The document discusses big data, including its scope and sources. It notes that the volume of data doubles every 1-2 years and that 90% of data was created in the last two years. It also describes how big data can be analyzed in real-time and used for predictive purposes. Finally, it discusses some of the challenges with big data, such as privacy issues and the filter bubble effect.

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Big Data
Scope
• Data produced every 2 days = all data prior to
2003 … Google’s Schmidt
• Volume of Big Data, WW doubles every 1.2yrs
• 90% of data was produced in last 2 years
• 2012, everyday 2.5 exabytes of data created
• Increased digitization of communications &
transactions: mobile, IoTs
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Big Data
Generally
• Data versus actionable insights (Information)
– Orgs typically use small portion of data
• Good for increasing engagement with current
customers
• Increases lock-in
• Helps identify new products
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Big Data
Generally
• Useful when past is good predictor of future
• Provides real time insights (dashboards)
• Is it a revolution?
• Very buzzy, re: VCs etc.
• Example: Google Flu Trends
– Fall 2008
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Big Data
Data Sources
• Internet of Things (Sensors)
– Ex: Parking meters, Smart houses
• Machine to Machine networks: 250m 2014
– APIs versus Open Standards
• Data exhaust (ex. Social media)
• Clickstream data
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Big Data
Data Sources
• Location data
– In-store (iBeacon, sensors) and geo fencing
• Loyalty program data (Target)
• Transactional data (Krogers)
• Structured versus Unstructured data
• Primary to Secondary data (unimagined use)
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Big Data
Three Aspects
• Ingestion & Storage (cloud)
– cost
• Analytics
– value
• Visualization
– communication
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Big Data
Analytics
• Continuous
– Real-time reporting: Dashboards
• Predictive
– Netflix
– Google and house sales
• Sentiment
– Sales down, twitter chatter
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Big Data
Defining Big Data: 3 Vs
• Volume
• Velocity
• Variety
• Veracity (AP Hack “Obama hurt”)
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Big Data
Technology: Hadoop MapReduce
• Google Origins: MapReduce: indexing web,
examining user behavior to improve algorithm
• Yahoo! & Hadoop Open Source project
• Cornerstone technology for Big Data
– For now ? Google’s Cloud Dataflow
• Makes Big Data more manageable
Digital Marketing alexbr.brown@gmail.com University of Delaware

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Big Data
Technology: Hadoop MapReduce
• Cheap, scalable
• Fast
• Store, then figure out
• Open Source
– Cloudera
– Hortonworks
Digital Marketing alexbr.brown@gmail.com University of Delaware

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Big Data
Cloud Computing
• Business and consumer markets
• Increasing volumes of data to manage &
interpret
• Moore’s law
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Big Data
Cloud Computing
• Virtualization
• Variable cost
• Scalability and agility
Digital Marketing alexbr.brown@gmail.com University of Delaware

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Big Data
Cloud Computing
• Run apps from anywhere, from any device
• Easier to upgrade apps
• Makes IT more accessible
• Software as a Service (SaaS)
– Ex: Salesforce.com
Digital Marketing alexbr.brown@gmail.com University of Delaware

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Big Data
Cloud Computing
• Very buzzy re: VCs
• Key players: Google, Amazon, Windows Azure,
Oracle, IBM, Rackspace, Salesforce.com
• Privacy issues
• Data ownership and security?
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Big Data
Changes in Analysis
• Data starved to data abundance
– Too expensive, and not enough of it
• Samples and statistical significance
• Hypothesis driven versus Data driven: end of
theory?
• Correlations to design algorithms
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Big Data
Changes in Analysis
• Patterns in the data: relationships
• What versus Why?
– Amazon recommendations (risks ?)
• Causation versus Correlation (good enough)
• Limitation: we know what, but not why
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Big Data
Criticism
• Bias in data sources
– Ex: Twitter and election results
• Importance of human intuition
• Filter bubble
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Big Data
Algorithms
• A repeating set of rules, series of instructions
• Designed from the data, or purchased
• Predicts future, based on the past (Big Data)
• Input data unique, passes algorithm, output
unique (creates probability)
• Replaces human intuition: or mix of both
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Big Data
Machine Learning & Algorithms
• Increasing returns on data
• Designed into algorithm (real-time)
• Examples
– Google (search)
– Amazon (human editors versus algorithm) what, not
why
– Facebook (news feed)
– Netflix (recommendations)
– Zynga (data from game play)
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Big Data
Old World, New World
• Old World: Analogue, same content by
vehicle, medium, retail store
• New World: Digital, unique content driven by
algorithms to increase engagement and lock-in
Digital Marketing alexbr.brown@gmail.com University of Delaware

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Big Data
Old World, New World
• New World Examples:
– Netflix
– Amazon
– In store retail experience w/ mobile app and
iBeacon technology (smart retail) Macy’s ?
– Target, pregnant teen
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Big Data
Unintended Consequence: Filter Bubble effect
• Human curated content versus serendipity
• Editors versus wisdom of the crowd
• Personal universe of online information
• Past clickstreams determine future choice:
information determinism
– Netflix, Amazon etc.
• Echo chamber effect
Digital Marketing alexbr.brown@gmail.com University of Delaware

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Big Data
Unintended Consequence: Filter Bubble effect
• Echo chamber effect
• What we want to see, versus what we should see
– Amazon / Netflix recommendations (what, not why)
– Good enough recs. Versus risks ?
• Personalized: Google, Facebook etc.
• Personalization drives ad revenue
• Wikipedia is a standout
Digital Marketing alexbr.brown@gmail.com University of Delaware

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Big Data
Privacy
• Law (and keeping up)
• Terms of Service (bargain) Binary choice
• Terms of Service: changes (for business model)
• Scope of reach: Facebook / Google
– EU: Right to be Forgotten ?
– EU: Privacy rules change, Google
• Tension, privacy versus personalization
• Opt in, versus Opt out: by default
– Org donations; Facebook Nearby
Digital Marketing alexbr.brown@gmail.com University of Delaware

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Big Data
Privacy
• Knowledge of data collection: invisible
• Knowledge of data use (secondary: sometimes
unimagined)
• Creep factor (how did they know?)
– Ad retargeting
– Snapchat ?
Digital Marketing alexbr.brown@gmail.com University of Delaware

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Big Data
Privacy
• Combining data can de-anonymize
• Data brokers: reselling
– Acxiom
• Hacking, data theft (Target, Home Depot,
Kmart, etc.)
Digital Marketing alexbr.brown@gmail.com University of Delaware