Governing the Data to Dollars Value Chain™ - Sept 2012 NYC Data Governance Conference

Analytics: Governing the Data to Dollars Value Chain™

Jaime FitzgeraldPresident, Fitzgerald Analytics

Sept 13, 2012 2:40PMNew York City

Architects of Fact‐Based Decisions™

2Analytics: Governance of the Transformation of Data to Dollars © 2012 Fitzgerald Analytics, Inc. All Rights Reserved

Introduction

Jaime Fitzgerald,Founder @ Fitzgerald Analytics

Professional Mission:

To help clients find and unlock opportunitiesvia data, technology, and processes, whilealways addressing the crucial role of people.

Writing a book: “The Data to Dollars Value Chain” Collaborating re: Analytics, Info Mgt, Data Mgt via groups like TDWI, NYTC, DAMA, AIIM, and “Analytics in Fiserv NYC”

Easier Ways to Find Opportunities

Better Ways to Unlock That Potential

andTo Achieve my MissionI Focus On:

Other Projects:


Improving Analytic Processes

Building Analytic Teams

Measuring Things Clients Did Not Previously Measure

Turning Data into Dollars™

Things my team and I work on a lot

2

3

1

4


The “importance statement”…

“Technology is to us…

….what manufacturing is to

General Motors."

‐Jamie Dimon, Chairman/President/CEO JPMorgan Chase

Source: The Economist, 2007, re: banking http://econ.st/nrMSpa


…the problem statement


Table of Contents

1. Data to Dollars Value Chain™

2. Governance Recommendations

3. Case Studies


The Data to Dollars Value Chain™

4. Results

New Data Source Acquisition

Data Discovery

Data Governance

Data Quality

Analysis Insight Better Decisions

BetterProcesses

More Customers

Happier & More Loyal Customers

Opportunity Discovery

Revenue Growth

Margin Expansion

Cost Reduction

Risk Management

3. Action2. Analysis1. Data


Analytics Value Chain as a Cycle

Data

Analysis

ActionResults

New Data Learnings

1

3

2

4

5


Example: Customer Analytics Cycle

Customer Data

Analysis

ActionResults

New Data Learnings

1

3

2

4

5 Descriptive Predictive Test & Learn Hypotheses

Messages Offers Products Pricing & Policy Customer Service

Profitability Behaviors Demographics Psychographics Wants & Needs

Customer Response Financial Impact Predictions vs. Actuals

Surprises Input

Response to actions Delta vs. Predictions New insights New hypotheses New data sources


So why DOES this happen?


“Uber Cause”: Complexity + Human Limitations

1. Business Models 2. Decisions + Processes

3. Analysis Options4. Tech Tools,

Platforms, Plug‐ins, Deployment Options

Analytic Projects

Complexity = f (Bizmodels x Decisions x Processes x Analysis Options x……..)


Other Root Causes of “Analytic Underperformance”

Cross‐Functional Collaboration

Communication Challenges

Technology and Data Management Barriers

1

2

3

The good news is…

…managing complexity makes all these other problems easier to manage also…

People: Talent, Skills, Learning, Team Effectiveness

4


Table of Contents



3. Case Studies


Recommendation #1: Process Standards

Plan and execute with discipline ‐ “begin with the end in mind”

Insights Required

Analysis Methods Required

Data Required

Platforms, Technology, People, and Processes

Business GoalTranslates to

Which drives

Allowing definition of

And selection of the right

Plan:

Act:


Rec #2: Core Governance Objectives

Objective Recommendations

• Business‐driven w/ technical inputs (e.g. time est.)• Standard criteria (process not personality!)• Decision‐rights clear (governance team!)

• Essential for testing, QA, audit, often compliance• Key concept: lineage / process branching

• Trust = f (trust‐worthiness + communication)• Key concept: fit with purpose• Trust drives use…Use drives value.

• Optimize globally (not locally)• Key Concept: the “Enterprise Hat”• Impossiblewithout governance

1. Prioritization

2. Repeatability / traceability

3. Trust & Use

4. Enterprise Alignment


Rec #3: Speaking of Alignment

Analytics Governance

DataGovernance


Rec #4: The Power of “Causal Clarity”

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


Example: creating “Causal Clarity”

ROI on Sales & Marketing:

Volume

Price per Transaction

Sales and Marketing

Transactions per Client

# of Clients

X

Opportunity: “Efficiency of New Client Acquisition”Key Driver / KPI: Acquisition Cost per New Client

Formula: [spending on new client marketing]/[# New Clients)


Rec #5: Analytic Governance “Cheat Sheet”

Executive Establish “causal clarity” visually so that everyone understands Encourage teams to use this context to prioritize and target effort Expect recommendations to be justified by their impact on key drivers

Business Identify points of opportunity before investing time in analytic details

Technology Insist upon understanding the business context and causal logic of requests for analytic systems and effort

1. Begin Your Business Model

2. Define + Agree on the Causal Model

3. Identify Points of Opp.

4. Define the info needed to unlock the opp.

5. Keep analysis as simple as possible…

For All:

By Role:


Table of Contents



3. Case Studies


Case Study: Missing the “Analytics Layer”

The cost of “competing without analytics”?

Key Performance Metric Performance vs. Peer Group

% of Store Customers w/ Store Card

Line of Credit per Cardholder

Utilization Rate (balance/credit limit)

“Gross Profit Yield on Balances”

Impact on Store Profits (extra boost)

Total profit per card (annual, lifetime)

Step 1: Performance Gap Analysis

Case Study:Private Label Credit


It is hard to compete without the analytics layer

Insights Required

Missing Analytics Layer

Data

Tools, Technology, People, and Processes

Business GoalPlan:

Act:


Step 2: Groundwork for Analytics Capability

Basic pre‐conditions to analytics excellence…

Data Quality

Data Integration

Customer Profitability

Analytics Data Mart


Step 3: Proof of Concept (‘”It’s Valuable”)

The first 3 analytic wins proved the value….

Credit Line Decision Rules

(Profit Lift = 14 %)

Credit Approval Models

(Profit Lift = 10%)

Marketing ROI

(Efficiency Gain = 30%)


The “Long Pass” analogy

In Analytics, what is the

“cost of an interception?”


Step 4: Scale & Sustainability (1 FTE to 10 FTE)

Analytics Governance Board

Request Management System

Project Portfolio Management

Project 1 Project 2 Project 3 Project 4

Profits quintupled, sustainable growth trajectory

Data Gov.

IT Governance

Strategy


Case Study: “Lean Startup” Governance

Realities Implications for Governance

Budget Constraints “Process overhead” considered untenable…

Rapid‐cycle work‐styles Adds speed and agility… …but increases risk of mistakes… …and lost knowledge…

“Rework” a big risk THIS is where the team realises how much they NEED documentation

3. In‐Code “Knowledge

Drops”

Key solutionelements:

1. Causal Clarity

2. Govern the Info‐Product Portfolio


Context: Local Search Business Models


Bundle’s Disruption – It is based on REAL SPEND DATA


Key Data Source: Billions of Real Spending Records

Unlike other merchant listing sites, our content is based on real credit card spending by 20 million households

Example: Credit Card Statement Data

Key Issues with this Data:

1. Credit card data lacks merchant identifier

2. Depend on text analytics to associate transactions with merchants


Key Steps in “Refinement” (Transformation)

Other Data: Census, Bureau of Labor Statistics, User Feedback

Card Transaction Data

Merchant Listings(e.g., Address, Phone

Number, Business Type)

Old DataTransformed in New Ways

To Create New Features Such As…

Data‐Driven Reviews From an Array of Customer

Segments

People Who Shop Here Also Like…

The Bundle Loyalty Score

Linking

Normalization

Aggregation

Clustering