Data Management: Case Study Presented @ Enterprise Data World 2010

Tales from a Master Data Management Road Trip
March 17, 2010
1:30 – 2:30 pm
Architects of Fact-Based Decisions™
Jaime FitzgeraldArt Garanich

22010-3-17 Enterprise Data World Presentation
Table of Contents
Overview: Why Did We Embark on This Journey?2
Key Landmarks on the Journey3
A Crucial Turning Point: Moving to Execution4
Results: What Makes it All Worthwhile6
Lessons Learned5
Introduction1

Table of Contents
Lessons Learned5
Introduction1

Introduction:
What Are We Going to Cover Today?
Today, we would like to:
 Introduce ourselves
 Share our experiences on the “Journey of MDM Transformation”
 Share the lessons we’ve learned – what worked well, what didn’t
 Answer questions you may have
 Encourage others to start their own transformation!

Art Garanich
Who Are We?
 25 Years in Technology
 Focus on legacy modernization
 Enjoys metaphors and parenting
 13 years in Management Consulting
 Focus on the strategic value of data, and
helping companies profit from it
 Enjoys cycling and parenting
 Private label credit card issuer
 Subsidiary of Bridgestone Firestone
 Boutique strategy consulting firm
focused on fact-based decisions
 Takes a holistic approach to turning
“data into dollars”
About My
Company
About Me
Jaime Fitzgerald

Table of Contents
Introduction1
Lessons Learned5

Before our Journey, the Data Environment at CFNA was “Messy”
CFNA’s data environment was not actively managed…causing pain and tension in many places
Prior to our MDM Transformation, we faced:
 Multiple platforms
 Numerous locations of data
 Limited documentation of:
– Data locations
– Data elements
– Relationships between elements
– Business rules
– Business purposes & users of data
– Data flow
– Existing documentation not always utilized
 No holistic view of our customers
 Extremely time-consuming to pull new information
 Significant tension between business users,
analysts, and IT staff
Our Legacy Data Environment
This presentation is about our journey to “a better place”

Examples of the Tension “Back Then”
“Our analysis generated
millions of dollars in new
value… but it took forever to
obtain and clean the data!”
“When I talk to customers I scroll
through 12 screens to find the info I
need…then I don’t know where to put
the info I capture!”
“Poor data, systems, and product
features are holding us back!”
Analytics
Team
Sales and
Marketing
Operations
Functions

Executives
de-prioritized
fact-based decisions
Users didn’t trust
existing reports . . .
Examples of the Tension “Back Then”
 So they kept demanding more
new reports
 They created “homegrown”
reports with “surprising results”
“We don’t have
data to measure
customer value.”
“Why bother
asking for
something they
can’t do?”
“Before I can tell you
what I want from it, I
need to know what it
can do!”
“I THINK we
should try new
pricing….”
An unhealthy relationship
developed between users
and IT…

An Ongoing Journey Towards Improvement
Suffering Led
to Interest
“What is MDM?”
“Should I care?”
…Which Led to Desire
for A Cure…
“How do we get there?”
…and to a
Never-Ending Journey
Towards Better MDM…
“Feeling better every day”
1 2 3Phase of
Journey
State of
Information
Landscape:
Results of
Current
State:
MDM Knowledge: Low
Data Situation: Messy
Brittle Systems
“We can’t change that!”
Complexity Increasing
“Here’s a workaround.”
Data Quality: Low
There Is a Cure!
Get Me to It!
Where To?
Let’s Stop the Bleeding …
…And Start with the Basics
MDM Function Setup
MDM Governance in Place
“Show me the Results!”
 More confident decisions
 More effective system
modernization
 Reduced operational risk
 More transparency
List of Pain Points Growing
Many Pain Points…
Our adventure got underway via three main phases…
“Targeted” Application to Gain:

Table of Contents
Lessons Learned5
Introduction1

Key Landmarks
Stopping the Bleeding
Getting Buy-in & Alignment
Building the MDM Function
1
2
3
Key Landmark:
Since beginning this journey, we passed four key landmarks…
Turning the Corner to
“Targeted Application”
4
 Some problems were SO painful and SO immediate, we needed to
apply MDM principles “surgically” even before we had a fully
fledged MDM function.
 For example: building our customer profitability database, we
encountered and solved data quality issues and created a “safe
route” for data to enable this analysis…
 A few early wins on analysis increased the appetite for even better
information
 The organization recognized the role of MDM in improving
information AND agility
 The IT department shifted towards more strategic imperatives
 We built a new MDM function to define and institutionalize key best
practices
 Established our policies, standards, governance, and stewardship roles
 We reached a fork in the road: lots of new knowledge built, where
should we begin applying them?
 The list of pain points is long: we realized that we can’t “boil the ocean”
 We developed a “targeted approach” to applying MDM capabilities
 Our first successful case: a new Data Warehouse for the growing
analytics team
What it was like:

About “Stopping the Bleeding”
If the skills, experience, and
best practices are missing . . .
You can TRY and FAIL to
stop the bleeding . . .
This preserved existing problems with
data and created new ones!
New SQL instance with
consolidated data
Led to more misuse of data
(right data, wrong use)
Created self-serve
data access via Intranet
Data security issues arose“Home-grown data stores”
We had tried many times to fix data-related problems, without addressing the root issues well…

Our First Landmark: “MDM Critical Care” to “Stop the Bleeding”
Diagnosis
1. Request for
Change to
Systems / Data
2. Workaround
Solution
3. Increased
Complexity
Systemic Issue:
“The Downward Spiral” 1. A Systemic Problem w/ Data Systems.
Frequent symptoms point to a larger
problem…with broader root causes.
Change the Way you Manage Data. Manage
data as distinct from systems or processes, but
keeping in mind the inter-relationships
1. Short-term: stop
the bleeding
Symptoms
(Systemic)
Immediate Issue:
“Toxic Data”
Symptoms
(Immediate)
High Stakes Analysis
Underway . . .
Data Quality Low
Strategic Growth At Risk
Prescriptions
2. Once bleeding
stops: deal with the
more fundamental
issues
We needed to stop the bleeding before we could resolve the systemic patterns causing them…

Value of Analytics Increases
Our Second Landmark: Gaining Buy-In
Drivers of Buy-In How Buy-In Was “Formalized”
Strategy for Growth
 Required more analytics
 Required better systems, new
products, etc.
 High profile “wins”
 Desire for more
 Creation of MDM
function
 Establishment of
governance
 Skills and knowledge
built and acquired
Dissatisfaction with Status Quo
We took the time to “connect the dots” in ways that built buy-in for an MDM function…

Our Third Landmark: Building the MDM Function
Establishing the function included creation of 1) the MDM Team itself and 2) the data stewards…
1. Sales &
Marketing
2. Operations
 Responsible for optimizing the benefits of data at the enterprise level
 Reviews and advises on MDM consequences of IT/Business changes
 Builds & Maintains MDM function, including:
1) Guiding principles
2) Essential capabilities
3) Documentation
3. Finance 4. Analytics
5. Information
Technology
 Responsible for optimizing the benefits of data at the departmental level
 Responsible for ensuring standard use of data according to MDM principles and standards
 Ensures clear business requirements with regard to data elements, usage, business rules, and communication with IT
Data Stewards (Department Level)
MDM Team
(Enterprise Level)

Our Fourth Landmark: Turning the Corner to “Targeted Application”
Application of MDM Principles
 Pain-Point Identification
 Prioritization of Pain Points
 Initiative-Level Prioritization
 Resource Allocation
Selection of Opportunities
 Implementation standards and
frameworks
 Tools
 Integration with SDLC (System
Development Lifecycle) and Project
Management Standards
To get value from our investment, it was essential to move from theory to execution…

Table of Contents
Lessons Learned5
Introduction1

Execution
At first, we were overwhelmed with choices . . .
Where to start? What destination first?
Prior to our MDMTransformation, we faced:
 Multiple platforms
 Numerouslocations of data
 Limited documentation of:
– Data locations
– Data elements
– Relationships between elements
– Business rules
– Business purposes & users
– Data flow
– Existing documentation not always utilized
 No holistic view of our customers
 Extremely time-consuming to pull new information
 Significanttension between business users,
analysts, and IT staff
Our Legacy Data Environment

Our Solution: “Work Backwards from the Goal” of ROI on MDM Programs
Applied Case 1
Precondition: Application of MDM Principles in High ROI Ways . . .
Ultimate Goal = Return on Investments in MDM Programs
Case 2 Case 3 Case 4
How Do We “Bridge this Gap”?

“Unpacking the Steps” on the Pathway to ROI . . .
Ultimate Goal = Return on Investment
Apply MDM Principles & Best Practices to High-Impact Use-Cases
 Understand Problems it Solves, and Therefore the Value Proposition
 Understand Concepts, Principles, Application, and High-Level Techniques to Measure Value
Organizational Commitment
Preconditions:
 Principles
 Best Practices
 Governance Framework
 Data Stewardship Roles and Processes
Develop Governance Capability: “How do we manage this?”
 Pain-Point Identification
 Prioritization of Pain Points
 Initiative-Level Prioritization
 Resource Allocation
Capability to Select & Execute on the Best Opportunities
1. Commitment
4. Execution
 Implementation Standards and Frameworks
 Tools
 Integration with SDLC (Solution Development
Lifecycle) and Project Management Standards
2. Governance
3. Selection

Challenges at Each Stage
Stage (Precondition) Challenges Solutions and Learnings
1. Organizational
Commitment
 Without broad buy-in, implementation
will be “messy”
 The link between MDM and business
results is not obvious to everyone. Some
people NEED specifics to believe in it…
 Use specific examples to help people
understand the problems MDM
solves
 Solve a problem right away, even if
this happens before a full-fledged
program exists
2. Develop Governance
Capability: “How do
We Manage This?”
 There is a lot to learn
 Governance requires ongoing
commitment
 Governance alone doesn’t unlock results
 Align governance with OTHER key
areas where standards already exist
 Link with Knowledge Management
 Maximize cross-functional breadth
3. Capability to Select &
Execute on the Best
Opportunity
 Users often report pain points without
broader context
 The business case for solving individual
pain points can be ambiguous
 Look for projects with the biggest
business benefit AND most likely to
benefit from MDM
 “Bundle” pain points into the
projects that would solve them
4. Apply MDM Principles
& Best Practices to
High-Impact Use-Cases
 Some organizations try to jump right to
this point! (the myth of turnkey
application)
 Even WITH preparation, this is a tough
transition to make
 A good “toolkit” is essential
 Facing specifics improves the
frameworks and standards also
 Getting results maintains
momentum

Our Decision:
To Focus our Limited MDM Resources on Building a Better Data Warehouse…
The Data Warehouse will provide consolidated data which enables better strategic decisions
Category Current State Future-State Benefits with Data Warehouse
High Stakes
Decisions
 Decisions often made with
imperfect data
 Better data (more accurate, relevant and holistic)
will enable better decisions
Efficiency of
Analysis
 Analysis is time consuming
 Involvement from IT necessary to
source data
 Increased speed of analysis
 More frequent updates available
 Fewer resources needed per analysis
 Analysts will not be accessing operational data,
reducing operational burden
Integration of
Sources
 Inconsistent data limits ability to
use multiple data sources
 Combined data will provide holistic view of
customer and business
Security  Ad-hoc use of data exports is not
optimal
 Analysts access operational data,
causing some risk to systems
 Data Warehouse data will mask sensitive customer
data
 Standard source will limit ad-hoc usage
 Analysts will not be accessing operational data
Data Clarity  Data sources are not documented
 Inconsistent fields across data
sources
 Greater clarity about data thanks to:
– Well documented sources
– Data management standards
– Data dictionaries
Benefits of the Data Warehouse

Table of Contents
Lessons Learned5
Introduction1

Lesson Learned: Value of Buy-in
Buy-in from the Executive team was essential to moving forward

Lesson Learned: An Iterative Approach Maintains Momentum
Time and Money Invested
Capabilities
↑
$
More Capabilities
↑
$
Building Capabilities
Applying Capabilities
 Target high ROI application
 Get value
 Measure value
 Communicate and promote
 Target next
opportunity
 Get value
 Measure value
By taking an iterative approach that mixes building and applying capabilities, we’ve found it easier
to maintain momentum and buy-in…
MeasureableBenefitsUnlocked

Lesson Learned: Importance of Capability Building
We had to put in place the basic infrastructure and skills before we could move forward
Develop Basic Knowledge
Data Stewards
Governance Policy, Standards, and Procedures
Develop Basic Skills
MDM Team

Table of Contents
Lessons Learned5
Introduction1

A Better Pattern: Then vs. Now
Old Pattern
Via a
Long
Journey
New Pattern
MDM
Function ROI
Pain
Points
Business
Goals
Business
Results
Learnings
1. Request for
Change to
Systems / Data
2. Workaround
Solution
3. Increased
Complexity
“The Downward Spiral”

Results: Enterprise-Level Progress!
Our strategy is working, analytics has become a strength, IT is more nimble, and profits are up!
Strategy
Area of Progress:
Analytics
Legacy
Modernization
Profit Growth
 Growth strategy has gained traction, with new capabilities driving growth
in card sales volume, fee revenue, and profits
 Confidence in organizational capacity for continued growth is up
 Starting with construction of customer profitability analytics, the team has
unlocked tens of millions of dollars in new profit growth
 Analytics team has grown from one person to six (and from a team to an
official function!)
 We have begun phased legacy modernization that will enable more
strategic growth
 Our MDM capabilities will be essential to this modernization initiative
 Despite the economic crisis, we achieved record sales and profits this year
 While regulatory uncertainty has increased this year, our analytic
capabilities are helping us to rapidly adapt…

Continue the Journey with Us!
Art Garanich Jaime Fitzgerald
jfitzgerald@fitzgerald-analytics.com
917-846-3759
garanichart@cfna.com
216-362-3418
Our journey continues…we hope to stay in touch with you, our “fellow travelers,” to learn from
each other and improve results!

Data Management: Case Study Presented @ Enterprise Data World 2010

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