SlideShare ist ein Scribd-Unternehmen logo
1 von 19
Downloaden Sie, um offline zu lesen
© 2014 IBM Corporation
Big Data & Analytics – beyond Hadoop
Ian Radmore, IBM UKI Big Data Specialist
June 18th, 2014
© 2014 IBM Corporation
Data: To have and to hold? Or to Analyse and Act!
Data in
Data at
2
© 2014 IBM Corporation
The auto industry is already the 2nd largest data generator AND 20% CAGR!
Ford Fusion: 145 actuators, 4700 relays
and 70 sensors, including radar, sonar,
accelerometer, camera, rain sensors.
Collectively, these devices generate
more than 25 gigabytes of data per
hour, which is processed by more than
70 on-board computers.
1 car year = 1TB
3
© 2014 IBM Corporation
A Big Data & Analytics approach helps provide a foundation for a Smarter Enterprise
Invest in aInvest in a
big data & analyticsbig data & analytics
platformplatform
Be proactive aboutBe proactive about
privacy, security andprivacy, security and
governancegovernance
Imagine It. Realise It. Trust It.
Build a culture thatBuild a culture that
infuses analyticsinfuses analytics
everywhereeverywhere
Confidence in Your
Data
Confidence in
Accelerating Value
Confidence in Your
Skills
4
© 2014 IBM Corporation
Deployed real-time CDR analysis solution to handle exploding data
volume growth and performance requirements
Analyzes call, internet usage, and text records in real-time to identify
and address poorly performing cells
Uses InfoSphere Streams and IBM Netezza
Significant Benefits:
Over 90% reduction in time to merge/load call record data
Over 90% reduction in storage
Increased network quality, improved customer satisfaction,
reduced churn
Sprint Increases Revenue & Improves
Customer Satisfaction
“Over 90+% reduction in
merge/load times and
storage requirements”
“Over 90+% reduction in
merge/load times and
storage requirements”
Capabilities Utilised:
• Stream processing
• Data Warehouse Analytics Appliance
5
© 2014 IBM Corporation
• Examines trends, volume, and content of millions of public Twitter
messages in real-time
• Analytic accelerators to understand sentiment (positive, negative,
neutral)
• Capabilities
• Stream Computing
• Visualization
• Benefits
• Real-time display of public sentiment as candidates respond
to questions
• Debate winner prediction based on public opinion instead of
solely political analysts
University of Southern California
Innovation Lab Monitors Political
Debates
Solution to measure public sentiment during key
primary & general presidential debates
6
© 2014 IBM Corporation
7
KTH Swedish Royal Institute of
Technology Reducing Traffic
Congestion
• Deployed real-time Smarter Traffic system to predict and
improve traffic flow.
• Analyzes streaming real-time data gathered from
cameras at entry/exit to city, GPS data from taxis and
trucks, and weather information.
• Predicts best time and method to travel such as when to
leave to catch a flight at the airport
Significant benefits:
• Enables ability to analyze and predict traffic faster and
more accurately than ever before
• Provides new insight into mechanisms that affect a
complex traffic system
• Smarter, more efficient, and more environmentally
friendly traffic
7
Capabilities Utilised:
Stream Computing
7
© 2014 IBM Corporation
Pacific Northwest Smart Grid
Demonstration Project
Capabilities:
Stream Computing – real-time control
system
Data Warehouse Appliance – analyze
massive data sets
Demonstrates scalability from 100 to
500K homes while retaining 10 years’
historical data
60k metered customers in 5 states
Accommodates ad hoc analysis of price
fluctuation, energy consumption profiles,
risk, fraud detection, grid health, etc.
8
© 2014 IBM Corporation
Information Integration & Governance
Systems Security
On premise, Cloud, As a service
Storage
IBM Watson Foundations
IBM Big Data & Analytics Infrastructure
New /
Enhanced
ApplicationsAll Data
What action
should I take?
Decision
management
Cognitive
What did
I learn?
Landing,
Exploration and
Archive data
zone
EDW and
data mart
zone
Operational
data zone
Real-time Data Processing & Analytics What is
happening?
Discovery and
exploration
Why did it
happen?
Reporting,
content and
analysisWhat could
happen?
Predictive
analytics and
modelling
Deep
Analytics
data zone
8
Realise It. Invest
© 2014 IBM Corporation
Realise It. In-Store Presence Zones
Intelligent location-based technology to gain deep insight
into customer in-store behaviour
Enable retailers to integrate the physical and digital experience to facilitate an ongoing
dialogue that creates loyalty via an exceptional in-store shopping experience
Presence Zones
Sensors
9
© 2014 IBM Corporation
IBM Internal Use Only
Realise It. The Customer Insight Appliance
10
© 2014 IBM Corporation
Realise It. A Multichannel Korean retailer
Reliable insight
provides decision support for senior
management
Targeted campaigns
can be developed for marketing
Precise measurement
of cross-channel campaigns
Business Challenge: As sales increased for this retailer’s online shopping mall,
management experienced increasing difficulty ensuring that an appropriate product mix
was being presented to its customers.
The Solution: The company adopted sophisticated analytics and marketing automation
to understand, predict and act on consumer buying behavior with confidence. Real-time
marketing automation delivers personalised content to each shopper, triggered by their
interaction history. Delivered at the right place and time, these offers can move the
shopper toward a sale and even increase the size of the purchase.
“We have greatly improved our understanding of our customers, which is helping us to
make smarter decisions that significantly improve business performance.”
—Spokesperson, multichannel Korean retailer
Combining marketing
automation with analytics
to personalise
communications and
optimise offerings
11
© 2014 IBM Corporation
Millions of
events per
second
Microsecond
Latency
Traditional / Non-traditional
data sources
Real time delivery
Powerful
Analytics
Algorithmic
Trading
Telco Churn
Prediction
Smart
GridCyber
Security Government /
Law enforcement
ICU
Monitoring
Environment
MonitoringValue
Clear business goals
Business change driven outcomes
Volume
Terabytes/second
Petabytes/day
Variety
All kinds of data
All kinds of analytics
Velocity
Decisions in microseconds
Massively scalable
Veracity
Screening, validation & certification of data
Example Streaming Data Sources:
Video, Audio, Networks, Social Media, Sensor, Weather
Realise It. IBM InfoSphere Streams:
Real-Time Adaptive Analytics for Big Data In-Motion
Connected
Car
11
3
© 2014 IBM Corporation
Create foundation
of trusted data
Understand usage and
monitor compliance
Model exposure and
understand variability
Trust the factsTrust the facts Ensure privacyEnsure privacy
and securityand security
Make riskMake risk
aware decisionsaware decisions
Trust It. Be proactive about privacy, security and governance.
14
© 2014 IBM Corporation
Big Data Uses Cases Delivered with Unique IBM Capabilities
Unique IBM Capabilities:
1. In-memory computing with BLU
Acceleration
2. Data privacy and security of big
data
3. Data Discovery and Exploration
4. Building Confidence in Big Data
with Information Governance
5. Stream computing
WATSON FOUNDATIONS
Decision
Management
Planning &
Forecasting
Discovery &
Exploration
Business Intelligence & Predictive Analytics
Content
Analytics
Information Integration & Governance
Data Mgmt &
Warehouse
Hadoop
System
Stream
Computing
Content
Management
WATSON FOUNDATIONS
Decision
Management
Planning &
Forecasting
Discovery &
Exploration
Business Intelligence & Predictive AnalyticsBusiness Intelligence & Predictive Analytics
Content
Analytics
Information Integration & Governance
Data Mgmt &
Warehouse
Hadoop
System
Stream
Computing
Content
Management
Real-time traffic flow
optimisation
Low-latency
network analysis
Fraud & risk
detection
Predictive asset
maintenance
Understand and act on
customer sentiment
Predict and act on
intent to purchase
15
© 2014 IBM Corporation
16
© 2014 IBM Corporation
http://www.youtube.com/watch?v=FGp-h-x0Hss
17
© 2014 IBM Corporation
Building a real-time enterprise is a journey, which depends on a solid Big Data & Analytics
foundation for success
Be proactive
about privacy,
security and
governance
Build a culture
that infuses
analytics
everywhere
Invest in a
big data &
analytics
platform
Imagine It. Realise It. Trust It.
18
© 2014 IBM Corporation
Ian Radmore
IBM Big Data Specialist,
UK & Ireland
IBM United Kingdom Limited
City Gate West
Toll House Hill
Nottingham
NG1 5FN
Mobile +44 7843 368078
Ian.radmore@uk.ibm.com
19

Weitere ähnliche Inhalte

Was ist angesagt?

Infrastructure 2.0 - Network World
Infrastructure 2.0 - Network WorldInfrastructure 2.0 - Network World
Infrastructure 2.0 - Network WorldGlenn Allison
 
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
 
Big data and analytics ibm digital game plan short v2 nonconf
Big data and analytics ibm digital game plan short v2 nonconfBig data and analytics ibm digital game plan short v2 nonconf
Big data and analytics ibm digital game plan short v2 nonconfFriedel Jonker
 
Inside the mind of Generation D: What it means to be data-rich and analytica...
Inside the mind of Generation D:  What it means to be data-rich and analytica...Inside the mind of Generation D:  What it means to be data-rich and analytica...
Inside the mind of Generation D: What it means to be data-rich and analytica...Derek Franks
 
WSO2Con - Integrating Telecom Big Data: Challenges and Lessons Learned
WSO2Con - Integrating Telecom Big Data: Challenges and Lessons LearnedWSO2Con - Integrating Telecom Big Data: Challenges and Lessons Learned
WSO2Con - Integrating Telecom Big Data: Challenges and Lessons LearnedFabíola Fernandes
 
Customer Experience: A Catalyst for Digital Transformation
Customer Experience: A Catalyst for Digital TransformationCustomer Experience: A Catalyst for Digital Transformation
Customer Experience: A Catalyst for Digital TransformationCloudera, Inc.
 
Conference Presenation Predictive Analytics ITC-AP 2013 , Prof Lili Saghafi
Conference Presenation Predictive Analytics ITC-AP 2013 , Prof Lili Saghafi Conference Presenation Predictive Analytics ITC-AP 2013 , Prof Lili Saghafi
Conference Presenation Predictive Analytics ITC-AP 2013 , Prof Lili Saghafi Professor Lili Saghafi
 
Teaching organizations to fish in a data-rich future: Stories from data leaders
Teaching organizations to fish in a data-rich future: Stories from data leadersTeaching organizations to fish in a data-rich future: Stories from data leaders
Teaching organizations to fish in a data-rich future: Stories from data leadersAmanda Sirianni
 
Data-driven marketing - expert panel
Data-driven marketing - expert panelData-driven marketing - expert panel
Data-driven marketing - expert panelCloudera, Inc.
 
It’s Not Enough to Just Collect Data
It’s Not Enough to Just Collect DataIt’s Not Enough to Just Collect Data
It’s Not Enough to Just Collect DataTeradata
 
WCIT 2014 Rohit Tandon - Big Data to Drive Business Results: HP HAVEn
WCIT 2014 Rohit Tandon - Big Data to Drive Business Results: HP HAVEnWCIT 2014 Rohit Tandon - Big Data to Drive Business Results: HP HAVEn
WCIT 2014 Rohit Tandon - Big Data to Drive Business Results: HP HAVEnWCIT 2014
 
ACFE Presentation on Analytics for Fraud Detection and Mitigation
ACFE Presentation on Analytics for Fraud Detection and MitigationACFE Presentation on Analytics for Fraud Detection and Mitigation
ACFE Presentation on Analytics for Fraud Detection and MitigationScott Mongeau
 
Importance of Big Data in the Telecom Industry
Importance of Big Data in the Telecom IndustryImportance of Big Data in the Telecom Industry
Importance of Big Data in the Telecom IndustryMahindra Comviva
 
Insurance Industry Trends in 2015: #2 Mobility
Insurance Industry Trends in 2015: #2 Mobility Insurance Industry Trends in 2015: #2 Mobility
Insurance Industry Trends in 2015: #2 Mobility Euro IT Group
 
Executing Complex Strategies through a Field Sales Force
Executing Complex Strategies through a Field Sales ForceExecuting Complex Strategies through a Field Sales Force
Executing Complex Strategies through a Field Sales Forceaktana
 
How to optimize the supply chain with ai
How to optimize the supply chain with ai How to optimize the supply chain with ai
How to optimize the supply chain with ai GlobalTechCouncil
 
Business Intellenge (BI)
Business Intellenge (BI)Business Intellenge (BI)
Business Intellenge (BI)Vivek Kumar
 

Was ist angesagt? (19)

Infrastructure 2.0 - Network World
Infrastructure 2.0 - Network WorldInfrastructure 2.0 - Network World
Infrastructure 2.0 - Network World
 
AI at the Edge
AI at the EdgeAI at the Edge
AI at the Edge
 
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
 
Big data and analytics ibm digital game plan short v2 nonconf
Big data and analytics ibm digital game plan short v2 nonconfBig data and analytics ibm digital game plan short v2 nonconf
Big data and analytics ibm digital game plan short v2 nonconf
 
01 big dataoverview
01 big dataoverview01 big dataoverview
01 big dataoverview
 
Inside the mind of Generation D: What it means to be data-rich and analytica...
Inside the mind of Generation D:  What it means to be data-rich and analytica...Inside the mind of Generation D:  What it means to be data-rich and analytica...
Inside the mind of Generation D: What it means to be data-rich and analytica...
 
WSO2Con - Integrating Telecom Big Data: Challenges and Lessons Learned
WSO2Con - Integrating Telecom Big Data: Challenges and Lessons LearnedWSO2Con - Integrating Telecom Big Data: Challenges and Lessons Learned
WSO2Con - Integrating Telecom Big Data: Challenges and Lessons Learned
 
Customer Experience: A Catalyst for Digital Transformation
Customer Experience: A Catalyst for Digital TransformationCustomer Experience: A Catalyst for Digital Transformation
Customer Experience: A Catalyst for Digital Transformation
 
Conference Presenation Predictive Analytics ITC-AP 2013 , Prof Lili Saghafi
Conference Presenation Predictive Analytics ITC-AP 2013 , Prof Lili Saghafi Conference Presenation Predictive Analytics ITC-AP 2013 , Prof Lili Saghafi
Conference Presenation Predictive Analytics ITC-AP 2013 , Prof Lili Saghafi
 
Teaching organizations to fish in a data-rich future: Stories from data leaders
Teaching organizations to fish in a data-rich future: Stories from data leadersTeaching organizations to fish in a data-rich future: Stories from data leaders
Teaching organizations to fish in a data-rich future: Stories from data leaders
 
Data-driven marketing - expert panel
Data-driven marketing - expert panelData-driven marketing - expert panel
Data-driven marketing - expert panel
 
It’s Not Enough to Just Collect Data
It’s Not Enough to Just Collect DataIt’s Not Enough to Just Collect Data
It’s Not Enough to Just Collect Data
 
WCIT 2014 Rohit Tandon - Big Data to Drive Business Results: HP HAVEn
WCIT 2014 Rohit Tandon - Big Data to Drive Business Results: HP HAVEnWCIT 2014 Rohit Tandon - Big Data to Drive Business Results: HP HAVEn
WCIT 2014 Rohit Tandon - Big Data to Drive Business Results: HP HAVEn
 
ACFE Presentation on Analytics for Fraud Detection and Mitigation
ACFE Presentation on Analytics for Fraud Detection and MitigationACFE Presentation on Analytics for Fraud Detection and Mitigation
ACFE Presentation on Analytics for Fraud Detection and Mitigation
 
Importance of Big Data in the Telecom Industry
Importance of Big Data in the Telecom IndustryImportance of Big Data in the Telecom Industry
Importance of Big Data in the Telecom Industry
 
Insurance Industry Trends in 2015: #2 Mobility
Insurance Industry Trends in 2015: #2 Mobility Insurance Industry Trends in 2015: #2 Mobility
Insurance Industry Trends in 2015: #2 Mobility
 
Executing Complex Strategies through a Field Sales Force
Executing Complex Strategies through a Field Sales ForceExecuting Complex Strategies through a Field Sales Force
Executing Complex Strategies through a Field Sales Force
 
How to optimize the supply chain with ai
How to optimize the supply chain with ai How to optimize the supply chain with ai
How to optimize the supply chain with ai
 
Business Intellenge (BI)
Business Intellenge (BI)Business Intellenge (BI)
Business Intellenge (BI)
 

Andere mochten auch

Hadoop bigdata overview
Hadoop bigdata overviewHadoop bigdata overview
Hadoop bigdata overviewharithakannan
 
Big Data Hadoop (Overview)
Big Data Hadoop (Overview)Big Data Hadoop (Overview)
Big Data Hadoop (Overview)Rohit Srivastava
 
Introduction to Bigdata and HADOOP
Introduction to Bigdata and HADOOP Introduction to Bigdata and HADOOP
Introduction to Bigdata and HADOOP vinoth kumar
 
Big data and hadoop overvew
Big data and hadoop overvewBig data and hadoop overvew
Big data and hadoop overvewKunal Khanna
 
Hadoop Basics - Apache hadoop Bigdata training by Design Pathshala
Hadoop Basics - Apache hadoop Bigdata training by Design Pathshala Hadoop Basics - Apache hadoop Bigdata training by Design Pathshala
Hadoop Basics - Apache hadoop Bigdata training by Design Pathshala Desing Pathshala
 
Keynote talk at Financial Times Forum - BigData and Advanced Analytics at SIB...
Keynote talk at Financial Times Forum - BigData and Advanced Analytics at SIB...Keynote talk at Financial Times Forum - BigData and Advanced Analytics at SIB...
Keynote talk at Financial Times Forum - BigData and Advanced Analytics at SIB...Usama Fayyad
 
Introduction and Overview of BigData, Hadoop, Distributed Computing - BigData...
Introduction and Overview of BigData, Hadoop, Distributed Computing - BigData...Introduction and Overview of BigData, Hadoop, Distributed Computing - BigData...
Introduction and Overview of BigData, Hadoop, Distributed Computing - BigData...Mahantesh Angadi
 
Hadoop and BigData - July 2016
Hadoop and BigData - July 2016Hadoop and BigData - July 2016
Hadoop and BigData - July 2016Ranjith Sekar
 
BigData - Hadoop -by 侯圣文@secooler
BigData - Hadoop -by 侯圣文@secooler BigData - Hadoop -by 侯圣文@secooler
BigData - Hadoop -by 侯圣文@secooler Shengwen HOU(侯圣文)
 
Big Data & Hadoop Tutorial
Big Data & Hadoop TutorialBig Data & Hadoop Tutorial
Big Data & Hadoop TutorialEdureka!
 
Seminar Presentation Hadoop
Seminar Presentation HadoopSeminar Presentation Hadoop
Seminar Presentation HadoopVarun Narang
 
Hadoop introduction , Why and What is Hadoop ?
Hadoop introduction , Why and What is  Hadoop ?Hadoop introduction , Why and What is  Hadoop ?
Hadoop introduction , Why and What is Hadoop ?sudhakara st
 
Big Data Analytics with Hadoop
Big Data Analytics with HadoopBig Data Analytics with Hadoop
Big Data Analytics with HadoopPhilippe Julio
 

Andere mochten auch (17)

Anju
AnjuAnju
Anju
 
Hadoop bigdata overview
Hadoop bigdata overviewHadoop bigdata overview
Hadoop bigdata overview
 
Big Data Hadoop (Overview)
Big Data Hadoop (Overview)Big Data Hadoop (Overview)
Big Data Hadoop (Overview)
 
Introduction to Bigdata and HADOOP
Introduction to Bigdata and HADOOP Introduction to Bigdata and HADOOP
Introduction to Bigdata and HADOOP
 
Big data and hadoop overvew
Big data and hadoop overvewBig data and hadoop overvew
Big data and hadoop overvew
 
Hadoop Basics - Apache hadoop Bigdata training by Design Pathshala
Hadoop Basics - Apache hadoop Bigdata training by Design Pathshala Hadoop Basics - Apache hadoop Bigdata training by Design Pathshala
Hadoop Basics - Apache hadoop Bigdata training by Design Pathshala
 
Keynote talk at Financial Times Forum - BigData and Advanced Analytics at SIB...
Keynote talk at Financial Times Forum - BigData and Advanced Analytics at SIB...Keynote talk at Financial Times Forum - BigData and Advanced Analytics at SIB...
Keynote talk at Financial Times Forum - BigData and Advanced Analytics at SIB...
 
Introduction and Overview of BigData, Hadoop, Distributed Computing - BigData...
Introduction and Overview of BigData, Hadoop, Distributed Computing - BigData...Introduction and Overview of BigData, Hadoop, Distributed Computing - BigData...
Introduction and Overview of BigData, Hadoop, Distributed Computing - BigData...
 
Hadoop and BigData - July 2016
Hadoop and BigData - July 2016Hadoop and BigData - July 2016
Hadoop and BigData - July 2016
 
BigData - Hadoop -by 侯圣文@secooler
BigData - Hadoop -by 侯圣文@secooler BigData - Hadoop -by 侯圣文@secooler
BigData - Hadoop -by 侯圣文@secooler
 
Big Data & Hadoop Tutorial
Big Data & Hadoop TutorialBig Data & Hadoop Tutorial
Big Data & Hadoop Tutorial
 
Big data and Hadoop
Big data and HadoopBig data and Hadoop
Big data and Hadoop
 
Seminar Presentation Hadoop
Seminar Presentation HadoopSeminar Presentation Hadoop
Seminar Presentation Hadoop
 
Hadoop introduction , Why and What is Hadoop ?
Hadoop introduction , Why and What is  Hadoop ?Hadoop introduction , Why and What is  Hadoop ?
Hadoop introduction , Why and What is Hadoop ?
 
What is Big Data?
What is Big Data?What is Big Data?
What is Big Data?
 
Big Data Analytics with Hadoop
Big Data Analytics with HadoopBig Data Analytics with Hadoop
Big Data Analytics with Hadoop
 
Big data ppt
Big  data pptBig  data ppt
Big data ppt
 

Ähnlich wie IBM's big data seminar programme -moving beyond Hadoop - Ian Radmore, IBM

Robert Lecklin - BigData is making a difference
Robert Lecklin - BigData is making a differenceRobert Lecklin - BigData is making a difference
Robert Lecklin - BigData is making a differenceIBM Sverige
 
Real-Time Analytics for Industries
Real-Time Analytics for IndustriesReal-Time Analytics for Industries
Real-Time Analytics for IndustriesAvadhoot Patwardhan
 
Why You Need to Govern Big Data
Why You Need to Govern Big DataWhy You Need to Govern Big Data
Why You Need to Govern Big DataIBM Analytics
 
Ibm big data-platform
Ibm big data-platformIbm big data-platform
Ibm big data-platformIBM Sverige
 
Big datacamp june14_alex_liu
Big datacamp june14_alex_liuBig datacamp june14_alex_liu
Big datacamp june14_alex_liuData Con LA
 
InfoSphere Streams toolkits :Real-Time Analytics on Data in Motion
InfoSphere Streams toolkits :Real-Time Analytics on Data in MotionInfoSphere Streams toolkits :Real-Time Analytics on Data in Motion
InfoSphere Streams toolkits :Real-Time Analytics on Data in MotionAvadhoot Patwardhan
 
IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014
IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014
IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014Findwise
 
Make from your it department a competitive differentiator for your business
Make from your it department a competitive differentiator for your businessMake from your it department a competitive differentiator for your business
Make from your it department a competitive differentiator for your businessMarcos Quezada
 
InterConnect 2013 Big Data & Analytics Keynote: Mychelle Mollot
InterConnect 2013 Big Data & Analytics Keynote: Mychelle MollotInterConnect 2013 Big Data & Analytics Keynote: Mychelle Mollot
InterConnect 2013 Big Data & Analytics Keynote: Mychelle MollotIBM Events
 
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
 
Why Infrastructure Matters for Big Data & Analytics
Why Infrastructure Matters for Big Data & AnalyticsWhy Infrastructure Matters for Big Data & Analytics
Why Infrastructure Matters for Big Data & AnalyticsRick Perret
 
Take the Big Data Challenge - Take Advantage of ALL of Your Data 16 Sept 2014
Take the Big Data Challenge - Take Advantage of ALL of Your Data 16 Sept 2014Take the Big Data Challenge - Take Advantage of ALL of Your Data 16 Sept 2014
Take the Big Data Challenge - Take Advantage of ALL of Your Data 16 Sept 2014pietvz
 
Getting started with Hadoop on the Cloud with Bluemix
Getting started with Hadoop on the Cloud with BluemixGetting started with Hadoop on the Cloud with Bluemix
Getting started with Hadoop on the Cloud with BluemixNicolas Morales
 
Come fare business con i big data in concreto
Come fare business con i big data in concretoCome fare business con i big data in concreto
Come fare business con i big data in concretoHP Enterprise Italia
 
Welcome to 2015's Digital Enterprise IT Infrastructure
Welcome to 2015's Digital Enterprise IT Infrastructure   Welcome to 2015's Digital Enterprise IT Infrastructure
Welcome to 2015's Digital Enterprise IT Infrastructure John Sing
 
The Big Picture: Real-time Data is Defining Intelligent Offers
The Big Picture: Real-time Data is Defining Intelligent OffersThe Big Picture: Real-time Data is Defining Intelligent Offers
The Big Picture: Real-time Data is Defining Intelligent OffersCloudera, Inc.
 
BLU Acceleration on the Cloud – 101
BLU Acceleration on the Cloud – 101BLU Acceleration on the Cloud – 101
BLU Acceleration on the Cloud – 101IBM Analytics
 
The nexus of Social, Mobile, Cloud and Big Data Analytics
The nexus of Social, Mobile, Cloud and Big Data AnalyticsThe nexus of Social, Mobile, Cloud and Big Data Analytics
The nexus of Social, Mobile, Cloud and Big Data AnalyticsE-Government Center Moldova
 

Ähnlich wie IBM's big data seminar programme -moving beyond Hadoop - Ian Radmore, IBM (20)

Robert Lecklin - BigData is making a difference
Robert Lecklin - BigData is making a differenceRobert Lecklin - BigData is making a difference
Robert Lecklin - BigData is making a difference
 
Real-Time Analytics for Industries
Real-Time Analytics for IndustriesReal-Time Analytics for Industries
Real-Time Analytics for Industries
 
Why You Need to Govern Big Data
Why You Need to Govern Big DataWhy You Need to Govern Big Data
Why You Need to Govern Big Data
 
Ibm big data-platform
Ibm big data-platformIbm big data-platform
Ibm big data-platform
 
Big datacamp june14_alex_liu
Big datacamp june14_alex_liuBig datacamp june14_alex_liu
Big datacamp june14_alex_liu
 
InfoSphere Streams toolkits :Real-Time Analytics on Data in Motion
InfoSphere Streams toolkits :Real-Time Analytics on Data in MotionInfoSphere Streams toolkits :Real-Time Analytics on Data in Motion
InfoSphere Streams toolkits :Real-Time Analytics on Data in Motion
 
IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014
IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014
IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014
 
Make from your it department a competitive differentiator for your business
Make from your it department a competitive differentiator for your businessMake from your it department a competitive differentiator for your business
Make from your it department a competitive differentiator for your business
 
InterConnect 2013 Big Data & Analytics Keynote: Mychelle Mollot
InterConnect 2013 Big Data & Analytics Keynote: Mychelle MollotInterConnect 2013 Big Data & Analytics Keynote: Mychelle Mollot
InterConnect 2013 Big Data & Analytics Keynote: Mychelle Mollot
 
Big Data and Analytics
Big Data and AnalyticsBig Data and Analytics
Big Data and Analytics
 
Big Data and Analytics
Big Data and AnalyticsBig Data and Analytics
Big Data and Analytics
 
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
 
Why Infrastructure Matters for Big Data & Analytics
Why Infrastructure Matters for Big Data & AnalyticsWhy Infrastructure Matters for Big Data & Analytics
Why Infrastructure Matters for Big Data & Analytics
 
Take the Big Data Challenge - Take Advantage of ALL of Your Data 16 Sept 2014
Take the Big Data Challenge - Take Advantage of ALL of Your Data 16 Sept 2014Take the Big Data Challenge - Take Advantage of ALL of Your Data 16 Sept 2014
Take the Big Data Challenge - Take Advantage of ALL of Your Data 16 Sept 2014
 
Getting started with Hadoop on the Cloud with Bluemix
Getting started with Hadoop on the Cloud with BluemixGetting started with Hadoop on the Cloud with Bluemix
Getting started with Hadoop on the Cloud with Bluemix
 
Come fare business con i big data in concreto
Come fare business con i big data in concretoCome fare business con i big data in concreto
Come fare business con i big data in concreto
 
Welcome to 2015's Digital Enterprise IT Infrastructure
Welcome to 2015's Digital Enterprise IT Infrastructure   Welcome to 2015's Digital Enterprise IT Infrastructure
Welcome to 2015's Digital Enterprise IT Infrastructure
 
The Big Picture: Real-time Data is Defining Intelligent Offers
The Big Picture: Real-time Data is Defining Intelligent OffersThe Big Picture: Real-time Data is Defining Intelligent Offers
The Big Picture: Real-time Data is Defining Intelligent Offers
 
BLU Acceleration on the Cloud – 101
BLU Acceleration on the Cloud – 101BLU Acceleration on the Cloud – 101
BLU Acceleration on the Cloud – 101
 
The nexus of Social, Mobile, Cloud and Big Data Analytics
The nexus of Social, Mobile, Cloud and Big Data AnalyticsThe nexus of Social, Mobile, Cloud and Big Data Analytics
The nexus of Social, Mobile, Cloud and Big Data Analytics
 

Mehr von Internet World

IBM's big data seminar programme- the case for big data & analytics - Gareth ...
IBM's big data seminar programme- the case for big data & analytics - Gareth ...IBM's big data seminar programme- the case for big data & analytics - Gareth ...
IBM's big data seminar programme- the case for big data & analytics - Gareth ...Internet World
 
Elastic Search Meetup Special - Yann Cluchey, Cogenta
Elastic Search Meetup Special - Yann Cluchey, Cogenta Elastic Search Meetup Special - Yann Cluchey, Cogenta
Elastic Search Meetup Special - Yann Cluchey, Cogenta Internet World
 
How to raise venture capital & the First Tuesday Award 2014
How to raise venture capital & the First Tuesday Award 2014How to raise venture capital & the First Tuesday Award 2014
How to raise venture capital & the First Tuesday Award 2014Internet World
 
Unreasonable learning - Shane Hill, Skoolbo
Unreasonable learning - Shane Hill, SkoolboUnreasonable learning - Shane Hill, Skoolbo
Unreasonable learning - Shane Hill, SkoolboInternet World
 
London's tech scene's at a critical point - Alex Wood, Tech City News
London's tech scene's at a critical point - Alex Wood, Tech City NewsLondon's tech scene's at a critical point - Alex Wood, Tech City News
London's tech scene's at a critical point - Alex Wood, Tech City NewsInternet World
 
Free:Formers CODE:OFF
Free:Formers CODE:OFF Free:Formers CODE:OFF
Free:Formers CODE:OFF Internet World
 
What the Internet of Things means for the mobile enterprise - Ian Evans, AirW...
What the Internet of Things means for the mobile enterprise - Ian Evans, AirW...What the Internet of Things means for the mobile enterprise - Ian Evans, AirW...
What the Internet of Things means for the mobile enterprise - Ian Evans, AirW...Internet World
 
Have your cake and eat it too: adopting technologies without sacrificing - Pa...
Have your cake and eat it too: adopting technologies without sacrificing - Pa...Have your cake and eat it too: adopting technologies without sacrificing - Pa...
Have your cake and eat it too: adopting technologies without sacrificing - Pa...Internet World
 
Business Networking Hacks in Today’s Connected World - Marian Gazdik, Startup...
Business Networking Hacks in Today’s Connected World - Marian Gazdik, Startup...Business Networking Hacks in Today’s Connected World - Marian Gazdik, Startup...
Business Networking Hacks in Today’s Connected World - Marian Gazdik, Startup...Internet World
 
What IT capacity planning can learn from manufacturing's just-in-time models ...
What IT capacity planning can learn from manufacturing's just-in-time models ...What IT capacity planning can learn from manufacturing's just-in-time models ...
What IT capacity planning can learn from manufacturing's just-in-time models ...Internet World
 
How personal data has changed and what this means for businesses looking forw...
How personal data has changed and what this means for businesses looking forw...How personal data has changed and what this means for businesses looking forw...
How personal data has changed and what this means for businesses looking forw...Internet World
 
The database of you - Andy Caddy, Virgin Active Health Clubs
The database of you - Andy Caddy, Virgin Active Health ClubsThe database of you - Andy Caddy, Virgin Active Health Clubs
The database of you - Andy Caddy, Virgin Active Health ClubsInternet World
 
Using big data to find out what women want - John Lervik, Cxense
Using big data to find out what women want - John Lervik, CxenseUsing big data to find out what women want - John Lervik, Cxense
Using big data to find out what women want - John Lervik, CxenseInternet World
 
Relevance = Revenue - PK Vaish, Copernica
Relevance = Revenue - PK Vaish, CopernicaRelevance = Revenue - PK Vaish, Copernica
Relevance = Revenue - PK Vaish, CopernicaInternet World
 
How to drive e-commerce sales with content marketing - David Bowen, EPiServer
How to drive e-commerce sales with content marketing - David Bowen, EPiServerHow to drive e-commerce sales with content marketing - David Bowen, EPiServer
How to drive e-commerce sales with content marketing - David Bowen, EPiServerInternet World
 
Innovation at Tesco - Angela Maurer, Tesco
Innovation at Tesco - Angela Maurer, TescoInnovation at Tesco - Angela Maurer, Tesco
Innovation at Tesco - Angela Maurer, TescoInternet World
 
Responsive Web Design: Advantages & Best Practice - Darrin Adams, Cantarus
Responsive Web Design: Advantages & Best Practice - Darrin Adams, CantarusResponsive Web Design: Advantages & Best Practice - Darrin Adams, Cantarus
Responsive Web Design: Advantages & Best Practice - Darrin Adams, CantarusInternet World
 
Offline Direct Marketing for Mobile Marketeers - Sam Heaton, Stannp
Offline Direct Marketing for Mobile Marketeers - Sam Heaton, StannpOffline Direct Marketing for Mobile Marketeers - Sam Heaton, Stannp
Offline Direct Marketing for Mobile Marketeers - Sam Heaton, StannpInternet World
 
How to drive mobile traffic to your local stores? - Bruno Berthezene, Solocal...
How to drive mobile traffic to your local stores? - Bruno Berthezene, Solocal...How to drive mobile traffic to your local stores? - Bruno Berthezene, Solocal...
How to drive mobile traffic to your local stores? - Bruno Berthezene, Solocal...Internet World
 
When smart-phones sense how you feel: The era of intelligent mobile devices -...
When smart-phones sense how you feel: The era of intelligent mobile devices -...When smart-phones sense how you feel: The era of intelligent mobile devices -...
When smart-phones sense how you feel: The era of intelligent mobile devices -...Internet World
 

Mehr von Internet World (20)

IBM's big data seminar programme- the case for big data & analytics - Gareth ...
IBM's big data seminar programme- the case for big data & analytics - Gareth ...IBM's big data seminar programme- the case for big data & analytics - Gareth ...
IBM's big data seminar programme- the case for big data & analytics - Gareth ...
 
Elastic Search Meetup Special - Yann Cluchey, Cogenta
Elastic Search Meetup Special - Yann Cluchey, Cogenta Elastic Search Meetup Special - Yann Cluchey, Cogenta
Elastic Search Meetup Special - Yann Cluchey, Cogenta
 
How to raise venture capital & the First Tuesday Award 2014
How to raise venture capital & the First Tuesday Award 2014How to raise venture capital & the First Tuesday Award 2014
How to raise venture capital & the First Tuesday Award 2014
 
Unreasonable learning - Shane Hill, Skoolbo
Unreasonable learning - Shane Hill, SkoolboUnreasonable learning - Shane Hill, Skoolbo
Unreasonable learning - Shane Hill, Skoolbo
 
London's tech scene's at a critical point - Alex Wood, Tech City News
London's tech scene's at a critical point - Alex Wood, Tech City NewsLondon's tech scene's at a critical point - Alex Wood, Tech City News
London's tech scene's at a critical point - Alex Wood, Tech City News
 
Free:Formers CODE:OFF
Free:Formers CODE:OFF Free:Formers CODE:OFF
Free:Formers CODE:OFF
 
What the Internet of Things means for the mobile enterprise - Ian Evans, AirW...
What the Internet of Things means for the mobile enterprise - Ian Evans, AirW...What the Internet of Things means for the mobile enterprise - Ian Evans, AirW...
What the Internet of Things means for the mobile enterprise - Ian Evans, AirW...
 
Have your cake and eat it too: adopting technologies without sacrificing - Pa...
Have your cake and eat it too: adopting technologies without sacrificing - Pa...Have your cake and eat it too: adopting technologies without sacrificing - Pa...
Have your cake and eat it too: adopting technologies without sacrificing - Pa...
 
Business Networking Hacks in Today’s Connected World - Marian Gazdik, Startup...
Business Networking Hacks in Today’s Connected World - Marian Gazdik, Startup...Business Networking Hacks in Today’s Connected World - Marian Gazdik, Startup...
Business Networking Hacks in Today’s Connected World - Marian Gazdik, Startup...
 
What IT capacity planning can learn from manufacturing's just-in-time models ...
What IT capacity planning can learn from manufacturing's just-in-time models ...What IT capacity planning can learn from manufacturing's just-in-time models ...
What IT capacity planning can learn from manufacturing's just-in-time models ...
 
How personal data has changed and what this means for businesses looking forw...
How personal data has changed and what this means for businesses looking forw...How personal data has changed and what this means for businesses looking forw...
How personal data has changed and what this means for businesses looking forw...
 
The database of you - Andy Caddy, Virgin Active Health Clubs
The database of you - Andy Caddy, Virgin Active Health ClubsThe database of you - Andy Caddy, Virgin Active Health Clubs
The database of you - Andy Caddy, Virgin Active Health Clubs
 
Using big data to find out what women want - John Lervik, Cxense
Using big data to find out what women want - John Lervik, CxenseUsing big data to find out what women want - John Lervik, Cxense
Using big data to find out what women want - John Lervik, Cxense
 
Relevance = Revenue - PK Vaish, Copernica
Relevance = Revenue - PK Vaish, CopernicaRelevance = Revenue - PK Vaish, Copernica
Relevance = Revenue - PK Vaish, Copernica
 
How to drive e-commerce sales with content marketing - David Bowen, EPiServer
How to drive e-commerce sales with content marketing - David Bowen, EPiServerHow to drive e-commerce sales with content marketing - David Bowen, EPiServer
How to drive e-commerce sales with content marketing - David Bowen, EPiServer
 
Innovation at Tesco - Angela Maurer, Tesco
Innovation at Tesco - Angela Maurer, TescoInnovation at Tesco - Angela Maurer, Tesco
Innovation at Tesco - Angela Maurer, Tesco
 
Responsive Web Design: Advantages & Best Practice - Darrin Adams, Cantarus
Responsive Web Design: Advantages & Best Practice - Darrin Adams, CantarusResponsive Web Design: Advantages & Best Practice - Darrin Adams, Cantarus
Responsive Web Design: Advantages & Best Practice - Darrin Adams, Cantarus
 
Offline Direct Marketing for Mobile Marketeers - Sam Heaton, Stannp
Offline Direct Marketing for Mobile Marketeers - Sam Heaton, StannpOffline Direct Marketing for Mobile Marketeers - Sam Heaton, Stannp
Offline Direct Marketing for Mobile Marketeers - Sam Heaton, Stannp
 
How to drive mobile traffic to your local stores? - Bruno Berthezene, Solocal...
How to drive mobile traffic to your local stores? - Bruno Berthezene, Solocal...How to drive mobile traffic to your local stores? - Bruno Berthezene, Solocal...
How to drive mobile traffic to your local stores? - Bruno Berthezene, Solocal...
 
When smart-phones sense how you feel: The era of intelligent mobile devices -...
When smart-phones sense how you feel: The era of intelligent mobile devices -...When smart-phones sense how you feel: The era of intelligent mobile devices -...
When smart-phones sense how you feel: The era of intelligent mobile devices -...
 

Kürzlich hochgeladen

ChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics InfrastructureChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics Infrastructuresonikadigital1
 
Rock Songs common codes and conventions.pptx
Rock Songs common codes and conventions.pptxRock Songs common codes and conventions.pptx
Rock Songs common codes and conventions.pptxFinatron037
 
5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best PracticesDataArchiva
 
Virtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product IntroductionVirtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product Introductionsanjaymuralee1
 
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Guido X Jansen
 
Master's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationMaster's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationGiorgio Carbone
 
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxTINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxDwiAyuSitiHartinah
 
How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?sonikadigital1
 
Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...PrithaVashisht1
 
The Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerThe Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerPavel Šabatka
 
Mapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxMapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxVenkatasubramani13
 
Optimal Decision Making - Cost Reduction in Logistics
Optimal Decision Making - Cost Reduction in LogisticsOptimal Decision Making - Cost Reduction in Logistics
Optimal Decision Making - Cost Reduction in LogisticsThinkInnovation
 
Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Vladislav Solodkiy
 
CI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionCI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionajayrajaganeshkayala
 
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityStrategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityAggregage
 
CCS336-Cloud-Services-Management-Lecture-Notes-1.pptx
CCS336-Cloud-Services-Management-Lecture-Notes-1.pptxCCS336-Cloud-Services-Management-Lecture-Notes-1.pptx
CCS336-Cloud-Services-Management-Lecture-Notes-1.pptxdhiyaneswaranv1
 

Kürzlich hochgeladen (16)

ChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics InfrastructureChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics Infrastructure
 
Rock Songs common codes and conventions.pptx
Rock Songs common codes and conventions.pptxRock Songs common codes and conventions.pptx
Rock Songs common codes and conventions.pptx
 
5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices
 
Virtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product IntroductionVirtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product Introduction
 
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
 
Master's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationMaster's Thesis - Data Science - Presentation
Master's Thesis - Data Science - Presentation
 
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxTINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
 
How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?
 
Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...
 
The Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerThe Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayer
 
Mapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxMapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptx
 
Optimal Decision Making - Cost Reduction in Logistics
Optimal Decision Making - Cost Reduction in LogisticsOptimal Decision Making - Cost Reduction in Logistics
Optimal Decision Making - Cost Reduction in Logistics
 
Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023
 
CI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionCI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual intervention
 
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityStrategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
 
CCS336-Cloud-Services-Management-Lecture-Notes-1.pptx
CCS336-Cloud-Services-Management-Lecture-Notes-1.pptxCCS336-Cloud-Services-Management-Lecture-Notes-1.pptx
CCS336-Cloud-Services-Management-Lecture-Notes-1.pptx
 

IBM's big data seminar programme -moving beyond Hadoop - Ian Radmore, IBM

  • 1. © 2014 IBM Corporation Big Data & Analytics – beyond Hadoop Ian Radmore, IBM UKI Big Data Specialist June 18th, 2014
  • 2. © 2014 IBM Corporation Data: To have and to hold? Or to Analyse and Act! Data in Data at 2
  • 3. © 2014 IBM Corporation The auto industry is already the 2nd largest data generator AND 20% CAGR! Ford Fusion: 145 actuators, 4700 relays and 70 sensors, including radar, sonar, accelerometer, camera, rain sensors. Collectively, these devices generate more than 25 gigabytes of data per hour, which is processed by more than 70 on-board computers. 1 car year = 1TB 3
  • 4. © 2014 IBM Corporation A Big Data & Analytics approach helps provide a foundation for a Smarter Enterprise Invest in aInvest in a big data & analyticsbig data & analytics platformplatform Be proactive aboutBe proactive about privacy, security andprivacy, security and governancegovernance Imagine It. Realise It. Trust It. Build a culture thatBuild a culture that infuses analyticsinfuses analytics everywhereeverywhere Confidence in Your Data Confidence in Accelerating Value Confidence in Your Skills 4
  • 5. © 2014 IBM Corporation Deployed real-time CDR analysis solution to handle exploding data volume growth and performance requirements Analyzes call, internet usage, and text records in real-time to identify and address poorly performing cells Uses InfoSphere Streams and IBM Netezza Significant Benefits: Over 90% reduction in time to merge/load call record data Over 90% reduction in storage Increased network quality, improved customer satisfaction, reduced churn Sprint Increases Revenue & Improves Customer Satisfaction “Over 90+% reduction in merge/load times and storage requirements” “Over 90+% reduction in merge/load times and storage requirements” Capabilities Utilised: • Stream processing • Data Warehouse Analytics Appliance 5
  • 6. © 2014 IBM Corporation • Examines trends, volume, and content of millions of public Twitter messages in real-time • Analytic accelerators to understand sentiment (positive, negative, neutral) • Capabilities • Stream Computing • Visualization • Benefits • Real-time display of public sentiment as candidates respond to questions • Debate winner prediction based on public opinion instead of solely political analysts University of Southern California Innovation Lab Monitors Political Debates Solution to measure public sentiment during key primary & general presidential debates 6
  • 7. © 2014 IBM Corporation 7 KTH Swedish Royal Institute of Technology Reducing Traffic Congestion • Deployed real-time Smarter Traffic system to predict and improve traffic flow. • Analyzes streaming real-time data gathered from cameras at entry/exit to city, GPS data from taxis and trucks, and weather information. • Predicts best time and method to travel such as when to leave to catch a flight at the airport Significant benefits: • Enables ability to analyze and predict traffic faster and more accurately than ever before • Provides new insight into mechanisms that affect a complex traffic system • Smarter, more efficient, and more environmentally friendly traffic 7 Capabilities Utilised: Stream Computing 7
  • 8. © 2014 IBM Corporation Pacific Northwest Smart Grid Demonstration Project Capabilities: Stream Computing – real-time control system Data Warehouse Appliance – analyze massive data sets Demonstrates scalability from 100 to 500K homes while retaining 10 years’ historical data 60k metered customers in 5 states Accommodates ad hoc analysis of price fluctuation, energy consumption profiles, risk, fraud detection, grid health, etc. 8
  • 9. © 2014 IBM Corporation Information Integration & Governance Systems Security On premise, Cloud, As a service Storage IBM Watson Foundations IBM Big Data & Analytics Infrastructure New / Enhanced ApplicationsAll Data What action should I take? Decision management Cognitive What did I learn? Landing, Exploration and Archive data zone EDW and data mart zone Operational data zone Real-time Data Processing & Analytics What is happening? Discovery and exploration Why did it happen? Reporting, content and analysisWhat could happen? Predictive analytics and modelling Deep Analytics data zone 8 Realise It. Invest
  • 10. © 2014 IBM Corporation Realise It. In-Store Presence Zones Intelligent location-based technology to gain deep insight into customer in-store behaviour Enable retailers to integrate the physical and digital experience to facilitate an ongoing dialogue that creates loyalty via an exceptional in-store shopping experience Presence Zones Sensors 9
  • 11. © 2014 IBM Corporation IBM Internal Use Only Realise It. The Customer Insight Appliance 10
  • 12. © 2014 IBM Corporation Realise It. A Multichannel Korean retailer Reliable insight provides decision support for senior management Targeted campaigns can be developed for marketing Precise measurement of cross-channel campaigns Business Challenge: As sales increased for this retailer’s online shopping mall, management experienced increasing difficulty ensuring that an appropriate product mix was being presented to its customers. The Solution: The company adopted sophisticated analytics and marketing automation to understand, predict and act on consumer buying behavior with confidence. Real-time marketing automation delivers personalised content to each shopper, triggered by their interaction history. Delivered at the right place and time, these offers can move the shopper toward a sale and even increase the size of the purchase. “We have greatly improved our understanding of our customers, which is helping us to make smarter decisions that significantly improve business performance.” —Spokesperson, multichannel Korean retailer Combining marketing automation with analytics to personalise communications and optimise offerings 11
  • 13. © 2014 IBM Corporation Millions of events per second Microsecond Latency Traditional / Non-traditional data sources Real time delivery Powerful Analytics Algorithmic Trading Telco Churn Prediction Smart GridCyber Security Government / Law enforcement ICU Monitoring Environment MonitoringValue Clear business goals Business change driven outcomes Volume Terabytes/second Petabytes/day Variety All kinds of data All kinds of analytics Velocity Decisions in microseconds Massively scalable Veracity Screening, validation & certification of data Example Streaming Data Sources: Video, Audio, Networks, Social Media, Sensor, Weather Realise It. IBM InfoSphere Streams: Real-Time Adaptive Analytics for Big Data In-Motion Connected Car 11 3
  • 14. © 2014 IBM Corporation Create foundation of trusted data Understand usage and monitor compliance Model exposure and understand variability Trust the factsTrust the facts Ensure privacyEnsure privacy and securityand security Make riskMake risk aware decisionsaware decisions Trust It. Be proactive about privacy, security and governance. 14
  • 15. © 2014 IBM Corporation Big Data Uses Cases Delivered with Unique IBM Capabilities Unique IBM Capabilities: 1. In-memory computing with BLU Acceleration 2. Data privacy and security of big data 3. Data Discovery and Exploration 4. Building Confidence in Big Data with Information Governance 5. Stream computing WATSON FOUNDATIONS Decision Management Planning & Forecasting Discovery & Exploration Business Intelligence & Predictive Analytics Content Analytics Information Integration & Governance Data Mgmt & Warehouse Hadoop System Stream Computing Content Management WATSON FOUNDATIONS Decision Management Planning & Forecasting Discovery & Exploration Business Intelligence & Predictive AnalyticsBusiness Intelligence & Predictive Analytics Content Analytics Information Integration & Governance Data Mgmt & Warehouse Hadoop System Stream Computing Content Management Real-time traffic flow optimisation Low-latency network analysis Fraud & risk detection Predictive asset maintenance Understand and act on customer sentiment Predict and act on intent to purchase 15
  • 16. © 2014 IBM Corporation 16
  • 17. © 2014 IBM Corporation http://www.youtube.com/watch?v=FGp-h-x0Hss 17
  • 18. © 2014 IBM Corporation Building a real-time enterprise is a journey, which depends on a solid Big Data & Analytics foundation for success Be proactive about privacy, security and governance Build a culture that infuses analytics everywhere Invest in a big data & analytics platform Imagine It. Realise It. Trust It. 18
  • 19. © 2014 IBM Corporation Ian Radmore IBM Big Data Specialist, UK & Ireland IBM United Kingdom Limited City Gate West Toll House Hill Nottingham NG1 5FN Mobile +44 7843 368078 Ian.radmore@uk.ibm.com 19