Big Data and Analytics
What You Need to Know
Jennifer Barrett Glasgow, CIPP
Global Privacy and Public Policy Executive
Acxiom Corporation
Updated October 2013
© 2013 Acxiom Corporation. All Rights Reserved.
© 2013 Acxiom Corporation. All Rights Reserved.
Agenda
• Implications of Big Data and Analytics
• Big Data and Analytics Tutorial
• Big Data Types and Uses
• Risks from Big Data
• Governance for Big Data
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Implications of Big Data and
Analytics
• Revolutionizing business, science, research
and education
• Involves many different types of data
• Can result in wonderful new discoveries
• Has created new challenges and risks
White Paper: Big Data and Analytics: Seeking
Foundations for Effective Privacy Guidance
Centre for Information Policy Leadership at Hunton & Williams
http://www.informationpolicycentre.com/files/Uploads/Documents/Centre/Big_
Data_and_Analytics_February_2013.pdf
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Implications of Big Data and
Analytics
• Big data is more than just a lot of data
• Big data involves collection and
integration of all kinds of data,
including new types of data
• Big data involves sophisticated
analytical processes to reveal
new insights
• Big data needs big security
Big Data is Global
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Big Data and Analytics
Discovery (phase 1)
Acquisition
Preprocessing
Integration
Analysis
Interpretation
[Correlations, Trends,
Patterns]
[Quality evaluation]
Iterative process: Quality Not Sufficient or New Questions Arise
Classification
Regression
Segmentation
Association
Sequence
Application (phase 2)
Data, some
linked to
individuals
Input
Apply Algorithm
Output
Tailored
Results
Refresh Models
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Algorithm [Model]
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Big Data Types
• Must recognize different types of data
• Personal Data (PII)
− Personally Identifiable
• Non-Personal Data (Non-PII)
− Device Identifiable
• De-identified Data
− Data with personal/device IDs removed
• Aggregate Data
− Data about a number of people or devices
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© 2013 Acxiom Corporation. All Rights Reserved.
Big Data Types
• Must recognize different types of data
• Personal Data (PII)
− Personally Identifiable
• Non-Personal Data (Non-PII)
− Device Identifiable
• De-identified Data
− Data with personal/device IDs removed
• Aggregate Data
− Data about a number of people or devices
7
© 2013 Acxiom Corporation. All Rights Reserved.
Big Data Types
• Must recognize different types of data
• Personal Data (PII)
− Personally Identifiable
• Non-Personal Data (Non-PII)
− Device Identifiable
• De-identified Data
− Data with personal/device IDs removed
• Aggregate Data
− Data about a number of people or devices
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Big Data Types
Context matters!
• Types of Data
− Non-Sensitive Data
[name, address, listed phone #, general location, year of birth]
− Restricted Data (confidential)
[unlisted phone #, precise location, full date of birth)
− Sensitivity of Data (highly sensitive)
[SSN, financial account details, passwords]
• Results of Analytics
− Non-Sensitive Insights
− Restricted Insights
− Sensitivity Insights
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Big Data Types and Uses
• Must recognize different uses of data
• Corporate uses
−
−
−
−
−
−
Fulfill a transaction
Improve operational efficiency / accuracy
Fraud detection and prevention
Develop new products/services
Market and advertise products/services
Satisfy legal obligations
• Societal uses
− Research
− Free Speech
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Risks from Big Data
• Traditional Harms
− Financial
− Physical
• New Harms
− Emotional
− Intrusion
− Reputation
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Governance for Big Data
• Federal Laws and Regulations
- General (FTC Title V)
- Sector Specific (GLBA, FCRA, HIPAA/HITECH)
- Use Specific (CAN SPAM, TCPA, DNC)
- Data Specific (DPPA, COPPA, CPNI)
• State Laws and Regulations
- Consumer Protection Laws
- Do-Not-Call Laws
- Data Breach and Security Laws
- Website Disclosure Laws
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Governance for Big Data
• Marketing Self-Regulation
- Direct Marketing Ethical Guidelines
Effective
Co-Regulation
[notice, choice with industry opt-out for PII]
- Digital Advertising Alliance Guidelines
[notice, choice with industry opt-out for Non-PII]
- Network Advertiser’s Initiative
[notice, choice with industry opt-out for Non-PII, audits]
- Mobile Marketing Guidelines
[notice, choice with future industry opt-out for PII & Non-PII]
- EU Safe Harbor/Binding Corporate Rules
[strong privacy program recognizing EU laws]
- APEC Cross-Border Privacy Rules
[New program for safely moving data across borders]
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Perspectives on the Use of Data
Source: World Economic Forum and The Boston Consulting Group
TRADITIONAL APPROACH
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NEW PERSPECTIVE
Data collected with user awareness
Data passively collected from machineto-machine transactions – notice difficult
Binary definition of personal data
Definition of personal data is contextual
and dependent on social norms
Data collected for specific use
Economic value and innovation come
from combining data sets and new uses
User is the data subject
User can be the data subject, the data
controller (owner), and/or the data processor
Individual provides legal consent but
is not truly engaged
Individuals engage and understand how
data is used and how value is created
Policy framework focuses on
minimizing risks to the individual
Policy focuses on balancing protection
with innovation and economic growth
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Achieving Trusted Flows of Data
Source: World Economic Forum and The Boston Consulting Group
AREAS TO ADDRESS
OECD PRINCIPLES
EMERGING ISSUES
Protection and Scurity
Protection and Security
Protection and Security
Accountability &
Enforcement
Accountability
Accountability
Individual Participation
Data Quality (Access and
Correction)
Rights and responsibilities
for using data
Openness
Empowered Role of
Individual
Transparency and
Understanding
Collection Limitation
Purpose Specification
Respect for Context
Use Limitation
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Our Future Depends on . . .
Companies
TRUST
Hyper-Transparency
and More Control
Consumers
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Policy Makers
and Regulators
Thank you!
jennifer.glasgow@acxiom.com 501-352-2316
© 2013 Acxiom Corporation. All Rights Reserved.
© 2013 Acxiom Corporation. All Rights Reserved.