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

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

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…

USES

• Monitoring.

• Customer Trajectory.

HELPS US IN

• Promotions.

• Shelf allocation.

• Shelf life cycle management.

• Product positioning

CAMERA

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

PDT

CAMERAS

SURVEY/

LOYALTY

CARD

SAFEER

MEDIA

CASH

COUNTER

BIG DATASMART

DATA

SOCIAL

NETWORKIN

G SITES

Data

Sources

FLOW CHART

Analysis

Supply

Analytics

Path to

Purchase

Customer

Analytics

Retail

Analytics

• Merchandising

• Logistics

• Marketing

• E-Commerce

• Behaviour Analytics

• Purchase patterns

from point of sale

Retail Analytics

WHY BIG DATA IS NOT JUST AN IT CHALLENGE?

Travel and

Tourism

Healthcare

Automotive

Big Data

Analytics

Retail Analytics

Security

Traffic

Management

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

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/

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

Smart Data Analytics in Traffic Management

To improve the

everyday life

entangled due to

our most common

problem of

sticking in traffic

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.

CLOUD TOOLS USED FOR ANALYTICS

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

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

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

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

BI Magic

Quadrant

http://www.osbi.fr/wp-content/