The retail industry is at the forefront of the Big Data revolution, with every point-of-sale transaction, website click, or social media post potentially revealing an insight into the customer’s preferences and buying behaviour. The capability to harness this information effectively to provide optimal pricing and enhanced customer experience can be a game-changer for retailers.

1. Organizations are increasingly grappling with the “Big
Data” problem. What is Big Data? How did “Data”
evolve into “Big Data”?
Smart devices, improved Internet connectivity, Social
Media, and cheaper storage have contributed to a
significant increase in the volume, velocity and variety of
data generated every single day. Almost 80% of this
data is unstructured – photos, videos, sound, media,
e-mail, social feeds, blogs, locations, appliance
sensors, text messages, and more. This “Big Data” is an
incredible source of intelligence for organizations and a
potential source of competitive advantage.
The retail industry is at the forefront of the Big Data revolution, with
every point-of-sale transaction, website click, or social media post
potentially revealing an insight into the customer’s preferences and
buying behavior. The capability to harness this information effec-
tively to provide optimal pricing and enhanced customer experi-
ence can be a game-changer for retailers.
The best way to unlock the power of this data is to collaborate with
an innovative, established IT partner that will extract maximum
benefits from Big Data solutions, and deliver value-added
business insights.
Syntel’s Big Data Innovation Lab has built solutions around the
pioneering open source platform—Hadoop—to empower its retail
clients with valuable insights.
enable you to acquire,
organize, and analyze Big Data in conjunction with
traditional enterprise data to drive business value.
Our services have enabled clients to:
Transform key business processes in market-
ing, merchandizing, and supply chain
Gain customer insights to enable personali-
zation and influence purchase decisions
Improve profitability through dynamic real
time pricing
Speed up reporting, analysis, and modeling
SYNTEL
BIG DATASERVICES
Capitalize on changing market dynamics—
predict trends and prepare for future
Identify business use
cases with measurable
outcomes
Start with existing data
for quick successes
Build on small
successes and integrate
with transactional
applications and web
portals
OVERVIEW OF THE
BIG DATAINNOVATION LAB’S APPROACH
Develop
organization-wide
Big data strategy
Select the right tools
and architecture for
implementation
Run Project in sprints,
with tangible and
measurable
outcomes
BIG BENEFITS
FOR THE RETAIL
INDUSTRY
BIG
DATA

2. CLIENT
SITUATION
SYNTEL
SOLUTION
Improved Customer Insight for
Online Retail Store
BIGDATA
Leverage
Syntelits Retail Clients
How does help
BUSINESS
VALUE
CLIENT
SITUATION
SYNTEL
SOLUTION
Personalized End-user Experience for
Retail Store
BUSINESS
VALUE
1 2
SYNTEL`S
BIG DATAINNOVATION LAB
• Offering end-to-end Big Data services from data management, information
delivery, to information lifecycle management
• A dedicated team of technical and business domain experts with hands-on
experience in developing and deploying Big Data solutions
• Cost-effective solutions to manage and integrate data to derive insights for
decision support
• Product capabilities, tools, accelerators, and frameworks to create tangible
business value for clients
For more information, visit us at http://www.syntelinc.com/Bigdata.aspx or call us at US: +1(248)-619-3503, UK: +44(207)-636-3587
• One of the largest online home
improvement specialty retailers in the
US
• Teradata-based business warehouse
could capture and store web-click
history for 6 months only
• Limited ability to leverage historical
data to improve product offerings and
increase profitability
• One of the largest online home
improvement specialty retailers in the US
• Inability to identify unique customers and
preferences due to duplicate customer
records and limitations of their legacy
system
• Needed a technology solution to store
millions of customer records, interface
with third-party services, and identify
duplicate and unique customers
• Re-architected the data model to store
raw data and perform aggregation in
Hadoop with aggregated results moved
to Teradata
• Developed scalable and cost-effective
Big Data solution to track and analyze
the click stream using:
• HDFS, a Hadoop distributed file system
• Hive, an SQL-like interface that
abstracts complexities of Map-Reduce
• Cassandra, a distributed columnar
NoSQL datastore
• Single source of truth for customer
definition
• Enhanced ability to provide a
personalized end-user experience
• Peer-to-peer architecture eliminated
single point of failure and reduced
application downtime
• Client saved $1 million by migrating
from legacy database (mainframe
processing cycles) to Hadoop
• Reduced batch job processing time
due to lower processing requirement
• Focused targeted marketing for better
customer satisfaction
• Ability to derive timely insights from
large historical dataset
• Client saved $500,000 by migrating
from commercial data warehouse to
HDFS
• Faster response from BI team to
business user’s requests
• Enhanced customer conversions
with cross selling ability
• Migrated data feed to HDFS and
processed it on Hadoop
• Implemented Hive for data cleansing
• Leveraged data science techniques to
develop business logic for identifying
unique customers using Map-Reduce
framework