SlideShare une entreprise Scribd logo
1  sur  27
1© Cloudera, Inc. All rights reserved.
The Big Picture: Real-time Data is
Defining Intelligent Offers
Sean Anderson, Sr. Solutions Marketing Manager
Ryan Lippert, Sr. Product Marketing Manager
2© Cloudera, Inc. All rights reserved.
We empower
people to transform complex data
into clear and actionable insights
DRIVE
CUSTOMER INSIGHTS
CONNECT
PRODUCTS & SERVICES (IoT)
PROTECT
BUSINESS
3© Cloudera, Inc. All rights reserved.
DRIVE CUSTOMER INSIGHTS CONNECT PRODUCTS & SERVICES
(IoT)
PROTECT
BUSINESS
Delivering greater value through
improved customer understanding
Powering predictive analytics to increase
performance and reduce fleet downtime
Creating new revenue streams with an
advanced anti-fraud solution
Cloudera powering data-driven customers
4© Cloudera, Inc. All rights reserved.
Cloudera Use Cases
Omni-Channel optimization
Customer analysis
Sentiment analysis
Churn analysis
Market spend analysis
Next best offer
Smart promotions
Basket analysis
Network threat detection
User/entity behavioral
analysis
Logger
Merchant fraud
Connect products & services
Predictive maintenance
Remote monitoring
Supply chain optimization
Inventory optimization
Operations optimization
Spend analytics service
Drive customer insights Protect business
5© Cloudera, Inc. All rights reserved.
Powering a Variety of Use Cases…
Targeted Marketing
Smart Promotions
Recommendation Engines
Omni-Channel
Optimization
6© Cloudera, Inc. All rights reserved.
✓ Breaking down data silos
✓ Sharing data in accordance with
privacy and regulatory policies
✓ Becoming iterative, lean and
leveraging knowledge across the
business
Creating True Customer 360
7© Cloudera, Inc. All rights reserved.
Customer 360 Journey
• Marketing Systems
(Salesforce, Omniture,
CRM)
• Clickstream Data
Primary
Data
Source
• Clickstream
• NPS Systems
• Support Call Logs
• Social Feeds
Primary
Data
Source
Understand Your Customer Learn Behaviors Improve Interactions
• Shopping Cart Platforms
• Geolocation
Primary
Data
Source
8© Cloudera, Inc. All rights reserved.
The Data Journey
Collate the Data Sources Micro-Segmentation
Drive Personalized
Campaigns
Devise Micro- segments based
on combining multiple factors:
• Age
• Location
• Spending History
• Channel Preferences
• Content Preferences
• Apps Usage
• Social Influence
• Churn Score
• Lifetime Value
• Usage Patterns
• Data Usage
Drive Personalized Campaigns for
specific micro-segments
Retention campaign for high value
customers with iPhone who
recently shared a negative social
sentiment
Upsell campaign for high-data users
with family to move over to a family
bundle
Geo-Location based targeted
advertising for specific customer
micro-segments
9© Cloudera, Inc. All rights reserved.
How to Iteratively Build a True Customer 360?
Customer
Data
Source
Start with ingesting
the “best” version of
your customer profile
Find your common
identifiers across
datasets: customer
name, number, IMEI,
IMSI
IMEI
ChannelsPurchase
History
Add New Data Source
Common
Identifier
Current Source
Enrich with additional
demographic information
(purchase history or
channels) from other
systems / sources
Deliver A Use Case
Deliver a specific use case
based on the profile with
new data sets:
• Customer Lifetime value
• Next Best offer
• Omni Channel
Enrich Your Profile
• Enrich your customer
profiles with
purchase behavior
• Continue to enhance
with each new use
case
Location Clickstream
Continue to add new data sources iteratively to
enhance your customer profile with new use
cases
Call center
Social Media Apps
External
Data
New Data Sources
10© Cloudera, Inc. All rights reserved.
Three Scenarios
11© Cloudera, Inc. All rights reserved.
Three Scenarios – Event Modelling in Real Time
Events trigger changes in purchasing
preferences among customers and potential
customers.
However, most NBO frameworks are based on
historic data models. Historic models handle a
baseline of behavior/information well, but
struggle to optimize in the moment for events.
By incorporating the real-time behavior of users
back into the model on a rolling basis,
companies can capture the opportunity these
events present.
12© Cloudera, Inc. All rights reserved.
56% of all customer interactions happen
during a multi-channel, multi-event journey.
Companies that put data at the center of their
marketing and sales decisions improve their
marketing returns by 15-20% adding up to
150 to 20 billion in additional revenue.
Research states that personalized emails
improve click through rates by 14% and
conversation rates by 10%
Over 96% of organizations believe that email
personalization can improve email marketing
performance.
Three Scenarios - Email Personalization
13© Cloudera, Inc. All rights reserved.
HEB is the largest grocery chain in the state of
Texas.
When employees couldn't get to work, some
stores still operated with as few as five people
Hurricane impact projections are often not
accurate which means HEB had to plan for the
worst and leverage real-time data to make
shipping and staffing decisions.
Certain items become in high demand during a
hurricane, while other experience almost no-
demand (Frozen Foods, Flowers)
HEB leveraged real-time data to plan special
shipments which arrived before state and
federal aide.
Three Scenarios - Hurricane Harvey
14© Cloudera, Inc. All rights reserved.
The Platform
15© Cloudera, Inc. All rights reserved.
Cloudera Enterprise – The Platform for Customer 360
Location
Social
Clickstream
BI Tools
Online & Mobile Apps
Billing/
Ordering
CRM/ Profile
Marketing
Campaigns
Search
EDW
N/W
Logs
Call Center
Apps
Network
Other
Structured
Sources
Internal Systems External Sources
BI Solutions Real-Time
Apps
Search Data Science
Workbench
SQL
Machine
Learning
Systems Data
16© Cloudera, Inc. All rights reserved.
Key Enabling Capabilities
Ideal for real-time analytics on
IoT and time series data.
Simplifies Lambda architectures
for running real-time analytics on
streaming data
Leading analytic SQL engine
running natively in Hadoop. Impala
provides the fastest insights, at
high-concurrency, with the familiar
access necessary for powering BI
and analytics across the business.
Kudu: Real-Time Offers Impala: Self Service BI Data Science Workbench
Collaborative hub for enterprise
data science and an integrated
development environment for
running Python, R, & Scala with
support for Spark
17© Cloudera, Inc. All rights reserved.
• Serve real-time data at scale for real-time decision making
• Aggregate relational, NoSQL, structured & unstructured data
• Stream processing & analytics on changing operational data
• Leverage linear performance scalability and predictable TCO
• Deliver a secure, low-latency, high-concurrency experience
Extract real-time insights from big data
OPERATIONAL
DATABASE
18© Cloudera, Inc. All rights reserved.
The Underlying Driver
What drives a use case to real-time?
High Frequency
Trading
APT Detection
Fraud Detection
Predictive Maintenance
Next Best Offer
Inventory Management
Shipping/Logistic
Systems
CRM Systems
Employee Management
Strategic Planning
Real-time data management use cases are
defined by a common set of characteristics.
• Narrow time window in which to make a decision
(automated or manual)
• Opportunity for data points to change the decision,
and thus the business’s path
• Decreasing value of data over time
Not all use cases have a pressing need for
real-time data.
• Broader strategic decisions, for example, do not
require real-time data input
• Over time, decreases in HW costs and increases in
availability of real-time systems will lead most use
cases to be conducted in real-time
Real Time
Some
Latency
Acceptable
19© Cloudera, Inc. All rights reserved.
Managing Data from Customer Touchpoints
Handle real-time
data ingest from
diverse sources
Fundamentall
y Secure
Data Streams
Deployment Flexibility
Machine Learning
Capabilities
Diverse Analytical
OptionsCombine data from various sources
Customer Data Mgmt.
Hub
Scale easily & Cost
effectively
Batch or Real- time
Data Streams
A comprehensive data management platform to drive business insights from data
Data Sources
Data Storage &
Processing
Serving, Analytics &
Machine Learning
Data Ingest
Data Sources
Security, Scalability & Easy Management
20© Cloudera, Inc. All rights reserved.
The Right Storage Technology to Meet Your Use Case
Real-Time Inputs Real-Time Analytics
Input data is pushed into a
semi-static model of a well-
defined process, resulting in the
selection of an optimal strategy
given the known variables.
Input data itself becomes part of
the model, continuously
evolving (within boundaries) as
behaviors change and new
connections are identified.
21© Cloudera, Inc. All rights reserved.
Operational DB: Real-Time Architecture
Driving the Model Through Machine Learning
Kafka
Spark
Streaming
Kudu
Spark MLlib
Input Data
Addt’l
Sources
Individual Session
Full Model/Learning
Genesis
Spark
1 Event
Occurs
2
Messaging
3
Stream
Processing 4
Land in
Relational
Store
5
Apply ML
Libraries
22© Cloudera, Inc. All rights reserved.
Operational DB: Real-Time Architecture
MLlib & K-Means: Defining Microsegments via Machine Learning
Height
Weight
Height
Weight
1 2
Height
Weight
3
Height
Weight
4
L
M
S
XL
L
M
S
XS
Near
Custom
?
23© Cloudera, Inc. All rights reserved.
Operational DB: Real-Time Architecture
Driving Prediction and Optimization
Kafka
Spark
Streaming
Kudu
Spark MLlib
Input Data
Addt’l
Sources
Individual Session
1
Data
Processed
Genesis
Spark
2
Request Processed/
Kudu Queried
3
4
Results
Returned
Results
Processed
5
Processed
Data
Returned
Full Model/Learning
24© Cloudera, Inc. All rights reserved.
Operational DB: Real-Time Architecture
Driving Prediction and Optimization
Step 1: Data Processed
Apache Spark processes the data from the event (car sensors, manufacturing,
wearables, etc), which potentially involves keeping a running list of the last X
number of events
Step 2: Request Processed/Kudu Queried
A Spark application uses the data gathered in step one to query Kudu’s database
in a predefined manner to look for similar patterns defined via machine learning
Step 3: Kudu Results Returned
Kudu returns the results from the query in step 2 back to Spark to determine what
needs to be returned to the application
Step 4: Results Processed
Spark associates the results from Kudu with the information stored from the
current event to determine the next step to feed back to the application
Step 5: Processed Data Returned
The machine-generated, best possible outcome is prescribed and served to the
application
25© Cloudera, Inc. All rights reserved.
Operational DB: NBO Use Case
Prediction and Optimization
Kafka
Spark
Streaming
Kudu
Spark MLlib
Application
Addt’l
Sources
Individual Session
User Shopping
Spark
Full Model/Learning
Data Request Sent For Stream Processing
Data Cleaned/Ordered/Processed, Then
Delivered to Kudu for Modelling
Automated processes based on machine
learning enable prediction and
optimization at a new level.
Illustrative,
models will
likely have
>2
dimensions
26© Cloudera, Inc. All rights reserved.
Visit:
Solutions
Gallery
27© Cloudera, Inc. All rights reserved.
Thank you
Sean Anderson, Sr. Solutions Marketing
Manager
Ryan Lippert, Sr. Product Marketing Manager

Contenu connexe

Tendances

Extending BI with Big Data Analytics
Extending BI with Big Data AnalyticsExtending BI with Big Data Analytics
Extending BI with Big Data AnalyticsDatameer
 
Increase your ROI with Hadoop in Six Months - Presented by Dell, Cloudera and...
Increase your ROI with Hadoop in Six Months - Presented by Dell, Cloudera and...Increase your ROI with Hadoop in Six Months - Presented by Dell, Cloudera and...
Increase your ROI with Hadoop in Six Months - Presented by Dell, Cloudera and...Cloudera, Inc.
 
Modernizing Architecture for a Complete Data Strategy
Modernizing Architecture for a Complete Data StrategyModernizing Architecture for a Complete Data Strategy
Modernizing Architecture for a Complete Data StrategyCloudera, Inc.
 
Informatica Becomes Part of the Business Data Lake Ecosystem
Informatica Becomes Part of the Business Data Lake EcosystemInformatica Becomes Part of the Business Data Lake Ecosystem
Informatica Becomes Part of the Business Data Lake EcosystemCapgemini
 
The Vortex of Change - Digital Transformation (Presented by Intel)
The Vortex of Change - Digital Transformation (Presented by Intel)The Vortex of Change - Digital Transformation (Presented by Intel)
The Vortex of Change - Digital Transformation (Presented by Intel)Cloudera, Inc.
 
Optimizing Regulatory Compliance with Big Data
Optimizing Regulatory Compliance with Big DataOptimizing Regulatory Compliance with Big Data
Optimizing Regulatory Compliance with Big DataCloudera, Inc.
 
Traditional BI vs. Business Data Lake – A Comparison
Traditional BI vs. Business Data Lake – A ComparisonTraditional BI vs. Business Data Lake – A Comparison
Traditional BI vs. Business Data Lake – A ComparisonCapgemini
 
The State of Big Data Adoption: A Glance at Top Industries Adopting Big Data ...
The State of Big Data Adoption: A Glance at Top Industries Adopting Big Data ...The State of Big Data Adoption: A Glance at Top Industries Adopting Big Data ...
The State of Big Data Adoption: A Glance at Top Industries Adopting Big Data ...Datameer
 
Markerstudy Group Drives Growth and Innovation
Markerstudy Group Drives Growth and InnovationMarkerstudy Group Drives Growth and Innovation
Markerstudy Group Drives Growth and InnovationCloudera, Inc.
 
IoT-Enabled Predictive Maintenance
IoT-Enabled Predictive MaintenanceIoT-Enabled Predictive Maintenance
IoT-Enabled Predictive MaintenanceCloudera, Inc.
 
Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021
Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021
Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021Tristan Baker
 
Digital Government: Data + Government Isn't Enough | Wrangle Conference 2017
Digital Government: Data + Government Isn't Enough | Wrangle Conference 2017Digital Government: Data + Government Isn't Enough | Wrangle Conference 2017
Digital Government: Data + Government Isn't Enough | Wrangle Conference 2017Cloudera, Inc.
 
Data Science in Enterprise
Data Science in EnterpriseData Science in Enterprise
Data Science in EnterpriseJosh Yeh
 
Big datacamp june14_alex_liu
Big datacamp june14_alex_liuBig datacamp june14_alex_liu
Big datacamp june14_alex_liuData Con LA
 
Using Big Data to Transform Your Customer’s Experience - Part 1

Using Big Data to Transform Your Customer’s Experience - Part 1
Using Big Data to Transform Your Customer’s Experience - Part 1

Using Big Data to Transform Your Customer’s Experience - Part 1
Cloudera, Inc.
 
Best Practices in Implementing Social and Mobile CX for Utilities
Best Practices in Implementing Social and Mobile CX for UtilitiesBest Practices in Implementing Social and Mobile CX for Utilities
Best Practices in Implementing Social and Mobile CX for UtilitiesCapgemini
 
Transform Banking with Big Data and Automated Machine Learning 9.12.17
Transform Banking with Big Data and Automated Machine Learning 9.12.17Transform Banking with Big Data and Automated Machine Learning 9.12.17
Transform Banking with Big Data and Automated Machine Learning 9.12.17Cloudera, Inc.
 

Tendances (20)

Extending BI with Big Data Analytics
Extending BI with Big Data AnalyticsExtending BI with Big Data Analytics
Extending BI with Big Data Analytics
 
Increase your ROI with Hadoop in Six Months - Presented by Dell, Cloudera and...
Increase your ROI with Hadoop in Six Months - Presented by Dell, Cloudera and...Increase your ROI with Hadoop in Six Months - Presented by Dell, Cloudera and...
Increase your ROI with Hadoop in Six Months - Presented by Dell, Cloudera and...
 
Modernizing Architecture for a Complete Data Strategy
Modernizing Architecture for a Complete Data StrategyModernizing Architecture for a Complete Data Strategy
Modernizing Architecture for a Complete Data Strategy
 
Informatica Becomes Part of the Business Data Lake Ecosystem
Informatica Becomes Part of the Business Data Lake EcosystemInformatica Becomes Part of the Business Data Lake Ecosystem
Informatica Becomes Part of the Business Data Lake Ecosystem
 
Operational Analytics
Operational AnalyticsOperational Analytics
Operational Analytics
 
The Vortex of Change - Digital Transformation (Presented by Intel)
The Vortex of Change - Digital Transformation (Presented by Intel)The Vortex of Change - Digital Transformation (Presented by Intel)
The Vortex of Change - Digital Transformation (Presented by Intel)
 
Infrastructure Matters
Infrastructure MattersInfrastructure Matters
Infrastructure Matters
 
Optimizing Regulatory Compliance with Big Data
Optimizing Regulatory Compliance with Big DataOptimizing Regulatory Compliance with Big Data
Optimizing Regulatory Compliance with Big Data
 
Traditional BI vs. Business Data Lake – A Comparison
Traditional BI vs. Business Data Lake – A ComparisonTraditional BI vs. Business Data Lake – A Comparison
Traditional BI vs. Business Data Lake – A Comparison
 
The State of Big Data Adoption: A Glance at Top Industries Adopting Big Data ...
The State of Big Data Adoption: A Glance at Top Industries Adopting Big Data ...The State of Big Data Adoption: A Glance at Top Industries Adopting Big Data ...
The State of Big Data Adoption: A Glance at Top Industries Adopting Big Data ...
 
Markerstudy Group Drives Growth and Innovation
Markerstudy Group Drives Growth and InnovationMarkerstudy Group Drives Growth and Innovation
Markerstudy Group Drives Growth and Innovation
 
IoT-Enabled Predictive Maintenance
IoT-Enabled Predictive MaintenanceIoT-Enabled Predictive Maintenance
IoT-Enabled Predictive Maintenance
 
Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021
Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021
Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021
 
Big Data & Analytics Day
Big Data & Analytics Day Big Data & Analytics Day
Big Data & Analytics Day
 
Digital Government: Data + Government Isn't Enough | Wrangle Conference 2017
Digital Government: Data + Government Isn't Enough | Wrangle Conference 2017Digital Government: Data + Government Isn't Enough | Wrangle Conference 2017
Digital Government: Data + Government Isn't Enough | Wrangle Conference 2017
 
Data Science in Enterprise
Data Science in EnterpriseData Science in Enterprise
Data Science in Enterprise
 
Big datacamp june14_alex_liu
Big datacamp june14_alex_liuBig datacamp june14_alex_liu
Big datacamp june14_alex_liu
 
Using Big Data to Transform Your Customer’s Experience - Part 1

Using Big Data to Transform Your Customer’s Experience - Part 1
Using Big Data to Transform Your Customer’s Experience - Part 1

Using Big Data to Transform Your Customer’s Experience - Part 1

 
Best Practices in Implementing Social and Mobile CX for Utilities
Best Practices in Implementing Social and Mobile CX for UtilitiesBest Practices in Implementing Social and Mobile CX for Utilities
Best Practices in Implementing Social and Mobile CX for Utilities
 
Transform Banking with Big Data and Automated Machine Learning 9.12.17
Transform Banking with Big Data and Automated Machine Learning 9.12.17Transform Banking with Big Data and Automated Machine Learning 9.12.17
Transform Banking with Big Data and Automated Machine Learning 9.12.17
 

En vedette

IoT - Data Management Trends, Best Practices, & Use Cases
IoT - Data Management Trends, Best Practices, & Use CasesIoT - Data Management Trends, Best Practices, & Use Cases
IoT - Data Management Trends, Best Practices, & Use CasesCloudera, Inc.
 
Large-Scale Data Science on Hadoop (Intel Big Data Day)
Large-Scale Data Science on Hadoop (Intel Big Data Day)Large-Scale Data Science on Hadoop (Intel Big Data Day)
Large-Scale Data Science on Hadoop (Intel Big Data Day)Uri Laserson
 
Non-geek's big data playbook - Hadoop & EDW - SAS Best Practices
Non-geek's big data playbook - Hadoop & EDW - SAS Best PracticesNon-geek's big data playbook - Hadoop & EDW - SAS Best Practices
Non-geek's big data playbook - Hadoop & EDW - SAS Best PracticesJyrki Määttä
 
Cloudera Customer Success Story
Cloudera Customer Success StoryCloudera Customer Success Story
Cloudera Customer Success StoryXpand IT
 
How to Build Multi-disciplinary Analytics Applications on a Shared Data Platform
How to Build Multi-disciplinary Analytics Applications on a Shared Data PlatformHow to Build Multi-disciplinary Analytics Applications on a Shared Data Platform
How to Build Multi-disciplinary Analytics Applications on a Shared Data PlatformCloudera, Inc.
 
Real Time Data Processing using Spark Streaming | Data Day Texas 2015
Real Time Data Processing using Spark Streaming | Data Day Texas 2015Real Time Data Processing using Spark Streaming | Data Day Texas 2015
Real Time Data Processing using Spark Streaming | Data Day Texas 2015Cloudera, Inc.
 
Webinar - Sehr empfehlenswert: wie man aus Daten durch maschinelles Lernen We...
Webinar - Sehr empfehlenswert: wie man aus Daten durch maschinelles Lernen We...Webinar - Sehr empfehlenswert: wie man aus Daten durch maschinelles Lernen We...
Webinar - Sehr empfehlenswert: wie man aus Daten durch maschinelles Lernen We...Cloudera, Inc.
 

En vedette (7)

IoT - Data Management Trends, Best Practices, & Use Cases
IoT - Data Management Trends, Best Practices, & Use CasesIoT - Data Management Trends, Best Practices, & Use Cases
IoT - Data Management Trends, Best Practices, & Use Cases
 
Large-Scale Data Science on Hadoop (Intel Big Data Day)
Large-Scale Data Science on Hadoop (Intel Big Data Day)Large-Scale Data Science on Hadoop (Intel Big Data Day)
Large-Scale Data Science on Hadoop (Intel Big Data Day)
 
Non-geek's big data playbook - Hadoop & EDW - SAS Best Practices
Non-geek's big data playbook - Hadoop & EDW - SAS Best PracticesNon-geek's big data playbook - Hadoop & EDW - SAS Best Practices
Non-geek's big data playbook - Hadoop & EDW - SAS Best Practices
 
Cloudera Customer Success Story
Cloudera Customer Success StoryCloudera Customer Success Story
Cloudera Customer Success Story
 
How to Build Multi-disciplinary Analytics Applications on a Shared Data Platform
How to Build Multi-disciplinary Analytics Applications on a Shared Data PlatformHow to Build Multi-disciplinary Analytics Applications on a Shared Data Platform
How to Build Multi-disciplinary Analytics Applications on a Shared Data Platform
 
Real Time Data Processing using Spark Streaming | Data Day Texas 2015
Real Time Data Processing using Spark Streaming | Data Day Texas 2015Real Time Data Processing using Spark Streaming | Data Day Texas 2015
Real Time Data Processing using Spark Streaming | Data Day Texas 2015
 
Webinar - Sehr empfehlenswert: wie man aus Daten durch maschinelles Lernen We...
Webinar - Sehr empfehlenswert: wie man aus Daten durch maschinelles Lernen We...Webinar - Sehr empfehlenswert: wie man aus Daten durch maschinelles Lernen We...
Webinar - Sehr empfehlenswert: wie man aus Daten durch maschinelles Lernen We...
 

Similaire à The Big Picture: Real-time Data is Defining Intelligent Offers

Using Big Data to Drive Customer 360
Using Big Data to Drive Customer 360Using Big Data to Drive Customer 360
Using Big Data to Drive Customer 360Cloudera, Inc.
 
The role of Big Data and Modern Data Management in Driving a Customer 360 fro...
The role of Big Data and Modern Data Management in Driving a Customer 360 fro...The role of Big Data and Modern Data Management in Driving a Customer 360 fro...
The role of Big Data and Modern Data Management in Driving a Customer 360 fro...Cloudera, Inc.
 
Driving Better Products with Customer Intelligence

Driving Better Products with Customer Intelligence
Driving Better Products with Customer Intelligence

Driving Better Products with Customer Intelligence
Cloudera, Inc.
 
Real-Time Analytics for Industries
Real-Time Analytics for IndustriesReal-Time Analytics for Industries
Real-Time Analytics for IndustriesAvadhoot Patwardhan
 
Entry Points – How to Get Rolling with Big Data Analytics
Entry Points – How to Get Rolling with Big Data AnalyticsEntry Points – How to Get Rolling with Big Data Analytics
Entry Points – How to Get Rolling with Big Data AnalyticsInside Analysis
 
The Big Picture: Learned Behaviors in Churn
The Big Picture: Learned Behaviors in ChurnThe Big Picture: Learned Behaviors in Churn
The Big Picture: Learned Behaviors in ChurnCloudera, Inc.
 
Chp11 Business Intelligence
Chp11 Business IntelligenceChp11 Business Intelligence
Chp11 Business IntelligenceChuong Nguyen
 
Think Like Your Customer
Think Like Your CustomerThink Like Your Customer
Think Like Your CustomerIBM Analytics
 
Think like your customer
Think like your customerThink like your customer
Think like your customerTrisha Dutta
 
How to Become an Analytics Ready Insurer - with Informatica and Hortonworks
How to Become an Analytics Ready Insurer - with Informatica and HortonworksHow to Become an Analytics Ready Insurer - with Informatica and Hortonworks
How to Become an Analytics Ready Insurer - with Informatica and HortonworksHortonworks
 
Réinventez le Data Management avec la Data Virtualization de Denodo
Réinventez le Data Management avec la Data Virtualization de DenodoRéinventez le Data Management avec la Data Virtualization de Denodo
Réinventez le Data Management avec la Data Virtualization de DenodoDenodo
 
IBM's big data seminar programme -moving beyond Hadoop - Ian Radmore, IBM
IBM's big data seminar programme -moving beyond Hadoop - Ian Radmore, IBMIBM's big data seminar programme -moving beyond Hadoop - Ian Radmore, IBM
IBM's big data seminar programme -moving beyond Hadoop - Ian Radmore, IBMInternet World
 
Leverage Machine Data
Leverage Machine DataLeverage Machine Data
Leverage Machine DataSplunk
 
Big Data & Analytics, Peter Jönsson
Big Data & Analytics, Peter JönssonBig Data & Analytics, Peter Jönsson
Big Data & Analytics, Peter JönssonIBM Danmark
 
Disrupting Insurance with Advanced Analytics The Next Generation Carrier
Disrupting Insurance with Advanced Analytics The Next Generation CarrierDisrupting Insurance with Advanced Analytics The Next Generation Carrier
Disrupting Insurance with Advanced Analytics The Next Generation CarrierDataWorks Summit/Hadoop Summit
 
Data analytics as a service
Data analytics as a serviceData analytics as a service
Data analytics as a serviceStanley Wang
 
How to Wrangle Data for Machine Learning on AWS
 How to Wrangle Data for Machine Learning on AWS How to Wrangle Data for Machine Learning on AWS
How to Wrangle Data for Machine Learning on AWSAmazon Web Services
 

Similaire à The Big Picture: Real-time Data is Defining Intelligent Offers (20)

Using Big Data to Drive Customer 360
Using Big Data to Drive Customer 360Using Big Data to Drive Customer 360
Using Big Data to Drive Customer 360
 
The role of Big Data and Modern Data Management in Driving a Customer 360 fro...
The role of Big Data and Modern Data Management in Driving a Customer 360 fro...The role of Big Data and Modern Data Management in Driving a Customer 360 fro...
The role of Big Data and Modern Data Management in Driving a Customer 360 fro...
 
Driving Better Products with Customer Intelligence

Driving Better Products with Customer Intelligence
Driving Better Products with Customer Intelligence

Driving Better Products with Customer Intelligence

 
Real-Time Analytics for Industries
Real-Time Analytics for IndustriesReal-Time Analytics for Industries
Real-Time Analytics for Industries
 
Entry Points – How to Get Rolling with Big Data Analytics
Entry Points – How to Get Rolling with Big Data AnalyticsEntry Points – How to Get Rolling with Big Data Analytics
Entry Points – How to Get Rolling with Big Data Analytics
 
The Big Picture: Learned Behaviors in Churn
The Big Picture: Learned Behaviors in ChurnThe Big Picture: Learned Behaviors in Churn
The Big Picture: Learned Behaviors in Churn
 
Big Data use cases in telcos
Big Data use cases in telcosBig Data use cases in telcos
Big Data use cases in telcos
 
Big Data use cases in telcos
Big Data use cases in telcosBig Data use cases in telcos
Big Data use cases in telcos
 
Chp11 Business Intelligence
Chp11 Business IntelligenceChp11 Business Intelligence
Chp11 Business Intelligence
 
Think Like Your Customer
Think Like Your CustomerThink Like Your Customer
Think Like Your Customer
 
Think like your customer
Think like your customerThink like your customer
Think like your customer
 
How to Become an Analytics Ready Insurer - with Informatica and Hortonworks
How to Become an Analytics Ready Insurer - with Informatica and HortonworksHow to Become an Analytics Ready Insurer - with Informatica and Hortonworks
How to Become an Analytics Ready Insurer - with Informatica and Hortonworks
 
Réinventez le Data Management avec la Data Virtualization de Denodo
Réinventez le Data Management avec la Data Virtualization de DenodoRéinventez le Data Management avec la Data Virtualization de Denodo
Réinventez le Data Management avec la Data Virtualization de Denodo
 
IBM's big data seminar programme -moving beyond Hadoop - Ian Radmore, IBM
IBM's big data seminar programme -moving beyond Hadoop - Ian Radmore, IBMIBM's big data seminar programme -moving beyond Hadoop - Ian Radmore, IBM
IBM's big data seminar programme -moving beyond Hadoop - Ian Radmore, IBM
 
Big Data & Analytics – beyond Hadoop
Big Data & Analytics – beyond HadoopBig Data & Analytics – beyond Hadoop
Big Data & Analytics – beyond Hadoop
 
Leverage Machine Data
Leverage Machine DataLeverage Machine Data
Leverage Machine Data
 
Big Data & Analytics, Peter Jönsson
Big Data & Analytics, Peter JönssonBig Data & Analytics, Peter Jönsson
Big Data & Analytics, Peter Jönsson
 
Disrupting Insurance with Advanced Analytics The Next Generation Carrier
Disrupting Insurance with Advanced Analytics The Next Generation CarrierDisrupting Insurance with Advanced Analytics The Next Generation Carrier
Disrupting Insurance with Advanced Analytics The Next Generation Carrier
 
Data analytics as a service
Data analytics as a serviceData analytics as a service
Data analytics as a service
 
How to Wrangle Data for Machine Learning on AWS
 How to Wrangle Data for Machine Learning on AWS How to Wrangle Data for Machine Learning on AWS
How to Wrangle Data for Machine Learning on AWS
 

Plus de Cloudera, Inc.

Partner Briefing_January 25 (FINAL).pptx
Partner Briefing_January 25 (FINAL).pptxPartner Briefing_January 25 (FINAL).pptx
Partner Briefing_January 25 (FINAL).pptxCloudera, Inc.
 
Cloudera Data Impact Awards 2021 - Finalists
Cloudera Data Impact Awards 2021 - Finalists Cloudera Data Impact Awards 2021 - Finalists
Cloudera Data Impact Awards 2021 - Finalists Cloudera, Inc.
 
2020 Cloudera Data Impact Awards Finalists
2020 Cloudera Data Impact Awards Finalists2020 Cloudera Data Impact Awards Finalists
2020 Cloudera Data Impact Awards FinalistsCloudera, Inc.
 
Edc event vienna presentation 1 oct 2019
Edc event vienna presentation 1 oct 2019Edc event vienna presentation 1 oct 2019
Edc event vienna presentation 1 oct 2019Cloudera, Inc.
 
Machine Learning with Limited Labeled Data 4/3/19
Machine Learning with Limited Labeled Data 4/3/19Machine Learning with Limited Labeled Data 4/3/19
Machine Learning with Limited Labeled Data 4/3/19Cloudera, Inc.
 
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Data Driven With the Cloudera Modern Data Warehouse 3.19.19Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Data Driven With the Cloudera Modern Data Warehouse 3.19.19Cloudera, Inc.
 
Introducing Cloudera DataFlow (CDF) 2.13.19
Introducing Cloudera DataFlow (CDF) 2.13.19Introducing Cloudera DataFlow (CDF) 2.13.19
Introducing Cloudera DataFlow (CDF) 2.13.19Cloudera, Inc.
 
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Introducing Cloudera Data Science Workbench for HDP 2.12.19Introducing Cloudera Data Science Workbench for HDP 2.12.19
Introducing Cloudera Data Science Workbench for HDP 2.12.19Cloudera, Inc.
 
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19Cloudera, Inc.
 
Leveraging the cloud for analytics and machine learning 1.29.19
Leveraging the cloud for analytics and machine learning 1.29.19Leveraging the cloud for analytics and machine learning 1.29.19
Leveraging the cloud for analytics and machine learning 1.29.19Cloudera, Inc.
 
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19Cloudera, Inc.
 
Leveraging the Cloud for Big Data Analytics 12.11.18
Leveraging the Cloud for Big Data Analytics 12.11.18Leveraging the Cloud for Big Data Analytics 12.11.18
Leveraging the Cloud for Big Data Analytics 12.11.18Cloudera, Inc.
 
Modern Data Warehouse Fundamentals Part 3
Modern Data Warehouse Fundamentals Part 3Modern Data Warehouse Fundamentals Part 3
Modern Data Warehouse Fundamentals Part 3Cloudera, Inc.
 
Modern Data Warehouse Fundamentals Part 2
Modern Data Warehouse Fundamentals Part 2Modern Data Warehouse Fundamentals Part 2
Modern Data Warehouse Fundamentals Part 2Cloudera, Inc.
 
Modern Data Warehouse Fundamentals Part 1
Modern Data Warehouse Fundamentals Part 1Modern Data Warehouse Fundamentals Part 1
Modern Data Warehouse Fundamentals Part 1Cloudera, Inc.
 
Extending Cloudera SDX beyond the Platform
Extending Cloudera SDX beyond the PlatformExtending Cloudera SDX beyond the Platform
Extending Cloudera SDX beyond the PlatformCloudera, Inc.
 
Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18Cloudera, Inc.
 
Analyst Webinar: Doing a 180 on Customer 360
Analyst Webinar: Doing a 180 on Customer 360Analyst Webinar: Doing a 180 on Customer 360
Analyst Webinar: Doing a 180 on Customer 360Cloudera, Inc.
 
Build a modern platform for anti-money laundering 9.19.18
Build a modern platform for anti-money laundering 9.19.18Build a modern platform for anti-money laundering 9.19.18
Build a modern platform for anti-money laundering 9.19.18Cloudera, Inc.
 
Introducing the data science sandbox as a service 8.30.18
Introducing the data science sandbox as a service 8.30.18Introducing the data science sandbox as a service 8.30.18
Introducing the data science sandbox as a service 8.30.18Cloudera, Inc.
 

Plus de Cloudera, Inc. (20)

Partner Briefing_January 25 (FINAL).pptx
Partner Briefing_January 25 (FINAL).pptxPartner Briefing_January 25 (FINAL).pptx
Partner Briefing_January 25 (FINAL).pptx
 
Cloudera Data Impact Awards 2021 - Finalists
Cloudera Data Impact Awards 2021 - Finalists Cloudera Data Impact Awards 2021 - Finalists
Cloudera Data Impact Awards 2021 - Finalists
 
2020 Cloudera Data Impact Awards Finalists
2020 Cloudera Data Impact Awards Finalists2020 Cloudera Data Impact Awards Finalists
2020 Cloudera Data Impact Awards Finalists
 
Edc event vienna presentation 1 oct 2019
Edc event vienna presentation 1 oct 2019Edc event vienna presentation 1 oct 2019
Edc event vienna presentation 1 oct 2019
 
Machine Learning with Limited Labeled Data 4/3/19
Machine Learning with Limited Labeled Data 4/3/19Machine Learning with Limited Labeled Data 4/3/19
Machine Learning with Limited Labeled Data 4/3/19
 
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Data Driven With the Cloudera Modern Data Warehouse 3.19.19Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
 
Introducing Cloudera DataFlow (CDF) 2.13.19
Introducing Cloudera DataFlow (CDF) 2.13.19Introducing Cloudera DataFlow (CDF) 2.13.19
Introducing Cloudera DataFlow (CDF) 2.13.19
 
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Introducing Cloudera Data Science Workbench for HDP 2.12.19Introducing Cloudera Data Science Workbench for HDP 2.12.19
Introducing Cloudera Data Science Workbench for HDP 2.12.19
 
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
 
Leveraging the cloud for analytics and machine learning 1.29.19
Leveraging the cloud for analytics and machine learning 1.29.19Leveraging the cloud for analytics and machine learning 1.29.19
Leveraging the cloud for analytics and machine learning 1.29.19
 
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
 
Leveraging the Cloud for Big Data Analytics 12.11.18
Leveraging the Cloud for Big Data Analytics 12.11.18Leveraging the Cloud for Big Data Analytics 12.11.18
Leveraging the Cloud for Big Data Analytics 12.11.18
 
Modern Data Warehouse Fundamentals Part 3
Modern Data Warehouse Fundamentals Part 3Modern Data Warehouse Fundamentals Part 3
Modern Data Warehouse Fundamentals Part 3
 
Modern Data Warehouse Fundamentals Part 2
Modern Data Warehouse Fundamentals Part 2Modern Data Warehouse Fundamentals Part 2
Modern Data Warehouse Fundamentals Part 2
 
Modern Data Warehouse Fundamentals Part 1
Modern Data Warehouse Fundamentals Part 1Modern Data Warehouse Fundamentals Part 1
Modern Data Warehouse Fundamentals Part 1
 
Extending Cloudera SDX beyond the Platform
Extending Cloudera SDX beyond the PlatformExtending Cloudera SDX beyond the Platform
Extending Cloudera SDX beyond the Platform
 
Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18
 
Analyst Webinar: Doing a 180 on Customer 360
Analyst Webinar: Doing a 180 on Customer 360Analyst Webinar: Doing a 180 on Customer 360
Analyst Webinar: Doing a 180 on Customer 360
 
Build a modern platform for anti-money laundering 9.19.18
Build a modern platform for anti-money laundering 9.19.18Build a modern platform for anti-money laundering 9.19.18
Build a modern platform for anti-money laundering 9.19.18
 
Introducing the data science sandbox as a service 8.30.18
Introducing the data science sandbox as a service 8.30.18Introducing the data science sandbox as a service 8.30.18
Introducing the data science sandbox as a service 8.30.18
 

Dernier

UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UbiTrack UK
 
Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Adtran
 
Computer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsComputer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsSeth Reyes
 
UiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation DevelopersUiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation DevelopersUiPathCommunity
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemAsko Soukka
 
Building AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxBuilding AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxUdaiappa Ramachandran
 
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfMachine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfAijun Zhang
 
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...Aggregage
 
Comparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioComparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioChristian Posta
 
Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024D Cloud Solutions
 
20230202 - Introduction to tis-py
20230202 - Introduction to tis-py20230202 - Introduction to tis-py
20230202 - Introduction to tis-pyJamie (Taka) Wang
 
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAAnypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAshyamraj55
 
Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )Brian Pichman
 
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDEADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDELiveplex
 
Introduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxIntroduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxMatsuo Lab
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IES VE
 
AI Fame Rush Review – Virtual Influencer Creation In Just Minutes
AI Fame Rush Review – Virtual Influencer Creation In Just MinutesAI Fame Rush Review – Virtual Influencer Creation In Just Minutes
AI Fame Rush Review – Virtual Influencer Creation In Just MinutesMd Hossain Ali
 
Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024SkyPlanner
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6DianaGray10
 

Dernier (20)

UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
 
Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™
 
Computer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsComputer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and Hazards
 
UiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation DevelopersUiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation Developers
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystem
 
Building AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxBuilding AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptx
 
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfMachine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdf
 
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
 
Comparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioComparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and Istio
 
Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024
 
20230202 - Introduction to tis-py
20230202 - Introduction to tis-py20230202 - Introduction to tis-py
20230202 - Introduction to tis-py
 
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAAnypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
 
Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )
 
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDEADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
 
20230104 - machine vision
20230104 - machine vision20230104 - machine vision
20230104 - machine vision
 
Introduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxIntroduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptx
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
 
AI Fame Rush Review – Virtual Influencer Creation In Just Minutes
AI Fame Rush Review – Virtual Influencer Creation In Just MinutesAI Fame Rush Review – Virtual Influencer Creation In Just Minutes
AI Fame Rush Review – Virtual Influencer Creation In Just Minutes
 
Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6
 

The Big Picture: Real-time Data is Defining Intelligent Offers

  • 1. 1© Cloudera, Inc. All rights reserved. The Big Picture: Real-time Data is Defining Intelligent Offers Sean Anderson, Sr. Solutions Marketing Manager Ryan Lippert, Sr. Product Marketing Manager
  • 2. 2© Cloudera, Inc. All rights reserved. We empower people to transform complex data into clear and actionable insights DRIVE CUSTOMER INSIGHTS CONNECT PRODUCTS & SERVICES (IoT) PROTECT BUSINESS
  • 3. 3© Cloudera, Inc. All rights reserved. DRIVE CUSTOMER INSIGHTS CONNECT PRODUCTS & SERVICES (IoT) PROTECT BUSINESS Delivering greater value through improved customer understanding Powering predictive analytics to increase performance and reduce fleet downtime Creating new revenue streams with an advanced anti-fraud solution Cloudera powering data-driven customers
  • 4. 4© Cloudera, Inc. All rights reserved. Cloudera Use Cases Omni-Channel optimization Customer analysis Sentiment analysis Churn analysis Market spend analysis Next best offer Smart promotions Basket analysis Network threat detection User/entity behavioral analysis Logger Merchant fraud Connect products & services Predictive maintenance Remote monitoring Supply chain optimization Inventory optimization Operations optimization Spend analytics service Drive customer insights Protect business
  • 5. 5© Cloudera, Inc. All rights reserved. Powering a Variety of Use Cases… Targeted Marketing Smart Promotions Recommendation Engines Omni-Channel Optimization
  • 6. 6© Cloudera, Inc. All rights reserved. ✓ Breaking down data silos ✓ Sharing data in accordance with privacy and regulatory policies ✓ Becoming iterative, lean and leveraging knowledge across the business Creating True Customer 360
  • 7. 7© Cloudera, Inc. All rights reserved. Customer 360 Journey • Marketing Systems (Salesforce, Omniture, CRM) • Clickstream Data Primary Data Source • Clickstream • NPS Systems • Support Call Logs • Social Feeds Primary Data Source Understand Your Customer Learn Behaviors Improve Interactions • Shopping Cart Platforms • Geolocation Primary Data Source
  • 8. 8© Cloudera, Inc. All rights reserved. The Data Journey Collate the Data Sources Micro-Segmentation Drive Personalized Campaigns Devise Micro- segments based on combining multiple factors: • Age • Location • Spending History • Channel Preferences • Content Preferences • Apps Usage • Social Influence • Churn Score • Lifetime Value • Usage Patterns • Data Usage Drive Personalized Campaigns for specific micro-segments Retention campaign for high value customers with iPhone who recently shared a negative social sentiment Upsell campaign for high-data users with family to move over to a family bundle Geo-Location based targeted advertising for specific customer micro-segments
  • 9. 9© Cloudera, Inc. All rights reserved. How to Iteratively Build a True Customer 360? Customer Data Source Start with ingesting the “best” version of your customer profile Find your common identifiers across datasets: customer name, number, IMEI, IMSI IMEI ChannelsPurchase History Add New Data Source Common Identifier Current Source Enrich with additional demographic information (purchase history or channels) from other systems / sources Deliver A Use Case Deliver a specific use case based on the profile with new data sets: • Customer Lifetime value • Next Best offer • Omni Channel Enrich Your Profile • Enrich your customer profiles with purchase behavior • Continue to enhance with each new use case Location Clickstream Continue to add new data sources iteratively to enhance your customer profile with new use cases Call center Social Media Apps External Data New Data Sources
  • 10. 10© Cloudera, Inc. All rights reserved. Three Scenarios
  • 11. 11© Cloudera, Inc. All rights reserved. Three Scenarios – Event Modelling in Real Time Events trigger changes in purchasing preferences among customers and potential customers. However, most NBO frameworks are based on historic data models. Historic models handle a baseline of behavior/information well, but struggle to optimize in the moment for events. By incorporating the real-time behavior of users back into the model on a rolling basis, companies can capture the opportunity these events present.
  • 12. 12© Cloudera, Inc. All rights reserved. 56% of all customer interactions happen during a multi-channel, multi-event journey. Companies that put data at the center of their marketing and sales decisions improve their marketing returns by 15-20% adding up to 150 to 20 billion in additional revenue. Research states that personalized emails improve click through rates by 14% and conversation rates by 10% Over 96% of organizations believe that email personalization can improve email marketing performance. Three Scenarios - Email Personalization
  • 13. 13© Cloudera, Inc. All rights reserved. HEB is the largest grocery chain in the state of Texas. When employees couldn't get to work, some stores still operated with as few as five people Hurricane impact projections are often not accurate which means HEB had to plan for the worst and leverage real-time data to make shipping and staffing decisions. Certain items become in high demand during a hurricane, while other experience almost no- demand (Frozen Foods, Flowers) HEB leveraged real-time data to plan special shipments which arrived before state and federal aide. Three Scenarios - Hurricane Harvey
  • 14. 14© Cloudera, Inc. All rights reserved. The Platform
  • 15. 15© Cloudera, Inc. All rights reserved. Cloudera Enterprise – The Platform for Customer 360 Location Social Clickstream BI Tools Online & Mobile Apps Billing/ Ordering CRM/ Profile Marketing Campaigns Search EDW N/W Logs Call Center Apps Network Other Structured Sources Internal Systems External Sources BI Solutions Real-Time Apps Search Data Science Workbench SQL Machine Learning Systems Data
  • 16. 16© Cloudera, Inc. All rights reserved. Key Enabling Capabilities Ideal for real-time analytics on IoT and time series data. Simplifies Lambda architectures for running real-time analytics on streaming data Leading analytic SQL engine running natively in Hadoop. Impala provides the fastest insights, at high-concurrency, with the familiar access necessary for powering BI and analytics across the business. Kudu: Real-Time Offers Impala: Self Service BI Data Science Workbench Collaborative hub for enterprise data science and an integrated development environment for running Python, R, & Scala with support for Spark
  • 17. 17© Cloudera, Inc. All rights reserved. • Serve real-time data at scale for real-time decision making • Aggregate relational, NoSQL, structured & unstructured data • Stream processing & analytics on changing operational data • Leverage linear performance scalability and predictable TCO • Deliver a secure, low-latency, high-concurrency experience Extract real-time insights from big data OPERATIONAL DATABASE
  • 18. 18© Cloudera, Inc. All rights reserved. The Underlying Driver What drives a use case to real-time? High Frequency Trading APT Detection Fraud Detection Predictive Maintenance Next Best Offer Inventory Management Shipping/Logistic Systems CRM Systems Employee Management Strategic Planning Real-time data management use cases are defined by a common set of characteristics. • Narrow time window in which to make a decision (automated or manual) • Opportunity for data points to change the decision, and thus the business’s path • Decreasing value of data over time Not all use cases have a pressing need for real-time data. • Broader strategic decisions, for example, do not require real-time data input • Over time, decreases in HW costs and increases in availability of real-time systems will lead most use cases to be conducted in real-time Real Time Some Latency Acceptable
  • 19. 19© Cloudera, Inc. All rights reserved. Managing Data from Customer Touchpoints Handle real-time data ingest from diverse sources Fundamentall y Secure Data Streams Deployment Flexibility Machine Learning Capabilities Diverse Analytical OptionsCombine data from various sources Customer Data Mgmt. Hub Scale easily & Cost effectively Batch or Real- time Data Streams A comprehensive data management platform to drive business insights from data Data Sources Data Storage & Processing Serving, Analytics & Machine Learning Data Ingest Data Sources Security, Scalability & Easy Management
  • 20. 20© Cloudera, Inc. All rights reserved. The Right Storage Technology to Meet Your Use Case Real-Time Inputs Real-Time Analytics Input data is pushed into a semi-static model of a well- defined process, resulting in the selection of an optimal strategy given the known variables. Input data itself becomes part of the model, continuously evolving (within boundaries) as behaviors change and new connections are identified.
  • 21. 21© Cloudera, Inc. All rights reserved. Operational DB: Real-Time Architecture Driving the Model Through Machine Learning Kafka Spark Streaming Kudu Spark MLlib Input Data Addt’l Sources Individual Session Full Model/Learning Genesis Spark 1 Event Occurs 2 Messaging 3 Stream Processing 4 Land in Relational Store 5 Apply ML Libraries
  • 22. 22© Cloudera, Inc. All rights reserved. Operational DB: Real-Time Architecture MLlib & K-Means: Defining Microsegments via Machine Learning Height Weight Height Weight 1 2 Height Weight 3 Height Weight 4 L M S XL L M S XS Near Custom ?
  • 23. 23© Cloudera, Inc. All rights reserved. Operational DB: Real-Time Architecture Driving Prediction and Optimization Kafka Spark Streaming Kudu Spark MLlib Input Data Addt’l Sources Individual Session 1 Data Processed Genesis Spark 2 Request Processed/ Kudu Queried 3 4 Results Returned Results Processed 5 Processed Data Returned Full Model/Learning
  • 24. 24© Cloudera, Inc. All rights reserved. Operational DB: Real-Time Architecture Driving Prediction and Optimization Step 1: Data Processed Apache Spark processes the data from the event (car sensors, manufacturing, wearables, etc), which potentially involves keeping a running list of the last X number of events Step 2: Request Processed/Kudu Queried A Spark application uses the data gathered in step one to query Kudu’s database in a predefined manner to look for similar patterns defined via machine learning Step 3: Kudu Results Returned Kudu returns the results from the query in step 2 back to Spark to determine what needs to be returned to the application Step 4: Results Processed Spark associates the results from Kudu with the information stored from the current event to determine the next step to feed back to the application Step 5: Processed Data Returned The machine-generated, best possible outcome is prescribed and served to the application
  • 25. 25© Cloudera, Inc. All rights reserved. Operational DB: NBO Use Case Prediction and Optimization Kafka Spark Streaming Kudu Spark MLlib Application Addt’l Sources Individual Session User Shopping Spark Full Model/Learning Data Request Sent For Stream Processing Data Cleaned/Ordered/Processed, Then Delivered to Kudu for Modelling Automated processes based on machine learning enable prediction and optimization at a new level. Illustrative, models will likely have >2 dimensions
  • 26. 26© Cloudera, Inc. All rights reserved. Visit: Solutions Gallery
  • 27. 27© Cloudera, Inc. All rights reserved. Thank you Sean Anderson, Sr. Solutions Marketing Manager Ryan Lippert, Sr. Product Marketing Manager