close

Вход

Забыли?

вход по аккаунту

?

Big Data: The New Era of Computing

код для вставки
В© 2013 IBM Corporation
Big Data: The New Era of Computing
March 6 2013
Martin J. Wildberger
VP, WW Information Management Development
IBM Software Group
В© 2013 IBM Corporation
2
Imagine the Possibilities in a World with No Limits
•
Data & content
•
Apps, web & sensors
•
At rest & in motion
•
Parallel processing
•
Virtualization
•
Optimized systems
Unlimited Compute Capacity Analytics
to Action Information from Everywhere
•
Big data analytics
•
Real
-
time analytic processing
•
New discovery paradigms
В© 2013 IBM Corporation
3
Mobile
Social
Cloud
Internet of Things
•
Business models under constant pressure
•
Demanding and connected customers and citizens •
Great relationships trump great products and services We’ve Moved into a New Era of Computing…
В© 2013 IBM Corporation
4
> 24
Petabytes
of data processed by Google in a single day
1.3 Zettabytes
of annual Internet traffic by 2016 1 Terabyte
of trade information captured per session
40 Terabytes
of Hadron Collider data generated daily 150 Exabytes
global size of “
Big Data
”
in Healthcare 30 Petabytes
of data transferred through its networks daily We are Seeing an Explosion of Data
В© 2013 IBM Corporation
5
4. Gain IT efficiency and scale
with Data Warehouse Augmentation What is Big Data?
The power of Data coming together… …with the power of Technology… …to deliver Improved Outcomes
1.
Enrich your information base
with Big Data Exploration
5. Prevent crime
with Security and Intelligence Extension
3. Optimize operations
with Operations Analysis
2. Improve customer interaction
with Enhanced 360Вє View of the Customer
•
Limitless compute and storage
•
Parallel processing techniques
•
Stream computing
•
Hardware
-
based query acceleration •
Limitless access to data
Variety
Volume
Velocity
Veracity
В© 2013 IBM Corporation
6
Using real
-
time analytics to detect life threatening conditions 24 hours sooner than symptoms exhibited improves quality of care for neonatal babies.
5
556 million cybercrime victims in 2011. All this new data and access to it brings new privacy and security requirements. The cost of losing customer loyalty (lost business) following a data breach is estimated at $3 million.
2
Sources: 1. Strategies to Reduce Costs and Improve Public Sector Performance, IBM, Nov 2011
2. http://www.symantec.com/about/news/release/article.jsp?prid=20110907_02
3. IBM Institute for Business Value, 2009
4. IBM CEO Study 2010
5. University of Ontario Institute of Technology -
http://public.dhe.ibm.com/common/ssi/ecm/en/odc03157usen/ODC03157USEN.PDF
In Canada, applying new analytical techniques in social services could help identify up to 40% of improper payments
. This could mean identifying up to $4.8 billion in potential reductions
in estimated improper payments.
1
The Demand for Big Data Solutions is Real
60% of CEOs agree they have more data than they can use effectively.
3
1 in 2 organization leaders say they don’t have access to the information they need to do their jobs.
4
В© 2013 IBM Corporation
7
Big Data Exploration
Enhanced 360
o
View
of the Citizen
Operations Analysis
Data Warehouse Augmentation
Security/Intelligence Extension
Addressing the Demand with Big Data
В© 2013 IBM Corporation
8
Applications for Big Data Analytics Span I
ndustries
“The Art of the Possible”
Homeland Security
Traffic Control
Telecom
Search Quality
Trading Analytics
Manufacturing
Fraud and Risk
Retail: Churn, NBO
Log Analysis
Finance Smarter Healthcare
Multi
-
channel sales
В© 2013 IBM Corporation
9
Explore and mine big data to find what is interesting and relevant to the business for better decision making
Requirements •
Explore new data sources for potential value
•
Mine for what is relevant for a business imperative •
Assess the business value of unstructured content
•
Uncover patterns with visualization and algorithms •
Prevent exposure of sensitive information
1. Big Data Exploration
Industry Examples
•
Customer service knowledge portal
•
Insurance catastrophe modeling •
Automotive features and pricing optimization •
Chemicals and Petroleum conditioned base maintenance •
Life Sciences drug effectiveness
•
… © 2013 IBM Corporation
10
The State University of New York (SUNY) at Buffalo gains insights from big data to slow progression of multiple sclerosis
Need
•
Researchers needed to quickly build models using a range of variable types and run them on a high
-
performing environment on huge data sets spanning more than 2,000 genetic and environmental factors that may contribute to multiple sclerosis (MS) symptoms
Benefits
•
Able to reduce the time required to conduct analysis from 27.2 hours to 11.7 minutes
•
Researchers are empowered to look for potential factors contributing to the risk of developing MS
10
10
В© 2013 IBM Corporation
11
Elie Tahari combines fashion savvy with powerful analytics Need
•
Enhance decision
-
making and keep pace with customer demand and provide faster access to actionable information about current production and inventory as well as future demand
Benefits
•
Able to predict customer orders 4 months in advance with 97 percent accuracy
•
Reduced reporting time from days to minutes and reduced supply chain logistics costs by 30 percent
•
Combined real
-
time sales, inventory and logistics information with near
-
real
-
time data warehouse design to update all transactional data within 5 minutes or less
11
11
В© 2013 IBM Corporation
12
Optimize every customer interaction
by knowing everything about them
2. Enhanced 360Вє View of the Customer
Requirements
•
Create a connected picture of the customer/citizen •
Mine all existing and new sources of information
•
Analyze social media to uncover sentiment about products •
Add value by optimizing every client interaction
Industry Examples
•
Smart meter analysis •
Telco data location monetization
•
Retail marketing optimization
•
Travel and Transport customer analytics and loyalty marketing •
Financial Services Next Best Action and customer retention •
…
В© 2013 IBM Corporation
13
Ufone reduced churn and kept subscribers happy, helping ensure that campaigns are highly effective and timely
Need
•
To ensure that its marketing campaigns targeted the right customers, before they left the network
•
To keep its higher usage customers happy with campaigns offering services and plans that were right for them
Benefits
•
Predictive analytics is expected to improve the campaign response rate from about 25% to at least 50%
•
CDRs can be analyzed within 30 seconds, instead of requiring at least a day
•
Expected to reduce churn by approximately 15
-
20% 13
В© 2013 IBM Corporation
14
14
В© 2013 IBM Corporation
Bass Pro –
optimizing marketing with multi
-
channel customer and sales analytics
Need
•
Existing EDW couldn’t provide detailed analytics on individual customers or purchases or multiple channels
•
Couldn’t analyze their business at the shopper level: path to purchase, market basket, or cross channel
•
Couldn’t give shoppers more value through more targeted offers, products, etc.
Benefits
•
Unprecedented ability to analyze the business -
at the customer, store, transaction level -
across all channels seamlessly
•
Bass Pro is positioned to dominate via analytics over competitors
-
Platform in place to support analytics
-
Growing skills and processes in business
В© 2013 IBM Corporation
15
3. Operations Analysis
Requirements •
Analyze machine data to identify events of interest •
Apply predictive models to identify potential anomalies •
Combine information to understand service levels •
Monitor systems to avoid service degradation or outages
Industry Examples
•
Automotive advanced condition monitoring •
Chemical and Petroleum condition
-
based maintenance •
Energy and Utility condition
-
based maintenance •
Telco campaign management
•
Travel and Transport real
-
time predictive maintenance •
… Apply analytics to machine data for greater operational efficiency © 2013 IBM Corporation
16
Dublin City Centre; Robust
and efficient citywide traffic awareness system, enhance rapid action on incidents
Need
•
A budget effective solution to improve traffic awareness system. •
To bring accuracy in event detection, inferring traffic condition (road speed) and prediction of bus arrival. •
Challenge is to rightly analyze GPS data, which is typically high data throughput and difficult to capture
Benefits
•
Monitor 600 buses across 150 routes daily
•
Analyzes 50 bus location updates per second , using InfoSphere Streams
•
Collects, processes, and visualizes location data of all public transportation vehicles
16
16
В© 2013 IBM Corporation
17
4. Data Warehouse Augmentation
Requirements •
Add new sources to existing data warehouse investments •
Optimize storage and provide query
-
able archive •
Rationalize for greater simplicity and lower cost •
Enable complex analytical applications with faster queries •
Scale predictive analytics and business intelligence Industry Examples
•
Pre
-
Processing Hub
•
Query
-
able Archive
•
Exploratory Analysis
•
Operational Reporting
•
Real
-
time Scoring
•
Segmentation and Modeling
Exploit technology advances to deliver more value from an existing data warehouse investment while reducing cost
В© 2013 IBM Corporation
18
Trident Marketing increases revenue nearly 1,000 percent with predictive analytics powered by IBM Netezza
Need
•
To acquire the maximum number of paying customers, while minimizing the cost of sales, Trident needed insight into which customers are most likely to buy which products, and when.
Benefits
•
Tenfold increase in revenue in four years
•
Ten percent increase in sales in 60 days
•
Thirty percent decrease in paid search marketing costs
18
18
В© 2013 IBM Corporation
19
5. Security and Intelligence Extension
Requirements •
Enhanced Intelligence and Surveillance Insight ‒
Find associations, uncover patterns and facts
•
Real
-
time Cyber Attack Prediction and Mitigation ‒
Discover new and known complex threats sooner
•
Crime Prediction and Protection ‒
Prevent criminal activities and apprehend criminals Industry Examples
•
Government threat and crime prediction and prevention •
Insurance claims fraud •
…
Enhance traditional security solutions to prevent crime by analyzing all types and sources of big data
В© 2013 IBM Corporation
20
Link to the Case Study
http://public.dhe.ibm.co
m/common/ssi/ecm/en/i
mc14775usen/IMC1477
5USEN.PDF
Insurance Bureau of Canada (IBC) outsmarting fraudsters with fraud analytics
Need
•
Automate processes to detect potential claim fraud and identify possible fraud rings. Fraud is said to account for 10 to 15 percent of Insurance losses to drive up claims costs
Benefits
•
Automates identity insight solution replaced the manual ad hoc approach to detect and investigate potential fraud
•
Analytics increases opportunities to identify previously unidentified fraud rings
•
Increases confidence in gathering information against suspected fraudsters
20
20
В© 2013 IBM Corporation
21
TerraEchos uses streaming data technology to support covert intelligence and surveillance sensor systems
Need

Deployed security surveillance system to detect, classify, locate, and track potential threats at highly sensitive national laboratory
Benefits

Reduced time to capture and analyze 275MB of acoustic data from hours to one
-
fourteenth of a second

Enabled analysis of real
-
time data from different types of sensors and 1,024 individual channels to support extended perimeter security

Enabled a faster and more intelligent response to any threat
21
21
В© 2013 IBM Corporation
22
Recommendations for Getting Started
1.
Understand
the “
Art of the Possible.
”
2.
Start with a clear mission or business requirement, and fully define a discrete set of use cases. 3.
Take inventory
and understand your data assets.
4.
Assess your current capabilities and technical architecture against what is required for your initial use cases.
5.
Explore
which data assets can be exposed for public consumption, to drive innovation and the development of Big Data solutions. Get Started. Be Bold. Think Big. ibm.com/bigdata
23
В© 2013 IBM Corporation
В© 2013 IBM Corporation
24
IBM’
s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion. Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion
.
Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user
’
зЊ ж©Їs зЌґз‰Ґж…­в° з‘Ёж” д¤ЇI жЌЇcж™©f畲慴楯測nз‘Ёж” зЌґs牡来rжЌЇcж™©f畲慴楯nв° ж…®a з‘Ёж” wжЅІж­¬жЅЎжђ зЃІжЅЈж•ізЌҐжђ®p 周敲敦潲攬e
ж№ЇвЃЎзЌіз•Іж…®жЌҐ жЌЎжё ж‰ҐвЃ§жҐ¶ж•®eз‘Ёж…ґaж…®a楮i楶楤iaж° з•іж•ІuwжҐ¬ж° ж…ЈжЎ©ж•¶ж” з‰ҐзЌµж±ґзЊ зЌ©жµ©ж±Ўз€ з‘Ї з‘ЁжЅіж” зЌґж…ґж•¤e桥牥h
Please note
BACKUP
В© 2013 IBM Corporation
26
Different Types of Data Are Coming Together…
Manage the complexity of multiple relational and non
-
relational data types and schemas
Variety
Streaming data and large volume data movement
Velocity
Scale from terabytes to zettabytes
Volume
Managing the reliability and predictability of inherently imprecise data types Veracity
“
We have for the first time an economy based on a key resource [Information] that is not only renewable, but self
-
generating. Running out of it is not a problem, but drowning in it is.
”
-
John Naisbitt
В© 2013 IBM Corporation
27
•
Limitless compute and storage
•
Commodity compute and tiered storage
•
Parallel processing techniques
•
MapReduce
and Stream •
Stream computing •
Real
-
time analytics and event detection
•
Hardware
-
based query acceleration
•
Analytical appliances with embedded analytics •
Limitless access to data
•
And ability to analyze any sort of data inc. social and sensor
…With Evolving Technology
В© 2013 IBM Corporation
28
Constant Contact Transforming Email Marketing Campaign Effectiveness with IBM Big Data Need
•
Analyze 35 billion
annual emails to guide customers on best dates & times to send emails for maximum response
Benefits
•
40 times improvement in analysis performance •
15
-
25% performance increase in customer email campaigns
•
Analysis time reduced from hours to seconds
28
28
28
В© 2013 IBM Corporation
29
Colt Technology Services Group saves USD 1.9M annually through improved Netezza business intelligence
Need
•
Gain a 360
-
view of the customer and eliminate manual processes to identify data from over 15 systems
Benefits
•
USD 1.9M in annual savings
•
90% reduction in the time to complete �wildcard’ searches
•
More than 95% reduction in the time to gather information
29
В© 2013 IBM Corporation
30
T
-
Mobile scales engineering success with IBM Netezza
Need
•
Provide longer retention and more meaningful results for click stream data
Benefits
•
Reduce tax and call
-
routing fees by using the data stored to defend against false claims
•
Acquire dropped
-
call and churn
-
reduction analysis capabilities
•
Increase network availability by identifying and fixing any network “
holes
”
•
Storage capacity increased from 100 TB to 2 PB
30
30
В© 2013 IBM Corporation
31
NYSE Euronext improves data management with Netezza data warehousing
Need
•
Greater flexibility to meet market demands
•
Reduce the time taken to access business
-
critical data on its network, which was taking 26 hours
•
The previous Oracle system trawled through large amounts of irrelevant information to complete searches
Benefits
•
Ability to conduct rapid searches of 650 TB of data; storing over 1 PB on Netezza
•
Time to access business
-
critical data reduced from 26 hours to 2 minutes; short time to value –
up & running within weeks
31
31
В© 2013 IBM Corporation
32
Execute
Deployed two or more big data initiatives and continuing to apply advanced analytics
Engage
Pilo
ting big data initiatives to validate value and requirements
Explore
Developing strategy and roadmap based on business needs and challenges
Educate
Focused on knowledge gathering and market observations
Big data adoption
Enterprise
-
wide big data initiatives
-
Incremental value across multiple use cases
-
Leverage investment from re
-
using the same big data platform
-
Enterprise data platform to support analytics
Big data case studies, whitepapers and
IBM Institute for Business Value reports
ibmbigdatahub.com
Join the technical community
IBM Briefings, Solution Centers
IBM Readiness Assessment for Big Data -
Prioritized business use cases
-
Recommend big data platform
Solution Design & Proof of Concept
-
Validate business value of the big data use case
-
Demonstrate big data capabilities to execute use case
Your Big Data Journey –
We can Help
Self
-
paced learning, exploration with downloads & test environment
BigDatauniversity.com, YouTube Big Data Channel
Join the business community
Автор
Editor
Editor160   документов Отправить письмо
Документ
Категория
Образовательные
Просмотров
83
Размер файла
3 570 Кб
Теги
Big Data
1/--страниц
Пожаловаться на содержимое документа