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FSIUG WEBINARJaime 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, 2012Time: 2:00 – 3:00 EST
More and more technologists are getting excited about "Big Data", which they often define ashaving greater volume, greater variety, and greater velocity than traditional data assets.Although "Big Data" has great potential to spur innovation, the enabling technology andanalytics create new challenges and risks. Organizations are investing significant time andmoney in "Big Data" strategies, tactics, teams and tools. Yet, despite the hype, most "Big Data"initiatives have not generated concrete and positive ROI.
Today’s Agenda
• Message from our President, Rich Bouthilette
• Message from our Quest Education Specialist, Jenn Abney
• Webinar• Q&A
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
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
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 [email protected]
800/652‐6422 x24037
Reconnect
• Jennifer Abney, Education Specialist, Quest International User Group
August 27‐29, 2012Connecticut Convention CenterHartford, Connecticut USAQuestDirect.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 HCMo Financialso Supply Chaino Tools & Technologyo Upgradeso Enhancement discussions with Oracle development and
support.o SIG meetings around the featured product modules.
Architects of Fact‐Based Decisions™
Turning Data to Dollars™ in the Era of "Big Data"
• Jaime Fitzgerald, Founder and President, Fitzgerald Analytics April 18, 2012
Nice to Meet You!
Jaime Fitzgerald@jfitzgerald
• Key Mission is to Find & unlock opportunitiesvia data, technology, people, + processes.
Principles:
“Begin with the End in Mind” (Covey)
“Quality is Free” (McGregor)
Data to Dollars™ specialist. Creator of a structured methodology and toolkit to accomplish this. Will share further at Reconnect!
Introduction
1. Big Data… Big Results?
2. Data to Dollars™
3. Implications of Big Data
4. Key Takeaways and Questions
Table of Contents
Transforming Data to Dollars™
It’s a journey…
Really Big Data
Product of everywhere
Big DataProduct of Alberta
Small Data
1
3
2
Defining Big Data: “Three Vs”
"Big Data“ is often defined as data with:
greater volume…
greater variety…
and/or
greater velocity….
Another Way to Define “Big Data”
What are the optimal methods to accomplish your goal?
• Centralized• Relational DBs (tables)
• Distributed• Non‐relational DBs (key‐value pairs)
Note that this definition hinges on methods applied, not on dataset sizes:
800GB Can Be “Traditional”
80GB Can Be “Big Data”
• SQL queries • Map‐reduce and custom algorithms
• Centralized• Standardized analytics
• Distributed• Custom analytics
• MS SQL Server• Oracle• Tableau• Excel pivot tables
• Hadoop• BigTable• Riak• Amazon S3
Traditional approaches Big‐data approaches
Data storage
Data access
Data analysis
Typical tools
My Perspective Towards “Big Data”
Skeptical (of the hype)…
….yet
Cautiously Optimistic!
Big DataProduct of Alberta
Big Data Hype – Does is Cause a Problem?
“Data is the New Oil” – World Economic Forum Report
The Potential is Real…It’s Just Not Easy to Get
Introduction
1. Big Data… Big Results?
2. Data to Dollars™
3. Implications of Big Data
4. Key Takeaways and Questions
Table of Contents
Will Big Data Unlock Big Results?
• It depends…
• ...on the principles you work by.
Stephen Covey
2. Insight You Need
3. Analytic Methods
4. Data You Need
5. Tools, Platforms, Technology, People, and Processes
1. Your Goal
Beginning with the End in Mind
Fitzgerald Analytics: Converting Data to Dollars™
Better Data Better Analysis Better Results
“A Journey of a Thousand Miles….”
Worth The Trip!
1
3
2
Key Steps in the Journey to Results
Data Governance
Data Management
Data Quality
New Data Source Acquisition
Analysis Insight Better Decisions
Better Processes
More Customers
Happier Customers
3. Results2. Analytics1. Data
Introduction
1. Big Data… Big Results?
2. Data to Dollars™
3. Implications of Big Data
4. Key Takeaways and Questions
Table of Contents
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.
Define Goal
Define Business Model
DefineCausality
1 2 3
Usually net profit
Can be anything!:
– Marketing ROI
– Non‐profit impact
– Customer satisfaction
– Etc.
Products / services
How sold / how delivered
To what customers
At what price
Cost structure (fixed vs. variable)
Known KPIs and rationale for them
Aka “drivers tree”
Makes the causal model visual
Inputs
• A simple example…
Here’s a Simple Example
ProfitProfit
CostsCosts
RevenuesRevenues
VolumeVolume
PricePrice
COGSCOGS
SG&ASG&A
. . .
. . .
. . .
. . .
Causality Flow and Strategy Planning• Causality flow and strategy planning move in opposite directions…
… but strategy is best developed in this direction (“Beginning with the End in Mind”)
ProfitProfit
CostsCosts
RevenuesRevenues
VolumeVolume
PricePrice
COGSCOGS
SG&ASG&A
. . .
. . .
. . .
. . .
Causality flows this way…
“Causal Clarity”
• If cause and effect are clear, practical analytics becomes feasible
Key Decisions
1. Drivers of Results…
Better Decisions
2. Optimized by Analysis & Data…
Revenue
Costs
Risks
Profit
3. Unlocking Better Results
Key Business Processes
Better Processes
Causes Effects
Causal Models: A Simple “Base Case”
• Each business model has an inherent “causal model,” but the “core branches” are similar
Revenue
Cost of Revenue
Operating Costs
Marketing
Overhead
Other
Gross Profit
Other Costs
Net Profit
less
less
Example: Drivers of Net Profit
Your Business Model
Has
A Point of Opportunity
Here is an opportunity to enhance ROI on Marketing + Sales efforts:
Volume
Price per Txn
Sales and Marketing
Transactions per Client
# of Clients
X
Point of Opportunity: “Efficiency of New Client Acquisition”Key Driver / KPI: Acquisition Cost per New Client
Formula: [spending on new client marketing]/[# New Clients)
Types of Questions Analytics May Answer
We are about to get practical, let’s keep the following in mind…
Source: Tom Davenport in “Analytics at Work”, Harvard Business School Press
Past Present Future
InformationWhat happened?
(Reporting)
What is happening now?
(Alerts)
What will happen?
(Extrapolation)
Insight
How and why did it happen?
(Modeling, experimental
design)
What’s the next best action?
(Recommendation)
What’s the best/worst that can happen?
(Prediction,optimization, simulation)
What We Need to Get Practical
• To get practical about analytics, we need three things…
What We Need Definition
1. Causal Clarity re: Your Business Model
How You Make Money Key Drivers of Results
2. Definition of Your Points of Opportunity
Gaps vs. Potential Opportunities Recognized
3. A Plan to Capture the Opportunity
Insight You Need Method to Get It
Planning Your Analysis
2. Insight You Need
3. Analytic Methods
4. Data You Need
5. Tools, Platforms, Technology, People, and Processes
1. Your Goal = “Point of Opportunity”
Choosing Analytic Methods
Selecting the right analytic method is a key success factor. Consider the logic below…
1. Your Goals
2. Types of Info you Need
3. Information Available
Analytic MethodInforms
Introduction
1. Big Data… Big Results?
2. Customer Profitability Analysis
3. Implications of Big Data
4. Conclusion and Questions
Table of Contents
2. Insight You Need
3. Analytic Methods
4. Data You Need
5. Tools, Platforms, Technology, People, and Processes
1. Your Goal = “Point of Opportunity”
What does “Big Data” change?
Big DataChangesTheseSteps...
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.
Big Data Allows Us To Work with Large Datasets
• We can analyze datasets larger than ever before
Beyond a certain point, conventional methods just aren’t feasible –Google couldn’t run on a relational DB
For larger datasets, big‐datamethods make more sense
For smaller datasets,conventional methods aremore cost‐effective
Dataset size
IT Costs
For a given desired speed of analysis…
Traditional methods
Big‐datamethods
Big Data Allows Us To Get Results Faster
• We can get results faster than ever before
Analysis speed
IT Costs
For a given dataset size…
Conventionalmethods
Big‐datamethods
SLOW FAST
Introduction
1. Big Data… Big Results?
2. Customer Profitability Analysis
3. Implications of Big Data
4. Conclusion and Questions
Table of Contents
Build/Maintain Customer Profitability Models:
Identify costs & revenues Build profiles Integrate data from
“new” sources
Example: Iterative Customer Profitability Enhancement
• Create consistent message • Target action to individuals• Optimize product / service
portfolio Data Warehouse
New Customer Knowledge Results of our actions
Assess accuracy of our predictive models
Refine segmentation schema
Define new goals, questions, data “wish lists” (big data? Or small…)
Take Smarter Actions w/ Customers Target: Who?
Message or action: What?
Offering: Product design
Service: How delivered? (how experienced by customer?)
External Data
Sources
Impact of Speed…
Instantly
Daily / weekly / monthlySmall Data
(+ related tech)
Big Data (+ related tech)
Our understandingOf customers:
Type of data and technology tools:
Impact of “resolution” (quality of picture)
Instantly
Instantly
Instantly
Instantly
Father just started at Bank of America
His son’sfavorite color isblue
All his friends have
Chase
Big Data (+ related tech)
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?)
Customer Segmentation and Lifetime Value (CLV)
Customer Retention
Cross‐sell, Up‐sell
Marketing Optimization & ROI
So how does Big Data + Related Tools Help With…
2
3
1
4
New Financial Product Design & Innovation5
Q&A