This document discusses how companies can use big data to improve customer experiences. It notes that by 2018, most companies will overhaul their digital interfaces to support 1000-10000x more customer interactions. The value of big data comes from sensors/real-time data, analytics, and effective customer engagement. Some top challenges for data-driven companies include lack of real-time capabilities, agility, productivity, and connectivity across data silos. The presentation discusses using customer 360 degree views to increase revenue and competitive advantage through better customer experiences and engagement. It provides examples of how Domino's Pizza and other companies are using big data for real-time recommendations and personalized customer experiences. Talend is presented as a solution for unified big data integration across

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©2016 Talend Inc
Delight your Customers with Big Data
Mike Hirt, VP Engineering
@mike_hirt
22nd September 2016, BigDataExpo

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By 2018, 80% of B2C and 60% of B2B Enterprises
will overhaul their "Digital Front Door" to support
1,000–10,000 times more customers and customer
touch points
IDC FutureScape: Worldwide IT Industry 2016 Top 10 Predictions
According to IDC

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Value Drivers
Sensors/ RT
Analytics
Effective Customer
Engagement
Digital
Transformation
2016
Revenues
YoY
Growth
Faster Data Better Data
New Data
Opportunities

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SUPPLIERS
CUSTOMERS
CLOUD
SENSORS
PREMISE

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Top Challenges for Data-Driven Companies
Real-Time
• Not able to seize
opportunities or stop
threats as they occur
• Data does not move
fast enough – using
day-old or week-old
information to make
decisions
• Difficult to make
changes to IT systems
and slow to implement
business requests
• IT systems are rigid
and not flexible
Agility
• As your business
scales, IT has difficulty
keeping up
• Challenges ramping up
big data skillsets
Productivity Connectivity
• Data is trapped in silos
and not shared with the
systems and people
making decisions, lack
of self-service
capabilities across the
organization
• Data types are
expanding – especially
with sensor data

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Why the Need for Customer 360°?
Increase Revenue & Competitive Edge
• Seamless customer experience via Omni
Channel
• Deliver superior customer experience at the
point of sale / service
• Real-time customer engagement
• Customers acquisition, retention &
monetization
• Marketing campaign effectiveness
• Accurate, targeted cross-sell / up-sell
• Products & services innovation

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Domino’sCustomer 360° at Domino’s
Know each
member of
the household
Send smarter
promotions & proactively
suggest orders

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Enhanced 360° Customer View Using Big Data
“We’re becoming more of a digital e-commerce operation rather than a traditional
quick service restaurant business”

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(Revenue Growth)
Data
Integration
Master Data
Management
Data
Quality
Big Data
Application
Integration
Hadoop 2.0
Spark &
Cloud
Our mission: enable the data
driven enterprise
The most advanced Big Data
integration platform
• 10X faster development
• One solution for batch &
streaming
• Deploy machine learning in
minutes
Talend: A History of Innovation
Data
Preparation

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Big Data Without Coding
Talend Big Data Sandbox
Pre-packaged VM with real-life use cases
• Sentiments analysis
• Real-time recommendation for real-time customer
engagement
• Real-time analytics

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Sentiment Analysis

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Real-Time Recommendation
Streaming
Batch
Query
Transform
Cleanse
Govern
Transform
Cleanse
Govern
Recommendation Engine
Spark Streaming/Kafka
Spark
NoSQL
Historical ClickStream &
Demographic Data
Live ClickStream
Live Recommendations
Speed Layer
Batch Layer

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©2015 Talend Inc
Demo

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Build (simple) model

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Recommendation engine

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Recommandation result

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Benefit: Turn IoT data into real-time decisions using one product.
z NoSQL
Fast Messaging, High-scale Ingestion, Data Processing
Real-Time AnalyticsUsing Sensors/Smart Devices
AMQP

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Why Talend for Big Data?
• Unified platform, enterprise grade and
predictable pricing (no data tax)
• 900+ components & connectors (100+
for Spark) + community contribution
• True support for Big Data & IoT
scenarios (with Batch & Streaming +
Machine Learning)
• Roots in Open Source, in sync with the
Big Data ecosystem
• Future proof architecture -1 click
upgrade from MapReduce to Spark

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Recognized as a Leader in Gartner MQ for Data
Integration Tools
Magic Quadrant for the Data Integration Tools
Mark A. Beyer, Eric Thoo, Ehtisham Zaidi, Rick Greenwald
August 2016
This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated
in the context of the entire document. The Gartner document is available upon request from
http://gtnr.it/2b6jTyW. Gartner does not endorse any vendor, product or service depicted in its research
publications, and does not advise technology users to select only those vendors with the highest ratings
or other designation. Gartner research publications consist of the opinions of Gartner's research
organization and should not be construed as statements of fact. Gartner disclaims all warranties,
expressed or implied, with respect to this research, including any warranties of merchantability or fitness
for a particular purpose.
According to Gartner, Inc. “The biggest changes in
the market from 2015 are the increased demand
for data virtualization, the growing use of data
integration tools to combine "data lakes" with
existing integration solutions, and the overall
expectation that data integration will become
cloud- and on-premises-agnostic.”

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Mobile labs with sensors measure soil
elements and recommend the right
amounts of fertilizers to optimize crop
yields and reduce costs.
Helping small farmers send
their kids to school

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©2015 Talend Inc
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