The document discusses an upcoming webinar hosted by the Financial Services Industry User Group (FSIUG) on turning data into dollars in the era of big data. The webinar will feature Jaime Fitzgerald, founder of Fitzgerald Analytics, and will cover how to avoid common pitfalls of managing large data volumes, leverage big data opportunities, and generate ROI from big data initiatives. The webinar agenda includes introductions from the FSIUG president and an education specialist, followed by Fitzgerald's presentation and a question and answer session.

1. FSIUG WEBINAR
Jaime Fitzgerald, Founder & President of Fitzgerald Analytics
Turning Data to Dollars™ in the Era of "Big Data":
How to avoid common pitfalls of managing large
volumes of data, sidestep "big data hype," and
capitalize on new opportunities
Date: April 18, 2012
Time: 2:00 – 3:00 EST
More and more technologists are getting excited about "Big Data", which they often define as
having greater volume, greater variety, and greater velocity than traditional data assets.
Although "Big Data" has great potential to spur innovation, the enabling technology and
analytics create new challenges and risks. Organizations are investing significant time and
money in "Big Data" strategies, tactics, teams and tools. Yet, despite the hype, most "Big Data"
initiatives have not generated concrete and positive ROI.

2. • Message from our
President, Rich
Bouthilette
• Message from our
Today’s Agenda
Quest Education
Specialist, Jenn Abney
• Webinar
• Q&A

3. The Financial Services Industry User Group
(FSIUG)
• Quest Affliated ‐ Independent User Group
• Comprised of Financial Services Instutions that
have licensed an Oracle ERP product
• Main Purpose: Provide ways for members to
share implementation strategies and product
experiences and help them shorten the
learning curve related to maximizing their ERP
platform

4. Recent Activities
• User Group meetings at various conferences
such as Collaborate and Open World
• Held a successful Financial Services Industry
Symposium last summer at Adelphi University
in Long Island
• Had a Kiosk at Oracle’s Financial Services
Industry Meeting in February in NYC

5. Upcoming Plans
• Lunch and Learns
– Suggestions for topics?
• Webinars
• Financial Services Industry track at Reconnect
– Peoplesoft Product focused event happening in late
August in Hartford, CT
– Submit FSI related abstracts
– Send abstracts or ideas to me of what you want to
hear: Richard Bouthillette rbouthillette@amica.com
800/652‐6422 x24037

6. Reconnect
• Jennifer Abney, Education Specialist, Quest
International User Group

7. August 27‐29, 2012
Connecticut Convention Center
Hartford, Connecticut USA
QuestDirect.org/RECONNECT
• PeopleSoft RECONNECT is a new PeopleSoft-focused
event, replacing our Regional events. This new event will offer in-
depth education into PeopleSoft product modules in a way that
isn’t possible at COLLABORATE due to space limitations.
• What content will be available?
o Granular content within PeopleSoft modules like:
o HCM
o Financials
o Supply Chain
o Tools & Technology
o Upgrades
o Enhancement discussions with Oracle development and
support.
o SIG meetings around the featured product modules.

8. Turning Data to Dollars™ in the Era of
"Big Data"
• Jaime Fitzgerald, Founder and President,
Fitzgerald Analytics
April 18, 2012
Architects of Fact‐Based Decisions™

9. Nice to Meet You!
Data to Dollars™ specialist.
Creator of a structured methodology and
toolkit to accomplish this.
Will share further at Reconnect!
• Key Mission is to
Find & unlock opportunities
via data, technology, people, + processes.
Principles:
Jaime Fitzgerald
@jfitzgerald “Begin with the End in Mind” (Covey)
“Quality is Free” (McGregor)

10. Table of Contents
Introduction
1. Big Data… Big Results?
2. Data to Dollars™
3. Implications of Big Data
4. Key Takeaways and Questions

11. Transforming Data to Dollars™
It’s a journey…
1 2
Small Data
Big Data
Product of Alberta
3
Really Big Data
Product of everywhere

12. Defining Big Data: “Three Vs”
"Big Data“ is often defined as data with:
greater volume…
greater variety…
and/or
greater velocity….

13. Another Way to Define “Big Data”
What are the optimal methods to accomplish your goal?
Traditional approaches Big‐data approaches
• Centralized • Distributed
Data storage • Relational DBs (tables) • Non‐relational DBs (key‐value pairs)
Data access • SQL queries • Map‐reduce and custom algorithms
• Centralized • Distributed
Data analysis • Standardized analytics • Custom analytics
• MS SQL Server • Hadoop
• Oracle • BigTable
Typical tools • Tableau • Riak
• Excel pivot tables • Amazon S3
Note that this definition hinges on methods applied, not on dataset sizes:
800GB Can Be 80GB Can Be
“Traditional” “Big Data”

14. My Perspective Towards “Big Data”
Skeptical (of the hype)…
….yet
Cautiously Optimistic!
Big Data
Product of Alberta

15. Big Data Hype – Does is Cause a Problem?
“Data is the New Oil” – World Economic Forum Report

16. The Potential is Real…It’s Just Not Easy to Get

17. Table of Contents
Introduction
1. Big Data… Big Results?
2. Data to Dollars™
3. Implications of Big Data
4. Key Takeaways and Questions

18. Will Big Data Unlock Big Results?
• It depends…
• ...on the
principles you
work by.
Stephen Covey

19. Beginning with the End in Mind
1. Your Goal
2. Insight You Need
3. Analytic Methods
4. Data You Need
5. Tools, Platforms, Technology,
People, and Processes

20. “A Journey of a Thousand Miles….”
2
1
Fitzgerald Analytics: Converting Data to Dollars™
Better Data Better Analysis Better Results
3
Worth The Trip!

21. Key Steps in the Journey to Results
1. Data 2. Analytics 3. Results
 Data Governance  Better Decisions
Analysis Insight
 Data Management  Better Processes
 Data Quality  More Customers
 New Data Source  Happier Customers
Acquisition

22. Table of Contents
Introduction
1. Big Data… Big Results?
2. Data to Dollars™
3. Implications of Big Data
4. Key Takeaways and Questions

23. Simplify Your Analytic Process via “Causal Clarity”
• …Clearly defining “Cause and Effect” is the most crucial enabler of analysis
that is simple, efficient and high impact.
1 2 3
Define Define Business Define
Goal Model Causality
Inputs  Usually net profit  Products / services  Aka “drivers tree”
 Can be anything!:  How sold / how  Makes the causal
– Marketing ROI delivered model visual
– Non‐profit impact  To what customers
– Customer  At what price
satisfaction  Cost structure (fixed vs.
– Etc. variable)
 Known KPIs and
rationale for them

24. Here’s a Simple Example
• A simple example…
Volume . . .
Revenues
Price . . .
Profit
COGS . . .
Costs
SG&A . . .

25. Causality Flow and Strategy Planning
• Causality flow and strategy planning move in opposite directions…
Causality flows this way…
Volume . . .
Revenues
Price . . .
Profit
COGS . . .
Costs
SG&A . . .
… but strategy is best developed in this direction (“Beginning with the End in Mind”)

26. “Causal Clarity”
• If cause and effect are clear, practical analytics becomes feasible
1. Drivers of 2. Optimized by 3. Unlocking
Results… Analysis & Data… Better Results
Better Revenue
Key Decisions
Decisions
Costs
Risks
Key Business Better
Processes Processes Profit
Causes Effects

27. Causal Models: A Simple “Base Case”
• Each business model has an inherent “causal model,” but the
“core branches” are similar
Example: Drivers
of Net Profit
Revenue
less
Your Has Cost of Revenue Gross Profit
Business Operating Costs
Model less Net Profit
Marketing Other Costs
Overhead
Other

28. A Point of Opportunity
Here is an opportunity to enhance ROI on Marketing + Sales efforts:
Point of Opportunity: “Efficiency of New Client Acquisition”
Key Driver / KPI: Acquisition Cost per New Client
Formula: [spending on new client marketing]/[# New Clients)
Transactions
per Client Price per Txn
X
# of Clients Volume
Sales and
Marketing

29. Types of Questions Analytics May Answer
We are about to get practical, let’s keep the following in mind…
Past Present Future
What happened? What is happening What will happen?
Information now?
(Reporting) (Alerts) (Extrapolation)
What’s the
How and why What’s the next best best/worst that
did it happen? action? can happen?
Insight
(Modeling, (Recommendation) (Prediction,
experimental optimization,
design) simulation)
Source: Tom Davenport in “Analytics at Work”, Harvard Business School Press

30. What We Need to Get Practical
• To get practical about analytics, we need three things…
What We Need Definition
1. Causal Clarity re: Your  How You Make Money
Business Model  Key Drivers of Results
2. Definition of Your Points of  Gaps vs. Potential
Opportunity  Opportunities Recognized
3. A Plan to Capture the  Insight You Need
Opportunity  Method to Get It

31. Planning Your Analysis
1. Your Goal = “Point of Opportunity”
2. Insight You Need
3. Analytic Methods
4. Data You Need
5. Tools, Platforms, Technology,
People, and Processes

32. Choosing Analytic Methods
Selecting the right analytic method is a key success factor. Consider
the logic below…
1. Your Goals
Analytic
2. Types of Info Informs Method
you Need
3. Information
Available

33. Table of Contents
Introduction
1. Big Data… Big Results?
2. Customer Profitability Analysis
3. Implications of Big Data
4. Conclusion and Questions

34. What does “Big Data” change?
1. Your Goal = “Point of Opportunity”
2. Insight You Need
3. Analytic Methods
Big Data
4. Data You Need Changes
These
Steps...
5. Tools, Platforms, Technology,
People, and Processes

35. Big‐Data Approaches and Tools Make Data Analysis
 Possible, for very large data sets that cannot be handled at all
with typical relational databases.
 Faster, for large data sets that can be handled with typical
relational databases, but doing so would take a long time. This
is the situation in the example above.
 Cheaper, for large data sets that can be handled with typical
relational databases, but doing so would be very expensive.

36. Big Data Allows Us To Work with Large Datasets
• We can analyze datasets larger than ever before
For a given desired speed of analysis…
Beyond a certain point, conventional
methods just aren’t feasible –
Google couldn’t run on a relational DB
IT Costs
For larger datasets, big‐data
methods make more sense
Dataset size
For smaller datasets,
conventional methods are
more cost‐effective Traditional Big‐data
methods methods

37. Big Data Allows Us To Get Results Faster
• We can get results faster than ever before
For a given dataset size…
IT Costs
SLOW FAST Analysis speed
Conventional Big‐data
methods methods

38. Table of Contents
Introduction
1. Big Data… Big Results?
2. Customer Profitability Analysis
3. Implications of Big Data
4. Conclusion and Questions

39. Example: Iterative Customer Profitability Enhancement
Build/Maintain Customer Take Smarter Actions w/ Customers
Profitability Models:  Target: Who?
• Create consistent message
 • Message or action: What?
Target action to individuals
 Identify costs & revenues
• Optimize product / service
 Build profiles Data  Offering: Product design
portfolio
Warehouse  Service: How delivered?
 Integrate data from
“new” sources (how experienced by customer?)
External New Customer Knowledge
Data  Results of our actions
Sources
 Assess accuracy of our predictive models
 Refine segmentation schema
 Define new goals, questions, data “wish
lists” (big data? Or small…)

40. Impact of Speed…
Type of data and Our understanding
technology tools: Of customers:
Daily / weekly /
Small Data monthly
(+ related tech)
Big Data Instantly
(+ related tech)

41. Impact of “resolution” (quality of picture)
All his His son’s
friends have favorite
Chase color is
blue
Instantly Father just
started at
Big Data Instantly Bank of
America
(+ related tech)
Instantly
Instantly
Helping us Take Smarter Actions w/ Customers
 Target: Is he one?
 Message or action: What?
 Offering: Product design
 Service: How delivered?
(how experienced by customer?)

42. So how does Big Data + Related Tools Help With…
1
Customer Segmentation and Lifetime Value (CLV)
2
Customer Retention
3
Cross‐sell, Up‐sell
4
Marketing Optimization & ROI
5
New Financial Product Design & Innovation

43. Q&A