creating business value from big data, analytics & technology

Business Value Consul t ing for a PREDICTIVE and AGILE Enterpr ise

STRATEGY + ANALYTICS + TECHNOLOGY

ENABLING BIG DATA TRANSFORMATIONS FOR CONTINUOUS ADVANTAGE ™

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Material cannot be reproduced or distributed in any

form without express written permission.


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 Mobile Mainframe

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 Variety Volume

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

Insight Visualization

Dashboard

(Views) Big Data

S + SS + US

Age Gender

Income

…….

FB

Updates Tweets

Real-time Business Analytics Engine

Prod

uct A

Cha

nnel

X

Offe

r P

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 SCIENCE TECHNOLOGY

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

•  Statistical Models •  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)

•  Statistical Models •  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.

1 Decision Maker Lens DEVELOP 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.

2 Business Analyst Lens DEVELOP 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.

3 Technologist Lens DEVELOP 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 SCIENCE TECHNOLOGY

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 [email protected]

Questions ? Comments ?

Please Email Me.




51

Q & A


CNBC: Rise of the Machines

http://www.hulu.com/watch/536745

Segment 2