Digital Marketing Class: Big data

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

Digital Marketing [email protected] University of Delaware

#buad477 follow @alexbrownracing http://www.udel.edu/alex/classes

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



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



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



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)



Big Data

Three Aspects• Ingestion & Storage (cloud)– cost

• Analytics– value

• Visualization– communication



Big Data

Analytics• Continuous– Real-time reporting: Dashboards

• Predictive– Netflix– Google and house sales

• Sentiment– Sales down, twitter chatter



Big Data

Defining Big Data: 3 Vs• Volume• Velocity• Variety

• Veracity (AP Hack “Obama hurt”)



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



Big Data

Technology: Hadoop MapReduce• Cheap, scalable• Fast• Store, then figure out• Open Source– Cloudera– Hortonworks



Big Data

Cloud Computing• Business and consumer markets• Increasing volumes of data to manage &

interpret• Moore’s law



Big Data

Cloud Computing• Virtualization• Variable cost• Scalability and agility



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



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?



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



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



Big Data

Criticism• Bias in data sources– Ex: Twitter and election results

• Importance of human intuition• Filter bubble



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



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)



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



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



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



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



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



Big Data

Privacy• Knowledge of data collection: invisible• Knowledge of data use (secondary: sometimes

unimagined)• Creep factor (how did they know?)– Ad retargeting– Snapchat ?



Big Data

Privacy• Combining data can de-anonymize• Data brokers: reselling– Acxiom

• Hacking, data theft (Target, Home Depot, Kmart, etc.)

