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5 Big Data Use Cases
for 2013Tim GasperDirector of ProductInfochimps, Inc.
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#1 enterprise cloud for big datasome of our customers our partners
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BI & DataVisualization
Big DataApplications
Reporting &Ad-hoc Business
Questions
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poll
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poll
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81% of companiessay Big Data is a top 5IT priority in 2013
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CRM/customer supportPOS/purchasesERP/accountingemail/documents/collab.BI & data warehousesystem & network logsweb logs/clickstreamgoogle analytics/omniturefacebook/twitteryelp/foursquare/googleexperian/epsilon/acxiommobile devicessensorsproduct reviewsgoogle search results+ more
?many terabytes of data,
sometimes many petabytes
more data than ever before
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BIG• volume• velocity• variety• variability
DATA• scalable• intelligent• agnostic• holistic
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poll
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14
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1risk analysis and fraud detection
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comprehensive data picture• build comprehensive data picture of customer-
side risk• publish a consolidated set of attributes for
analysis• add additional context, both internal and
externalparse and aggregate data from different sources
• credit and debit cards, product payments, deposits and savings
• banking activity, browsing behavior, call logs, e-mails and chats
merge data into a single view• a “fuzzy join” among data sources• structure and normalize attributes• sentiment analysis, pattern recognition
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customer risk analysis
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activity records in a central repository• centralized logging across all execution
platforms• structured and raw log data from multiple
applicationspattern recognition to detect anomalies/harmful behavior
• feature set and timeline vector are very dynamic
• “schema on read” provides flexibility for analysis
data is primarily served and processed in HDFS with MapReduce
• data filtering and projection in Pig and Hive• statistical modeling of data sets in R or SAS
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surveillance & fraud detection
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regulatorycompliance
Source:http://virtualization.sys-con.com/node/101598
ingest datasearch &
legal discovery
intraday analysis& historical analysis
(production reports +exploratory risk modeling)
TradingData
CustomerData
global investment bank#5usecases
trade risk
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brand and sentiment analysis
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the internet generates a lot of chatter about brands
• understanding what’s said is key to protecting brand value
• facebook & twitter generate a flood of data for large brands
capturing and processing direct feedback• better engagement and alerting via sentiment
analysis• integration with other customer service
systemshadoop handles the diverse data types and processing
• sources of data changing and semantics continuously evolving
• sophistication of algorithms is iteratively improving
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brand & sentiment analysis
ingest datasearch &
application
trend analysis
SocialMedia
TraditionalMedia
large media conglomerate#5usecases
News, Blogs, etc.
real-time sentiment,influence, gender,
topic extraction, etc.
customer insights/behavior
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understanding customer behavior and preferences
• rapidly test and build behavioral model of customer
• combine disparate data sources (transactional, social, etc.)
structure and analyze with Hadoop• traversing usage and social graphs• pattern identification and recognition to find
indicatorsfeature extraction to find root causes
• defining attributes and modeling statistical significance
• combinations and sequence of attributes + actions factor in
customer churn analysis
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comparison shopping is making retail hyper-competitive
• discount programs, e-mail correspondence entice shoppers
• brand loyalty means attention to detail and service
customer lifecycle is more than purchases• browsing and online data used to capture
customer attention• loyalty programs bridge the gap between
purchasesreach into online channels
• online engagement is personalized just as in store
• connecting online and in store shows customer awareness
customer loyalty
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ingest datacustomer insight
reports
shopping patternrecognition
Demographics,Geography,
Web Data, etc.
Point Of SalePurchase Data
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customer segmentation
targeted marketing and personalization
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the checkout lane is everywhere• cookies track users through ad impressions• purchasing behavior is time sensitive
logs collected online and offline• data is ingested incrementally• process happens at a variety of time scales
data logged into HBase and primary store• some events naturally associate, others require
deeper analysis• insights implemented via application logic
targeted offers
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collect and serve personalization information• wide variety of constantly changing data
sources• data guaranteed to be messy
data ingestion includes collection of raw data• filtering and fixing of poorly formatted data• normalization and matching across data
sourcesanalysis looks for reliable attributes and groupings
• interpretation (e.g. gender by name)• aggregation across likely matching identifiers• identify possible predicted attributes or
preferences
recommendations & forecasting
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ingest data
pre-definedweb content
and deals
behavioralcluster analysis
ClickstreamData from Online
Storefront
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major apparel brandtargeted discounts
big data business intelligence
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traditional datawarehousing
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big datawarehousing
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big datawarehousingThe Infochimps Approach
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big data exploration & visualization
ingest data
SQL analysiswith Hive & Hue
Retail SiteWeb Logs
BI dashboarding
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popular online deal sitebusiness command center
learn more >>
Request a Demo:http://infochimps.com/demo
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