You've heard of Big Data for sure. But what are the implications of this for your organisation? Can your organisation leverage Big Data too? If you decide to go ahead with your Big Data implementation where do you start? If these questions sound familiar to you then you've stumbled upon the right presentation. Go through the presentation to:
a. Learn more on Big data
b. How Big data can help you outperform in your marketplace.
c. How to proactively manage security and risk
d. How to create IT agility to underpin the business
Also, learn about IBM's superior Big Data technologies and how they are helping today's organisations take smarter decisions and actions.
2. Four Technologies Help Define the Smarter
Enterprise
CLOUD
COMPUTING
ENTERPRISE
MOBILITY
BIG DATA
ANALYTICS
SOCIAL BUSINESS
Client-centric,
digitally savvy in
its use of cloud,
mobile, social and
big data platforms
to transform
Embraces data in
all forms to apply
analytics, unlock
insight, and make
fact-based
decisions
Creates value in
new ways by
forging deeper
relationships with
clients and
between
employees
Constantly adapts
to changing
market dynamics,
buyer demands
and
disruptive
technologies
3. The number of organizations who see analytics as a
competitive advantage is growing.
63%
2010
business initiative
2011
2012
BUSINESS IMPERATIVE
IQ
4. What’s Changing?:
Big Data & Analytics Is Expanding Quickly
Data is the
world’s
newest resource
Decision-making
extends from few to many
As data value grows,
current systems won’t
keep pace
6. Why Act Now?
To outperform in your industry
To proactively manage security
and risk
To create IT agility to underpin
the business
7. Examples of Outstanding Performance Driven
by Big Data and Analytics
Traditional Approach
One size fits all marketing
å
Manual weather forecasting
Slow claims processing
Transformational Outcomes
Personalized, realtime marketing
offers
Real-time,
automated weather
prediction
Intelligent &
accelerated fraud
detection
Monthly risk management
Real-time Risk
Analysis
Just in time maintenance
Predictive
maintenance &
improved uptime
8. To Manage Risk and Create Agility: Embrace All
Data
….Uncertainty of New Information is Growing Alongside its
Complexity
Volume
Data at Scale
Terabytes to
petabytes of data
Variety
Data in Many
Forms
Structured,
unstructured, text,
multimedia
Velocity
Veracity
Data in Motion
Data Uncertainty
Analysis of streaming
data to enable
decisions within
fractions of a second.
Managing the
reliability and
predictability of
inherently imprecise
data types.
9. The Big Data Conundrum
The economies of deletion have changed….
• Leading us into new opportunities and challenges
• The percentage of available data an enterprise can analyze is decreasing proportionately
to the available to that enterprise
• Quite simply, this means as enterprises, we are getting
“more naive” about our business over time
• Just collecting and storing “Big Data” doesn’t drive a cent
of value to an organization’s bottom line
Data AVAILABLE to
an organization
Data an organization
can PROCESS
10. By 2015, 80% of All Available Data Will Be Uncertain
1 in 3
9000
7000
90
80
6000 70
5000 60
50
4000 40
3000 30
20
2000
Aggregate Uncertainty %
Global Data Volume in Exabytes
8000
10
0
Rising Uncertainty =
Declining Confidence
1 in 2
Lack the information
that they need
We are
here.
Sensors
Internet of
things
Social media
10
Video, Audio and Text
1000
0
Make decisions on
untrustworthy data
VoIP
Enterprise Data
Multiple sources: IDC, Cisco
2005
2015
2010
60%
Have too much data
11. IBM Big Data and Analytics: Helps You Outperform,
Manage Risk and Create IT Agility
CONSULTING and IMPLEMENTATION SERVICES
SOLUTIONS
Sales Marketing Finance
Risk
IT
Operations HR
Watson and Industry Solutions
ANALYTICS
Content
Decision
Analytics
Managemen
t
Business Intelligence and Predictive Analytics
Performance
Management
Risk
Analytics
BIG DATA PLATFORM
Content
Management
Hadoop
System
Stream
Data
Computin
Warehouse
g
Information Integration and Governance
SECURITY, SYSTEMS, STORAGE AND CLOUD
Scale
Management
Parallel
Processing
Low Latency
Resources
Data
Optimization
The Whole is Greater
Than the Sum of the Parts
Broadest set of capabilities across
big data and analytics
Pre-integrated components accelerate
value
Pre-built industry and horizontal
solutions
Integration and optimization with
storage
and infrastructure
Delivered in multiple forms: software,
appliance, and cloud
World-class consulting and
implementation drives innovation and
value
12. Big Data and Analytics Solutions Across
Industries
Banking
Insurance
Telco
Energy &
Utilities
Media &
Entertainment
Optimizing Offers and
Cross-sell
360˚ View of Domain
or Subject
Pro-active Call
Center
Smart Meter
Analytics
Business process
transformation
Customer Service and
Call Center Efficiency
Catastrophe
Modeling
Network Analytics
Distribution Load
Forecasting/Scheduli
ng
Audience &
Marketing
Optimization
Fraud & Abuse
Location Based
Services
Condition Based
Maintenance
Retail
Actionable Customer
Insight
Customer Analytics &
Loyalty Marketing
Merchandise
Optimization
Predictive
Maintenance
Analytics
Dynamic Pricing
Automotive
Advanced Condition
Monitoring
Data Warehouse
Optimization
Consumer
Products
Travel &
Transport
Chemical &
Petroleum
Operational
Surveillance, Analysis &
Optimization
Data Warehouse
Consolidation,
Integration &
Augmentation
Government
Healthcare
Shelf Availability
Civilian Services
Promotional Spend
Optimization
Defense &
Intelligence
Measure & Act on
Population Health
Outcomes
Merchandising
Compliance
Tax & Treasury
Services
Engage Consumers in
their Healthcare
Aerospace &
Defense
Electronics
Uniform Information
Access Platform
Customer/ Channel
Analytics
Data Warehouse
Optimization
Advanced Condition
Monitoring
Life Sciences
Increase visibility into
drug safety and
effectiveness
13. Harvest Business Value via Key Business-Driven Use Cases
Enrich Your Information
Base with Big Data
Exploration
Reduction In Time Required
For Analysis
Help Reduce Risk and Prevent
Fraud with
Security and
Intelligence Extension
1,100
99%
Improve Customer
Interaction with
Enhanced 360 View
of the Customer
42TB
Association Publishing Partnerships
Big Data Exploration
Find, visualize, understand
all big data to improve
business knowledge
Real-time Acoustic Data Analyzed
Enhanced 360o View
of the Customer
Security/Intelligence
Extension
Achieve a true unified view,
incorporating internal and
external sources
Lower risk, detect fraud
and monitor cyber security
in real-time
Optimize Infrastructure
and Monetize Data with
Operations Analysis
60K
Metered Customers
in Five States
Gain IT Efficiency and Scale
with Data Warehouse
Augmentation
40X
Gain in Analysis Performance
Operations Analysis
Data Warehouse Augmentation
Analyze a variety of machine
data for improved business results
Integrate big data and data warehouse
capabilities to increase operational efficiency
14. Big Data & Analytics Reference Architecture
Cognitive Computing
Real-time
Analytics
Data in
Motion
Information
Ingestion
and
Operational
Information
Landing Area,
Analytics
Zone
and Archive
Exploration,
Integrated
Warehouse,
and
Mart Zones
Data at
Rest
Information Governance,
Security & Business Continuity
Data in
Many Forms
Real-time Analytics
& Decision Management
DecisionMaking
Planning & Forecasting
Predictive Analytics
& Content Analytics
Reporting, Analysis
& Dashboards
Business
Processes
Data Discovery
& Visualization
Security, Systems, Storage and Cloud
Point of
Interaction
15. Infrastructure Matters to Support
New Big Data & Analytics Architecture
Core infrastructure
capabilities deliver
speed and confidence
Data
Optimization
Low Latency
Parallel
Processing
Scalability
An efficient and agile
infrastructure balances
the needs of different
analytics workloads
Optimal
Infrastructure
Predictive Analytics
Data Warehouse
SCM*
Cores
Text Analytics
Hadoop Workloads
Optimization
Sensitivity
Analysis
Network
Storage
* SCM-Storage Class Memory
16. Delivering Workload Optimized Performance
System for
Transactions
System for
Analytics
For apps like
Order Management
For apps like
Sales Analysis
Database cluster services
optimized for transactional
throughput and scalability
Data warehouse services
optimized for high-speed,
peta-scale analytics and
simplicity
System for
Operational Analytics
System for
Hadoop
For apps like
Real-time
Fraud Detection
For apps like
Big Data
Exploration
Operational data warehouse
services optimized to balance
high performance analytics
and real-time operational
throughput
Hadoop services optimized
for exploration of large
volumes of data with any
type of structure; and as a
queryable archive to
augment traditional data
warehousing
17. Complementary Analytics
Traditional Approach
New Approach
Structured, analytical, logical
Creative, holistic thought, intuition
Data Warehouse
Hadoop and
Streams
Multimedia
Transaction
Data
Web Logs
Internal App
Data
Mainframe
Data
Social Data
Structured
Repeatable
Linear
Unstructured
Exploratory
Dynamic
Sensor data:
images
OLTP System
Data
ERP
Data
RFID
Traditional
Sources
17
Text Data:
emails
New
Sources
18. A Year of Innovation for Big Data & Analytics
AGILE
GOVERNANCE FOR
ALL DATA Single point
Find and
protect
sensitive data
80% faster
monitoring
of security
for
traditional,
NoSQL, and
big data
PERFORMANCE
MANAGEMENT and
BUSINESS INTELLIGENCE
Cognos TM1 with
Mobile
contribution
Deploy on
Cloud, zLinux,
on premise.
Integrated
metrics and
scorecarding
Native mobile
on iOS and
Android
INFRASTRUCTURE
Analytics on
POWER 7-14x
lower TCO
X-86 innovation –
40% better perf
efficiency
System x – open
analytics on Linux
IBM Flash
Systems for low
latency analytics.
Real-time
compression to
access all
relevant data
19. IBM Big Data & Analytics Momentum
40,000
AnalyticsZone.com
Members
1550
30,000
1040
1100
730
170
Big data
Clients
Business
Partners
Big Data
Clients
85
Info Agenda
Engagements
2010
Big Data
Clients
860
Info Agenda
Engagements
2011
10,000
Big Data University
Enrollments
Big Data
Clients
9th
Analytics Solution
Center Opens
in Ohio
GBS Information and
Analytics Engagements
1640
2215
Business
Partners
2,300
Info Agenda
Engagements
40,000
Big Data University
Enrollments
Business
Partners
3,810
Info Agenda
Engagements
2012
Source: IBM. Note: All numbers used are cumulative.
3/31/2013
101,000
Big Data University
Enrollments
2013
21. IBM Is Helping Address the Analytics Skills
Gap
New technologies designed for business users
IBM AnalyticsZone to download and
experiment with software
Big Data University with robust curriculum
Big Data Stampede for accelerated value
Partnering with major universities globally
On-line resource centers & books written by
IBM thought leaders
22. How to Get Started
1. Build a culture that infuses analytics everywhere
Develop a curiosity-driven and evidence-inspired workforce
2. Be proactive about privacy, security and governance
Forward-thinking approaches to maximize impact while balancing risk
3. Invest in a Big Data & Analytics platform
Build to master plan: all data, all analytics, full range of business outcomes
23. NO OTHER VENDOR
can make this statement
IBM delivers a governable,
consumable Big Data platform
that’s steeped in analytics for data
in-motion and data at-rest.
This slide shows two examples of just how instrumented our world has become. On the left is a Brasilian clothing retailer who has linked up smart hangers with Facebook, when you touch it, the Like factor gets incremented and your RFID-enabled card adds what you like to your personal wish list. On the right is the new Nike LeBron James basketball shoes, it’s instrumented such that it can tell you how far you ran, how high you jumped, and so on.
Why act now? We’ve set the groundwork of what big data & analytics can do to make you a smarter enterprise. There needs to be a compelling reason to act. The top three reasons to act now: To outperform your industry To manager risk To create IT agility to underpin the business
Shinsegae Mall - A leading retailer in South Korea gathers deep insights into consumer behavior and runs targeted online marketing campaigns for greater profitability and loyalty when it implements a solution based on IBM Unica Campaigns software, IBM Netezza Data Warehouse software, IBM InfoSphere software, IBM Cognos software, IBM SPSS Modeling software and IBM Power 570 systems running IBM AIX 6 (http://w3-01.ibm.com/sales/ssi/cgi-bin/ssialias?infotype=CR&subtype=NA&htmlfid=0CRDD-8H8GRX&appname=crmd)Guohua Energy Investment Co. - A renewable wind energy utility company in Beijing improves forecasting accuracy by 15 percent and boosts power-producing capabilities by 10 percent when it engages IBM China Research Lab and deploys a power output-forecasting solution based on a HyREF solution; IBM business analytics and IBM Information Management software; and IBM BladeCenter, IBM System Storage and IBM System x technology (http://w3-01.ibm.com/sales/ssi/cgi-bin/ssialias?infotype=CR&subtype=NA&htmlfid=0CRDD-9868WR&appname=crmd)Allianz Life Insurance - A major Korean insurance company estimates gaining nearly USD1.4 billion in profits, reducing liability fees by 12 percent, accelerating the fraud detection process by 50 percent and increasing employee productivity by 60 percent when it engages IBM Business Partner KSTEC to implement a fraud detection solution based on KSTEC SmartWorks FDS software as well as IBM DB2 Enterprise data server, IBM SPSS Modeler, IBM WebSphere ILOG and IBM WebSphere Application Server applications running on IBM System p, IBM System x and IBM System z servers and validated on the IBM Insurance Industry Framework (http://w3-01.ibm.com/sales/ssi/cgi-bin/ssialias?infotype=CR&subtype=NA&htmlfid=0CRDD-8JKKCM&appname=crmd)State Bank of India –A global bank based in India analyzes credit risk in near-real time and cuts reporting time by 92 percent when it taps IBM Global Services - Global Business Services and IBM SPSS Lab Services to implement IBM Business Analytics and IBM Information Management software supported by an IBM WebSphere solution to help it proactively manage risk and comply with Basel II recommendations (http://w3-01.ibm.com/sales/ssi/cgi-bin/ssialias?infotype=CR&subtype=NA&htmlfid=0CRDD-97SARQ&appname=crmd) Thiess Pty. Ltd - A mining company in Australia reduces heavy equipment maintenance costs and improves productivity when it works with IBM Global Services - Global Business Services and IBM Research to pilot an advanced condition-monitoring solution based on IBM SPSS software and an IBM DB2 data server (http://w3-01.ibm.com/sales/ssi/cgi-bin/ssialias?infotype=CR&subtype=NA&htmlfid=0CRDD-94A3KM&appname=crmd)
Key PointsWe’re all familiar with the 3 V’sVolume is about rising volumes of data in all of your systems – which presents a challenge for both scaling those systems and also the integration points among themVariety is about managing many types of data, and understanding and analyzing them in their native form.Velocity is about ingesting data in real time and in-motionAnd veracity deals with the certainty, or truthfulness of big data. Veracity is a big issue – and one that directly relates to confidence. In fact, as the complexity of big data rises (the first 3 Vs grow), it actually becomes harder to establish veracity.
Key PointsResearch shows that data uncertainty is rising along with the volume of data, and we’re relatively early in the cycle. Why is uncertainty rising?One reason is that we are tapping into external data more than ever before. When combining external data, sometimes from uncertain sources, the overall level of uncertainty rises.Another reason are the various inputs – there are more sources of data. More fragmented records that need to be reconciled.Look at the statistics on the right1/3 make decisions on untrustworthy data. That’s from 2012. What is it like today? Or in 2014? 1/2 lack information and want more, yet 60% have too much data. That’s a paradox. We want more, but we can’t handle it. The answer isn’t making data “smaller”It isn’t ignoring new sources of big data and insight.And it isn’t making the data perfectly certain – that’s a fools errand.It’s about understanding the level of uncertainty, or confidence and acting despite that uncertainty. It’s about making the data good enough that you’re comfortable to act. That’s the new role for Information Integration and Governance.Client Stories & Anecdotes An insurer was gathering data in Hadoop for a telematics use case. They dumped in location data based on a device in your car – which was then used to calculate a potential monthly premium discount based on your actual driving history. But it wasn’t long before the marketing department was asking other questions. What are the household driving patterns? Who was driving the car? How long did they stay at particular locations? The issue of confidence came to the front – and it exposed that they weren’t confident in the data without combining it with other sources (such as master customer data records). Their first step was to better classify and understand that data – using enterprise metadata.Catchy StatementMore data = more uncertainty – yet everyone wants even more data. How will they cope?
Key PointsThe value of IBM’s big data & analytics platform is it’s breadth. We have the broadest set of capabilities for big data of any vendor.The whole becomes greater than the sum of the parts once we start integrating those components. And we’ve done that. Our data warehouses and Hadoop systems are well integrated with our IIG capabilities. Our analytic solutions such as Cognos and SPSS are integrated with the relevant components of the big data platform. Our industry solutions teams built industry specific and horizontal solutions based upon big data and analytics. Watson is one such example.Our security, systems, storage, and cloud are optimized for this And our partners build around this platform of capabilities with highly tailored and specific solutions for their clients.At the center of our BD&A offering is our Big Data Platform and Analytics capabilities. Information Integration and Governanace, Content Management, Hadoop, Stream Computing and Data Warehousing. Everything you need to manage and govern your data.The Analytics layer contains BI, Predictive, PM, Risk Analytics, Decision Management and Content Analytics.Solutions that leverage the platform to address specific Ithat address fraud, social media analytics, information lifecycle management
Key PointsIn our experience big data is best governed in zones. There are many sources of data – which you see on the left. The era of big data is about exploiting ALL available data – streaming, at-rest structured data, video and images, you name it. So the first requirement of the big data platform is that it can handle all available data.The big data platform must be capable of ingesting information, performing real-time analytics, persisting and analyzing data in warehouses and marts, and applying governance and security throughout each of the other zones. We’ll drill into this in more detail on the next slide.The big data platform enables advanced analytics and new insights. Cognitive capabilities – to learn dynamically and discover further insights. Predictive – to harness the power of big data to predict things your competitors do not see. A whole new set of analytic capabilities enables advanced applications. Watson is a good example – of cognitive and prescriptive capabilities based on a huge volume of big data. New automated processes will emerge – ones that are better informed by data and insight. All to support decisions
Takeaway – Choice. Openness. Most comprehensive IM portfolio with broadest set of SW and HW delivery options available. In most large enterprises today, it is common to see a mix of deployment options as clients choose the best deployment strategy to meet the unique requirements of a particular part of the business. For example, a client might use System z to support their manufacturing processes, and use private cloud services to support marketing, HR and finance, and PureData System for various analytic applications. When an area of the business needs: Highest qualities of service (i.e., security, availability, performance, efficiency), System z solutions are a good fit, . Ensure critical data is always available across the enterprise, making it accessible in new ways so that actionable insights can be derived from advanced and operational analyticsProvide ultimate security, ensuring the integrity of critical data while mitigating risk and providing enhanced complianceMost flexibility (i.e., need to run a particular application on a particular set of hardware and/or middleware), multi-platform software as part of custom-built solutions provide highest levels of flexibility for delivering and managing data services. Appliance Simplicity – PureData Systems are pre-integrated & workload-optimized systems that simplify data deployment and management. The Systems include server, storage, network and data management capabilities, pre-built and tuned for specific data workloads: For OLTP workloads: PureData System for Transactions For Operational analytic workloads: PureData System for Operational Analytics For Reporting and Analytic workloads: PureData System for Analytics Cloud Agility – Cloud services offer agility and speed time to market for delivering and managing data services. IBM offers options to provide cloud services like Database as a Service in both private and public cloud environments.
Big Data Platform – April 2013DB2 with BLU AccelerationSpeed of Thought Analytics8-25x faster reporting and analytics 10x storage space savings seen during beta testNoindexes, aggregates, tuning, or SQL / schema changesBig Data PlatformPlatform advances in consumability and performanceBig SQLstandard ANSI SQL access to data in BigInsights – standard SWL access to HadoopGPFS-FPO with POSIX compliance and enhanced security2-10xfaster Streams operations using bounded lists & maps - SpeedPureData System of HadoopExplore and analyze more data with appliance simplicity8x faster deployment than custom-built solutionsFirst appliancewith built-in analytics accelerator Only Hadoop system with built-in archiving toolsClaims based on product specs, IBM lab tests or client / partner beta test experience. Detailed footnotes on distribution version.
Key PointsWe’ve increased our momentum year after yearYou can see it in the growth in clients – with thousands of big data and analytics engagements we have the breadth of that experience. That experience drives our product roadmaps, our innovation, and our services people who know how to implement big data quickly.Over 100,000 registrants in big data university – that’s an incredible accomplishment. Our goal is to raise the market’s education level on big data and analytics and this has been an tremendous success, along with our developer days and hackathons. In addition to the 500+ big data platform partners, we have 2215 partners across big data and analytics. That’s the multiplying effect of the platform – those partners augment our technology with unique solutions that add value to specific markets. Catchy StatementOur momentum has been strong – but we think it will get stronger still with today’s announcement.Confidence in big data makes organizations confident that they can start this journey – and they can start it with a partner who will make them successful.
I'm pleased and excited to announce that IBM has jumped over Informatica in the Leader quadrant of the just published2013 Gartner Data Integration Tools MQ! In this report, IBM made the greatest positive movement of any vendor evaluated as part of the MQ. Additionally, with this ranking, IBM is #1 in two of the most recent Gartner reports -- the 2013 Data Integration Tools Magic Quadrant and the December 2012 Gartner Critical Capabilities for Data Integration report. Not only did we outperform Informatica in 2012 in the Data Integration Tools Magic Quadrant, our customer references also showed notable improvement in satisfaction with their overall customer experience relative to prior years. Keep those references coming in team, so that we can see more improvement next year! As part of the MQ, Gartner lauds IBM for our breadth of functionality, installed based and diversity of use, and alignment with information infrastructure and enterprise information management (EIM) trends. Here's a summary of our strengths, according to Gartner:Breadth of functionality Reference customers routinely cite [as strengths and their reasons for selecting IBM] the sheer breadth of functionality of the vendor's product set across …data integration styles, the degree of integration between the components …via common metadata and the scalability they can achieve in the face of high-volume requirements.Installed base and diversity of use IBM's tools are often deployed as an enterprise-wide standard. The scope and scale of the implementations is often large ... The customer base shows very heavy usage in BI/analytics and data warehousing scenarios, [but] reference customers also show diversity across a range of application types (including MDM, data migration and operational application integration). During 2012, IBM [achieved] solid growth of its data integration tool business, reflecting strong execution. Alignment with information infrastructure and enterprise information management (EIM) trendsCustomers view the broad and deep metadata management functionality as critical to the early stages and ongoing value in their EIM programs, and believe this enables them to derive greater value from IBM's data integration tools. Version 9.1 of Information Server introduced deeper support for Hadoop and the new InfoSphere Data Click functionality aimed at enabling power users to perform self-service data preparation for analytics purposes.
Why is Big Data so cool? Big Data provides objective information about people’s behaviors. Not their belief or morals, not what they want their behavior to be, or what they tell the world their behavior is, but honest to goodness unedited actions: their clicks on Web sites, comments on l Media Convl media classes and so on. Scientists can tell an enormous amount about you because of this data, more than the best surveys and research focus groups, or a Dr.’s interview.Consumability is really important, think about it for a moment. In a 20 person start-up, it’s easy enough for everyone to learn Hadoop and such in a month or so and start using it. But that’s just not the case in a large enterprise.