Jaime Fitzgerald: A Master Data Management Road-Trip - Presented Enterprise Data World 2010
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Transcript of Jaime Fitzgerald: A Master Data Management Road-Trip - 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
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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
Journey of MDM
Transformation
Lessons Learned5
Introduction1
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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
Journey of MDM
Transformation
Lessons Learned5
Introduction1
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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!
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Art Garanich
Who Are We?
Architects of Fact-Based Decisions™
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
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Table of Contents
Introduction1
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
Journey of MDM
Transformation
Lessons Learned5
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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”
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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
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Executivesde-prioritized
fact-based decisions
Users didn’t trustexisting 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…
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An Ongoing Journey Towards Improvement
Suffering Ledto 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 ofCurrent
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:
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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
Journey of MDM
Transformation
Lessons Learned5
Introduction1
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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:
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About “Stopping the Bleeding”
If the skills, experience, andbest practices are missing . . .
You can TRY and FAIL tostop the bleeding . . .
This preserved existing problems with data and created new ones!
New SQL instance withconsolidated 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…
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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…
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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…
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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)
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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…
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Table of Contents
Overview: Why Did We Embark on This Journey?2
Key Landmarks on the Journey3
A Crucial Turning Point: Moving to Execution4
Lessons Learned5
Results: What Makes it All Worthwhile6
Journey of MDM
Transformation
Introduction1
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Execution
At first, we were overwhelmed with choices . . .
Where to start? What destination first?
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– 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
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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”?
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“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
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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
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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
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Table of Contents
Overview: Why Did We Embark on This Journey?2
Key Landmarks on the Journey3
A Crucial Turning Point: Moving to Execution4
Lessons Learned5
Results: What Makes it All Worthwhile6
Journey of MDM
Transformation
Introduction1
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Lesson Learned: Value of Buy-in
Buy-in from the Executive team was essential to moving forward
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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…
Mea
sure
able
Ben
efits
Unl
ocke
d
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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
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Table of Contents
Overview: Why Did We Embark on This Journey?2
Key Landmarks on the Journey3
A Crucial Turning Point: Moving to Execution4
Lessons Learned5
Results: What Makes it All Worthwhile6
Journey of MDM
Transformation
Introduction1
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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”
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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…
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Continue the Journey with Us!
Architects of Fact-Based Decisions™
Art Garanich Jaime Fitzgerald
917-846-3759
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!