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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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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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 >>

sales@infochimps.com1-855-328-2386

Request a Demo:http://infochimps.com/demo

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