Big Data, Not Big Brother: Best Practices for Data Analytics · Big Data, Not Big Brother: Best...

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Big Data, Not Big Brother: Best Practices for Data Analytics Jennifer Glasgow Acxiom Corporation March 2013

Transcript of Big Data, Not Big Brother: Best Practices for Data Analytics · Big Data, Not Big Brother: Best...

Page 1: Big Data, Not Big Brother: Best Practices for Data Analytics · Big Data, Not Big Brother: Best Practices for Data Analytics Jennifer Glasgow Acxiom Corporation March 2013. Client

Big Data, Not Big Brother:

Best Practices for Data

Analytics

Jennifer Glasgow

Acxiom Corporation

March 2013

Page 2: Big Data, Not Big Brother: Best Practices for Data Analytics · Big Data, Not Big Brother: Best Practices for Data Analytics Jennifer Glasgow Acxiom Corporation March 2013. Client

Client Applications of Analytics

• Understanding Market/Product Trends

• Understanding Promotion

Effectiveness

• Targeting Communications

to Prospects and Customers

• Mitigating Fraud with Identity

Verification/Authentication

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Page 3: Big Data, Not Big Brother: Best Practices for Data Analytics · Big Data, Not Big Brother: Best Practices for Data Analytics Jennifer Glasgow Acxiom Corporation March 2013. Client

Acxiom Applications of Analytics

• Understand Data Quality

• Creating New Data

– At Individual and Geographic Levels

– Demographic/Lifestyle/Interest Modeling

– Look-a-Like Modeling (Propensities)

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Page 4: Big Data, Not Big Brother: Best Practices for Data Analytics · Big Data, Not Big Brother: Best Practices for Data Analytics Jennifer Glasgow Acxiom Corporation March 2013. Client

Phases of Analytics

• Discovery Phase

– Data Collection and Integration of Historical Data

– Data Analysis

– Conclusions/Predictions Reports

Models

– Acceptability of Results

• Application Phase

– Apply Model to Current/Future Data

Page 5: Big Data, Not Big Brother: Best Practices for Data Analytics · Big Data, Not Big Brother: Best Practices for Data Analytics Jennifer Glasgow Acxiom Corporation March 2013. Client

Privacy by Design Data Considerations

• Data Origination

– Notice and Choice

• Consumer Perspective

– Expectations/Understandings

– Benefits versus Risks

• Sensitive Data Considerations

• Applying the ‘Data Minimization Principle’

• Use of the Analytics – First Party versus Third Party

– PII versus Anonymous

Page 6: Big Data, Not Big Brother: Best Practices for Data Analytics · Big Data, Not Big Brother: Best Practices for Data Analytics Jennifer Glasgow Acxiom Corporation March 2013. Client

Privacy by Design Anonymization Spectrum

100% 0%

Device Identifiable Information

Anonymous

Choice

Notice

X

De-Identified Information

X Personally

Identifiable Information

Aggregate Information

Pseudo- anonymous

/ /

Personal Pseudo-

anonymous

PII DII AGI De-ID

SANI

ANI PII SANI

Covered Information

Ease of Technical Re-identification

Page 7: Big Data, Not Big Brother: Best Practices for Data Analytics · Big Data, Not Big Brother: Best Practices for Data Analytics Jennifer Glasgow Acxiom Corporation March 2013. Client

Privacy by Design Security Considerations

• More Data = More Risk

• Anonymization Helps

– But doesn’t solve the problem

Page 8: Big Data, Not Big Brother: Best Practices for Data Analytics · Big Data, Not Big Brother: Best Practices for Data Analytics Jennifer Glasgow Acxiom Corporation March 2013. Client

Take-Aways

• Many Factors Influence Right Approach

– Consider Value to Consumer

• Data and Use Define Most Parameters

• Anonymize as Much as Possible

• Don’t Overlook Security