Big Data

© 2013 Acxiom Corporation. All Rights Reserved.

BIG DATA


Data is Gold


Customers’ Lives Are Constantly Changing

Every hour of every day

5,769 people changed jobs

2,748 people moved

509 people were married

244 people got divorced

186 people declared bankruptcy

These people are your customers

© 2013 Acxiom Corporation. All Rights Reserved.4

Traditional Data and Data Solutions Data Application●List Data for Customer Acquisition●Enhancement Data for Upsell, Cross-sell, CRM ●Intense Data Analytic Driven Segmentation●Postal Address Cleansing●Locate, Look Up, Verify●Customer RecognitionDirect Marketing/Addressable Media: ●Call Center●Direct Mail●Direct eMail●Brick and Mortar


Channels Are Multiplying Rapidly New Types of Data Exploding Volume Escalating Velocity


Data Elements in Play Marketing Layer On and Offline – Anonymous and PII

Shopping behavior Viewing Behavior (Digital TV) Geo-Location (Mobile Device) Browsing behavior Click stream Purchase behavior Demographics, Sociographics Life Stage Analytics and Segmentation Spotlights, Footlights Cookies Email behavior – click and open

Social Network Data # of Networks # of Friends Likes, Interests, Mentions Blog Data Preference data Response data Pings Precise Geo w/n a store Cookie syncing Container tags Offline to online


Marketing

Online

Mobile

Set Top Box

Marketing

Online

Mobile

Set Top Box

Risk

Fraud

Authentication

Verification

Risk

Fraud

Authentication

Verification

IdentityIdentity

Marketing and Risk


BIG DATA


Agenda

•Landscape is Changing •Attitudes about Big Data •Spotlight on Data Brokering and Marketing•Implications for Data Content and Data Enablers•Implications for Users of Data •What is Expected of Stakeholders•Questions


Acxiom’s Privacy and Compliance Team•Acxiom has been big data for over two decades and been at the forefront in data protection and privacy matters

•Pioneered innovative privacy solutions, creating the foundations for what are now standard practices in many industries.

•One of the most advanced and robust privacy programs in the world helping clients be compliant and improve business results

•Most experienced privacy team in our industry which is regularly sought after by clients, policy makers and regulators

•Maintain strong relationships with key policy makers and regulators in the US and abroad.

•Provide innovative approaches to maximize the value of data while understanding the risks involved

Privacy Permission Practices

© 2013 Acxiom Corporation. All Rights Reserved.Privacy Permission Practices

11

Acxiom’s Privacy and Compliance TeamGeneral Counsel Jerry JonesGlobal Public Policy and Privacy Executive Jennifer GlasgowGlobal Compliance Risk Leader Tim Spainhour

Americas Team Public Policy and Privacy Officer Sheila Colclasure Compliance Legal Leader Jordan Abbott Compliance Legal Attorney Kimberlin Cranford Data Content Compliance Legal Attorney David Reckert US Marketing Data Products Compliance Melisa Aebly US Risk Products Compliance Maxlyn Wanner US Consumer Care Advocate Alice Beaton

Kelli Chase EU/EEC Team EU Privacy Officer Sachiko Scheuing UK Officer Alex Hazell French Privacy Officer Sarah Wanquet German Privacy Coordinator Dr. Sachiko Scheuing Polish Privacy Officer (Data & GSC) Michal Kacxorowski

Australian and New Zealand

Privacy Attorney Julie Dennis

Officer Karina Boscolo

Asian Privacy Officer Dr. JJ Pan


Landscape is Changing

Law and Regulations

versus Expectations

Big Data = Big Compliance

Big Data = Big Security


Landscape is Changing

•Attitudes about Big Data

•Data Minimization

•PII vs Anonymous

•Sensitive Data Evolving

•Analytics & Automated Decisions

•Accountability

Law and Regulations

versus Expectations


Attitudes about Big Data

20 Years of Consumer Attitudes

60% Pragmatist

30% Fundamentalist

10% Unconcerned

Examples of Big Data individuals can understand… Search is Big Data for consumers.


Attitudes about Big Data

•Individuals

• Some concerned about commercial surveillance

• Few concerned about government surveillance

•Consumer Advocates and Policy Makers

• Business only after profits

• Protect individuals from themselves

• Need for transparency

• Concern about secondary use

• Concern about too much data

• Concern about robust analytics


Data Minimization vs Big Data

•Increasing Commercial Surveillance

• Online (Search, Behavioral, etc.)

• Mobile (Location)

• Interactive TV

• Movies & Video

• Social Media (Social Networks, Blogs, Chat)

• Facial Recognition

• Apps

•International Perspective (especially EU)

Television

Mobile

Online

Retail

FacialRecognition

Minimize what?


100% 0%

PII vs Anonymous – Definitions

DeviceIdentifiableInformation

Anonymous

Choice

Notice

XDe-IdentifiedInformation

XPersonally

IdentifiableInformation

AggregateInformation

Pseudo-anonymous

/ /

PersonalPseudo-

anonymous

PII DII AGIDe-ID

SANI

ANIPIISANI

Covered Information

Ease of Technical Re-identification


Sensitive Data Evolving•Historical Sensitive Commercial Data

• Identification

• Financial

• Medical

•New Categories of Sensitive Commercial Data

• Specific location

• At-risk populations (children & elderly)

– Raise the age for COPPA from under 13 to 18

• Social network information (public & non-public)

• Biometrics & Facial recognition

• Modeled Data

Finances

Identification

Medical

Social Networks

Biometrics

At RiskPopulations

FacialRecognition

Location


•Effectiveness of Analytics

• Modeling – Identifying & Justifying Predictive Data

• Application of Models– Scope Creep & Secondary Uses

•Risks with Automated Decisions

• “Boxing in” Individuals

• Discrimination & Profiling

• EU Fear of Automated Decisions

Analytics & Automated Decisions

Big Data = Big Analytics


New Privacy Principle: Accountability •Accountability Principle (CIPL Project)

• Authorize Someone to Oversee Privacy – Chief Privacy Officer

– Need some independence/authority to have credibility

• Implement Written Policies that Conform to External Criteria

• Build Privacy into Agency Processes / Systems (e.g. Privacy by Design / Privacy Impact Assessments)

• Review Policies & Practices Periodically (Assessment or Audit)

• Provide Effective Redress to Individuals

•Adoption

• Unofficially Adopted by Regulators before Officially Required by Law


•Behavioral Advertising

•Using Offline Data for Online Advertising

•Mobile Marketing

• Location

• Mobile IDs

•Digital TV

•Cross-border Data Flows

Self-Regulation (Co-Regulation)

Companies must go beyond what is required by law!

Must work on and anticipate more self-regulatory guidelines = new policies.


•Policy Makers

• Way Behind the Times

• Reactive to Incidents

• Don’t Understand

– Technology

– Value of Data

– Protections Available

•Supreme Court and Lower Courts Engaged

•Data Broker Legislation (2-3 years?)

Legislation & Courts

Self-regulation can set the stage for workable

legislation.


•House Privacy Caucus

• Congressmen Markey & Barton

•Senate Commerce Committee

• Senator Rockefeller

•Federal Trade Commission (FTC)

• Commissioner Julie Brill

• Aggressive Consent Decrees

•General Accounting Office (GAO)

•States Attorneys General

Spotlight on Data Brokers & Data Services

Media attention high!

Client inquiries increasing.


Implications

•Evolve Policies and Practices

•Greater Visibility with Consumers

•Comply with All Policies

•Sustain Privacy Leadership Position

•Promote Self-Regulation•Evolve Acxiom Policies

•Develop Compliant Products and Services

•Be Proactive with Clients

•Be Careful with Partners and Suppliers

•Be Careful about Messaging

•Promote a Compliance Culture


•Don’t Understand 3rd Party Obligations

•New Anonymous Online Rules Prevent Common Offline Practices

• Closed Loop Marketing

• Open Loop Marketing

•Increased Risk of Non-compliance

Implications for Users of Big Data

Be a trusted vendor.


What is expected of Big Data Industry?

•Learn the rules for area/industry.

•Question what doesn’t seem to fit those rules.

• New approaches

• New products

• Short cuts / workarounds

•Help assure compliance all the time.

•Learn from media and enforcement examples

Call privacy team if you have ANY doubt/question.


TRUSTClients Policy Makers

and Regulators

Consumers

Exper

tise

and Le

ader

ship Com

pliance

and Leadership

Hyper-Transparency

Future Depends on…

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®

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Big Data

Business

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