Data Discovery for Big Insights

Jaime FitzgeraldPresident, Fitzgerald Analytics

May 17, 2012

Architects of Fact-Based Decisions™

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Jaime Fitzgerald

@jaimefitzgerald@fitzanalytics

Frequent Speaker on “practical analytics”…reducing the “barrier to benefit” from data and it’s potential

Founder of Fitzgerald Analytics, an advisory firm he built into a team of 25 professionals specializing in data-driven strategy, business analytics, and data asset management. Twice named a “Best Small Consulting Firm to Work For” by Consulting Magazine.

Data to Dollars™ specialist. Known for helping clients of all shapes and sizes get better results through data, analytics, & technology

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ROI on Data Assets: More Crucial Than Ever

“Data is the New Oil”

- World Economic Forum Report

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Trends in Business Intelligence & Data Discovery

Data discovery fits into three key trends…

Trend Implication

1. Agile Analytics Users need more flexible & efficient tools for rapid-cycle, iterative analysis

2. Big Data More data, in a variety of formats, is available and needs to be profiled, processed, integrated, and used appropriately

3. Data VisualizationToday’s executives, managers, and analysts expect insights to be delivered visually. Data visualization has gone from “nice to have” to “expected.”

The remaining slides put these trends into practical context.

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Action

Results

Data

Analysis

Action

From Data to Results: Key Steps

1

3

2

Results4

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4. Results

New Data Source Acquisition

Data Discovery

Data Governance

Data Quality

Analysis

Insight Better

Decisions

Better Processes

More Customers

Happier & More Loyal Customers

Opportunity Discovery

Revenue Growth

Margin Expansion

Cost Reduction

Risk Management

The Journey to Results: How Data Discovery Fits In

3. Action2. Analysis1. Data

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The Cycle of Analysis

Data

Analysis

ActionResults

New Data Learnings

1

3

2

4

5

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Cycle of Analysis: Customer Analytics

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

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Key Success Factors

1. “Begin with the End in Mind” (Goal-Centricity)

2. Agility & Fast Iteration

3. Cross-Functional Collaboration (IT, Data, Business, Domain Experts)