This document discusses big data analytics and its applications. It defines big data analytics as examining large amounts of diverse data to uncover hidden patterns and useful information. Descriptive, diagnostic, predictive, and prescriptive analytics are then described as answering different questions about what happened, why it happened, what will happen, and what should be done. Examples of applying analytics in retail and FMCG are given such as demand forecasting, operations optimization, and loyalty program management. Technologies used for different types of analytics are also listed.

1. Harnessing the Power of
Analytics
Ingest Infer Interpret
YOGESH DANDAWATE

2. Big Data Analytics
Big data analytics is the process of examining large amounts of data of a variety of types (big data) to uncover hidden
patterns, unknown correlations and other useful information. Such Information can provide advantages over rival
organizations and result in business benefits, such as more effective marketing and increased revenue.
Descriptive Analytics
• Answers the question,
"What happened in the
business?
• It looks at data and
information to describe
the current business
situation in a way that
trends, patterns and
exceptions become
apparent.
Diagnostic Or Inquisitive
Analytics
• Answers the question,
"Why is something
happening in the
business?"
• It is study of data to
validate / reject business
hypotheses. This includes
analytical drill downs
into data, statistical
analysis, factor analysis,
etc.
Predictive Analytics
• Answers the question,
"What is likely to happen
in the future?"
• Here data modeling and
forecasting are used to
determine future
possibilities
• Predictive analytics uses
data to determine the
probable future outcome
of an event or a
likelihood of a situation
occurring.
Prescriptive Analytics
• Answers what, when,
why questions
• For example, what
should a business do to
retain key customers?
How can businesses
improve their supply
chain to enhance service
levels while reducing
costs?
Created using http://www.mu-sigma.com/analytics/ecosystem/dipp.html

3. What are Big Data Dimensions?
Volume
• Data at Rest
• Terrabytes to
Exabytes of
existing data to
process
Velocity
• Data in Motion
• Streaming data,
near real time
response needs
Variety
• Data in multiple
forms
• Structured,
unstructured,
text, multimedia
data
Veracity
• Data in doubt
• Uncertain
information
(incomplete,
inconsistent,
ambiguous,
approximate data)

4. Analytics in Retail and FMCG
Application Areas
• Identifying of Potential customers
• Understanding customer churn
• Understanding customer sentiment
• Improving/Personalizing Customer
Experience
• Recommending products to
Customers
• Understand customer behavior
• In-store shopper movement
analysis
• Loyalty Program Management
• Content Analytics
• Demand Forecasting
• Operations Analytics
• Enterprise Information Management
• Supply Chain Optimization
• Scheduling Preventive Maintenance
and Repairs

5. Technology Elements
• Content Analytics
• Speech/Audio Analytics
• Video Analytics
• Text Analytics
• Web Analytics
• Sentiment Analysis
• Recommendation Engines
• Gesture Analytics
• Eyeball Tracking
• Expression Analysis
• Social Media Analytics
• Activity Stream/Click Analytics
• Storage and Search Technologies
• Active News and Event Analytics

6. • Stock & Re-Arrange
Essentials on shelves
• Reroute Shipments
• Notify/Update/Recomm
end Customers
• Enhance store security
Weather Forecast Real Time Decision Making
Disaster management

7. Call Center Management
• What are common customer
complaints?
• Locations from where maximum
complaints are getting registered?
• Are issues attributed to Operational
Efficiency, Equipment efficiency, People
Efficiency ?
• What is the general sentiment of the
product/s ?
• Which is the most popular product ?
• Customer feature recommendations?
• Average time for query resolution?
• Query/ Feedback /Complaints
Categorization
• Similar queries and their resolutions
Statistics
Geo tagging
Feedback
Complaints

8. Kinect
Expression Analyzer
Confused
Needs Assistance
Enhancing In Store Experience