This document discusses how retail organizations can leverage big data and analytics. It outlines various data sources in retail like POS systems, RFID, cameras, and social media. It then describes how this data can be used for purposes like monitoring customer behavior, promotions, and inventory management. The document also provides examples of how big data analytics can benefit sectors like security, travel, healthcare, automotive, and traffic management. Finally, it discusses various cloud tools that can be used for analytics like Pentaho, Tableau, QlikView, Microsoft Power BI, and IBM Cognos.

1. Dr Kumar Prasoon ( C.I.O Safeer Group)

7. In retail, all kinds of connected devices generate a flood of complex structured and
unstructured data.
POS Device
(Point-of-sale
Systems, used
for receiving
payments)
Near Field Communication
Devices (NFC’s) - the
technology being used to
establish radio
communication between
devices by touching them
together or bringing them in
very close proximity
PDT Devices
(Used for scanning
barcodes,
maintaining stock
etc.)
SOURCES OF DATA

8. RFID Chips Video Surveillance
Systems
Social Media Sites
– Used in marketing
of products, tracking
customer behaviour,
trends etc.
Used for the
purposes of
automatically
identifying and
tracking tags
attached to
objects
with video analytics that
record store traffic patterns,
employee-customer
interactions, and customer-
merchandise interactions
(such as the dwell time
around an end cap)
SOURCES OF DATA…

9. USES
• Monitoring.
• Customer Trajectory.
HELPS US IN
• Promotions.
• Shelf allocation.
• Shelf life cycle management.
• Product positioning
CAMERA

10. SURVEY/LOYALTY
USES
• Customer Mind map(GPOMS)
• Customer Demographics
• Customer preference
• Buying pattern
• Customer feedback
HELPS US IN
• Promotions
• Effective category management
• Product assortment
• Personalized customer service

12. PDT
CAMERAS
SURVEY/
LOYALTY
CARD
SAFEER
MEDIA
CASH
COUNTER
BIG DATA
SMART
DATA
SOCIAL
NETWORKIN
G SITES
Data
Sources
FLOW CHART
Analysis

13. Supply
Analytics
Path to
Purchase
Customer
Analytics
Retail
Analytics
• Merchandising
• Logistics
• Marketing
• E-Commerce
• Behaviour Analytics
• Purchase patterns
from point of sale
Retail Analytics

14. WHY BIG DATA IS NOT JUST AN IT CHALLENGE?

15. Travel and
Tourism
Healthcare
Automotive
Big Data
Analytics
Retail Analytics
Security
Traffic
Management

16. Smart Data in Security
• Prioritizing threats
• Stopping crime in its tracks
• Visualizing threats
http://www.cargosmarton.com/wp-content/uploads/2012/11/data_security.jpg

17. Big Data uses the
information that
customers are already
generating to provide
travel companies with
better more targeted
and customized ( and
ultimately more
profitable) services and
products
Smart Data in Travel & Tourism
http://www.bigdata.amadeus.com/

18. To gain new insight in
patient care &
early indications of
disease
Smart Data in Healthcare
http://www.ibmbigdatahub.com/blog/industry-vertical-analysis-healthcare-and-big-data

19. Smart Data Analytics in Traffic Management
To improve the
everyday life
entangled due to
our most common
problem of
sticking in traffic

20. Smart Data in Automotive Industry
•Optimizing supply chains by ensuring that
all components, parts have adequate
stock to meet the anticipated demand for
replacement parts by predicting when they
might fail, how many might fail and where
• Analyzing vehicles in the field to
predict/anticipate maintenance associated
with specific vehicles
• Monetizing gathered data by selling raw
data to rental companies, insurance
companies, public services providers, etc.
and selling reworked and aggregated data
to weather companies, web analysts, etc.

21. CLOUD TOOLS USED FOR ANALYTICS

22. Pentaho
Pros(+)
• Low Cost - Open Source Project
• Dashboards and Visualization
• Business Query Ad-hoc Reporting
• Predictive Analytics Integration
• Pixel Perfect Formatting
• Application Integration API
• Advanced Security Capabilities
• Mobile Reporting App
Cons(-)
• Requires technical resources for
implementation
• Requires plug-ins to compete with
commercial products
• User interface will be less intuitive out
of the box without customization

23. Pros(+)
• Data Volume and Scale
• Business Query Ad-hoc
• Pixel Perfect Formatting
• Scheduled Distribution
• Dashboards and Visualization
• Predictive Analytics Integration
• Portal Integration API
• Advanced SQL and Metadata
• Administration Automation
• Advanced Security Capabilities
Cons(-)
• Specialty BI tools lead in
Visualizations
• Specialty BI tools have a
simplified user interface
• Product works best with well
defined data model

24. Pros(+)
• Data Exploration
• Advanced Data Visualizations
• Intuitive End-User Interface
• Web Based Report
Development
• Mash-ups non-structured data
• High business user adoption
Cons(-)
• Data Scalability
• Limited Scheduling/Distribution
• Limited Production Reporting
• Limited Integration APIs
• Limited Stats Integration
• Limited Administration Automation

25. Pros(+)
• Excellent Dashboard Capabilities
• Ease of use for data mashups
• Little to no data modeling required
• Rapid Deployment
• Ease of Use for End User
• Auto-detect Relationships
• In-memory BI
• Data Exploration
Cons(-)
• In-Memory Architecture Data Scalability
• Limited direct Query Access
• Limited Scheduling/Distribution
• Limited Metadata/Object Reuse
• Limited Integration APIs
• Limited Administration Automation
• Enterprise readiness and scalability
• No Usages Stats integration

26. BI Magic
Quadrant
http://www.osbi.fr/wp-content/