge will be a Competitive Edge
④  Real-time Data-Driven Decision-making will be Mandatory
⑤  Every Business must address it or Die

Workflow
Centric
Separating Application Logic and Data
The New Business App Model
New
OLD Personalized
User Experience
Graphic Adapted From Gartner
SSO -> APPS
SSO -> DATA
Data Tightly Coupled with App
data
data
data
data

What is Changing Drastically?
Decision-Making
Process
Big Data &
Analytics
Open
Elastic IT
LoB Manager
Analyst
IT
•  Real-Time
•  Predictive
•  Closed-Loop
DATA SCIENCETECHNOLOGY
PROCESS
PEOPLE

Enabling Context Driven Decision-Making
What Businesses Need Now?
1
2
3
Predictive analytics
Real-time analytics
Investigative analytics
- Predict What is going to happen
- Know What is Happening Now
-  Analyze What & Why it Happened

Business Impact
Power of 1%
Savings Driven
by Real-Time
Decisions

18
Cases
Predicting Battery Performance
Casino Gaming Analysis

19
Predicting
Battery Performance

Forecasting vs. Prediction
Forecasting Prediction
Example Sales/Demand Forecast Likelihood of meeting forecasts
Statement about
the future
Projection or Estimate Event that is likely to happen
(probability)
Basis Assumptions about future Insights about the future
Usage For Planning in advance To take Pre-emptive action

Project Background
1.  Start-up Lithium Ion Battery Manufacturer
2.  4 Battery Models – 100-150 miles per charge
3.  First Target Use: Cars/Trucks – Racing, Commercial, Consumer
4.  Batteries in use in 1000+ Cars/Trucks
5.  Other Target Uses: Medical devices, Appliances, …

Hybrid & EV Battery Systems
All Electric
Hybrid
Rechargeable Battery Packs
Lead, Carbon, Nickel Hydride,…
Heavy
Lithium Ion Modules & Cells
Require Safety Enclosure
Lighter
~30-50 miles per gallon
~50-300 miles per charge
~$4,000+
8 yrs, 100,000 miles
~$7000+
8 yrs, 100,000 miles
48 lithium-ion modules.
Each module contains 4 lithium-ion cells (192 cells)
28 modules.
Each module contains 6 cells (168 cells)

Typical Hybrid & EV Battery Operation
Typically operates in ONE of TWO Modes :
Hybrid: High-power cycling (CS: Charge Sustaining) mode. (most common)
EV: Continuous discharge (CD: Charge Depleting) mode

Framing the “Decision-Making” Problem
①  Forecast Battery Capacity Available to the ENERGY GRID (from all vehicles)
•  Real-time Energy Supply & Demand Arbitrage ($$$$)
•  Fleet Operational Cost Optimization
②  Predict Battery Performance (for each Battery Model)
•  When is the overall system likely to fail (near to long term)
•  Which Cell/Module is not performing as it should (real-time)

Analysis & Modeling
Battery
DATA SOURCES
Electric Vehicle
Driver
Weather
Traffic
Real-Time
Streaming
Analytics
Profiles, Logs
Profiles, Logs
Profiles, Logs
Logs
Logs
Predicting
Battery Failure
Forecasting
Battery Reserve Capacity
Sensitivity Analysis
Regression Analysis
Trend Analysis
Cohort Analysis

26
Casino Gaming
Analysis

Project Background
1.  10 Casinos Evaluated
2.  50 slot machines per location
3.  5-10 Games per slot machine
4.  ~500 Players Per Casino Per Day
5.  Over 5 TB of data captured per day

Marketing Questions that Needed Answers
①  What are Potential Profitable Segment (Players) Opportunities
②  What Advertisements to Target (bring him/her to the casino)
③  What Individual Offers to Recommend (in casino to incr. playing time = spend)

Some Interesting Player Stats…
①  Ave. Time Spent by Player (at Casino Slot Machines) 5 hours (in a day)
②  Ave. Spend (Slot Machine) by Player ~$100 /day
③  Ave. # of Slot Machines Played (in a Casino) 3

Casino Gaming Analysis & Modeling
Casino
DATA SOURCES
Slot Machines
Games
Player
Analytics
Profiles, Logs
Profiles, Logs
Profiles, Logs
Game, Logs
Recommended Relevant
Individual Offers (in-game)
Identified 8 Potential (Player)
Segments to Target
(Behavioral + Psychographic)
Cohort Behavior Analysis
RFM Analysis
Multivariate Analysis
Cluster Analysis
Rewards Program
Play (Win/Loss) History
Referrals
Spend Patterns
Play Patterns
Real-time
Exploratory

31
Cases
Railroad Sensor Data Analytics
Predictive Advertising Analytics

32
Foundation Series Bootcamps

Why You Should Care?
Prepares YOU to understand, lead and drive Big Data Transformations in whatever ROLE you are in.
Broaden your thinking (from silos), Align with Data-Driven Decision-Making, Develop NEW Skills
THREE 1-day Bootcamps (in recommended order)
1.  Decision Maker Lens (POV) Learn how data-driven decisions are made using business frameworks
2.  Business Analyst Lens (POV) Know how data & analytical models are used in decisions
3.  Technologist Lens (POV( Understanding how Big Data Technologies enables data-driven decision-making
Goal is to make YOU understand how data-driven decision-making impacts business value in your organization or your
customer’s organization.
Provides YOU with the knowledge, mindset and practical tools

Practical Learning Objectives
This FOUNDATION SERIES program encourages YOU to apply the insights and best practices, learnt, in
the context of your own organization (or your customers) including (but not limited to):
①  Define problems & solutions that create business value from the application of big data & analytics
②  Brainstorm sources & variety of data, use of statistical and machine learning models, Collective wisdom
③  Design Experiments to collect and analyze data in creative ways to optimize business value.

Content Focus
This unique FOUNDATION SERIES program blends
①  Custom Curriculum (for corporate training)
②  Domain Specific Business frameworks, KPIs
③  Use case Examples, mini-cases, case studies, and
④  Brainstorming discussions
⑤  Check Lists (Questions to Ask)
Participants learn how businesses use big data & analytics for decision-making
effectively in critical functional areas such as strategy, customer support, sales,
marketing, supply chain and IT.

Tied Together by F.A.I.T.H™ Methodology
F
A
I
T
H
Framing the business problem, formulating biz case, strategizing on scenarios
Analysis & Modeling of the business problem with KVBI™, Relevant Data
Insights Extraction, Interpretation and Validation
Timely Action & Visual Reporting (using Technology)
Harvesting Yield & KPI Monitoring for Closed Loop Feedback

Get F.A.I.T.H™ Certified
Strategy + Analytics + Technology = Business Value
F A I T H
CONSISTENT. ITERATIVE. REPEATABLE. CLOSED-LOOP.
Create, Grow, Build Data-Driven Decision-Making Mindsets

Sample Case: Starbucks
Starbucks
Starbucks wants to expand in Brazil in 2014.
Wants to be Profitable in Year 1 of Expansion
and Triple market share by Year 3.
Your Team is asked to present an evidence based (data-driven)
Market Expansion Strategy Recommendation
Present in
40 mins
Ilustrative

Mini Case: Starbucks
Starbucks
1.  Who (think roles) would you want on your team ?
2.  List questions that needs answers (from data or otherwise)
3.  List Data Sources and Attributes you will need, use
4.  Identify Key Business Value Indicators (KVBI™) that will indicate profitability
5.  Determine Analytical Models to Use
6.  Identify Technology Infrastructure needed to support Strategy
Illustrative
Decision Making Task List

Competitive Analysis
THINK ABOUT…
Data (Available or not) that could enable
your understanding of the 5 forces
•  Data Sources (Internal, External)
•  Data Attributes (Dimensions)
Models that could surface insights on
competitive position
•  Prediction Models
•  Recommendation Models
Starbucks
Illustrative

THINK ABOUT…
Data (Available or not) that could inform
your STP (Segmentation, Targeting,
Positioning)
•  Data Sources (Internal, External)
•  Data Attributes (Dimensions)
Models that could surface insights on
marketing mix (relative to self, competition)
•  Prediction Models
•  Recommendation Models
Marketing Strategy
Starbucks
Illustrative

Bootcamp #1: Introduction to Data-Driven Decision-Making
Every decision involves making assumptions about uncertainty and risk.
Big Data & Analytics are transforming how decisions are made in every enterprise, from the start-up to the Global enterprise,
to reduce uncertainty & risk. So every professional or employee in any company must understand how line of business (LoB)
executives and managers make decisions in different departments - Strategy, HR, Marketing, Finance, Supply chain, IT and
more.
This Bootcamp is intended to give you a foundation on the business decision frameworks typically used in different
functional areas by decision-makers. You also learn how decision frameworks are applied across different verticl
business contexts and use cases.
1Decision Maker LensDEVELOP BUSINESS SENSE

Bootcamp #2: Introduction to Business Analytics
As companies are inundated with large volumes, variety, and velocity of data the need to use real-time, batch and
interactive forms of business analytics is becoming critical. Typically the business analyst and/or the data scientist is
responsible for creating analytical models on the data for LoB decision-makers to use for making informed decisions.
However, it is in the best interests of every professional and employee to get a fundamental understanding of the application
of business analytics in different functional areas.
This bootcamp is intended to provide a foundation on the application of business analytic models typically used in
functional areas such as strategy, marketing, finance, supply chain, IT, customer support, and more.
2Business Analyst LensDEVELOP ANALYTICAL SENSE

Bootcamp #3: Introduction to Big Data Infrastructure
Interestingly, Big Data is both hype and reality. However, every CXO, Senior Executives, LoB Managers, and even IT
must have a fundamental understanding of what the key Big Data technologies are and how they could enable business
value. This understanding is crucial to make the right investments that will create, generate, drive, and optimize business
value and a competitive advantage.
This bootcamp is intended to provide a foundation on the key Big Data technologies to invest in today and for the
future to become a real-time (agile) and predictive enterprise.
3Technologist LensDEVELOP TECHNOLOGY SENSE

Top 10 Bootcamp Takeaways For YOU
①  Build Data-Driven Mindset with F.A.I.T.H – Consistent, Iterative, Repeatable, Closed-Loop System
②  How data-driven decisions are made using well-know strategy & analysis frameworks
③  What kinds and types of data & analytic models are potentially used in decision-making
④  What & How key technologies and applications are driving the big data revolution
⑤  Common challenges & pitfalls in using big data & analytics
⑥  Design controlled experiments to distinguish causality from correlation
⑦  Mini-cases, case studies & examples from strategy, marketing, supply chain, IT and other applications
⑧  Recognize application opportunities in your own department, industry or function
⑨  Identify organizational and cultural enablers & barriers to data-driven decision-making
⑩  Importance of customer privacy and data ownership (in the context of your role)

46
Closing Thoughts

REMEMBER What is Changing Drastically?
Decision-Making
Process
Big Data &
Analytics
Open
Elastic IT
LoB Manager
Analyst
IT
•  Real-Time
•  Predictive
•  Closed-Loop
DATA SCIENCETECHNOLOGY
PROCESS
PEOPLE

Key Decision Areas (Driven by Big Data)
①  Customer Intelligence
②  Segmentation
③  Prediction and Recommendation
④  Dynamic Product Development & Innovation
⑤  Sensor Network Intelligence

Analytical Thoughts
①  Larger the Data Set Better the Prediction
②  Variety of Data = Richer, Deeper Insights
③  Trusting Predictions from Data Science
④  Real-time Segmentation & Targeting is non-trivial
⑤  Volume + Variety = Better Segmentation & Targeting
⑥  Human Intelligence Required to Pick Segments to Target!

Thank You!
Balu Rajagopal
balu@rightedge.com
Questions ? Comments ?
Please Email Me.

51
Q & A

CNBC: Rise of the Machines
http://www.hulu.com/watch/536745
Segment 2

Creating Business Value From Big Data, Analytics & Technology

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