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3 CSC Proprietary and Confidential August 25, 2014
Global Business Drivers Are Forcing Organizations to Operate and Compete in New Ways
Blurring industry lines with new forms
of competition
Heightening demand for increased
profitability
Higher customer expectations and
new buying behavior
Growing regulatory and risk concerns
4 CSC Proprietary and Confidential August 25, 2014
50 BILLION DEVICES INTERNET CONNECTED BY 2020
Vehicle, Asset, Person & Pet Monitoring & Controlling
Agriculture Automation
Security & Surveillance
Building Management
+ -
Embedded Mobile
Everyday Things
Smart Homes & Cities
Telemedicine & Healthcare
Looking into the Future
5 CSC Proprietary and Confidential August 25, 2014 5
Source: CIO Barometer 2013
August 25, 2014
CIOs See Big Data as One of the Most Significant Developments
Of CIOs Listed Analytics and Big Data
as one of the Most Significant
Developments for I.T.
56%
Source: CSC’s 2013 CIO Barometer Survey
6 CSC Proprietary and Confidential August 25, 2014
Technology Advances Are Enabling This Data to Be Found and Acted Upon
1970 1980 1990 2000 2010 2020 System ‘R’
RDBMS & SQL
Client-Server
Warehousing Data
Federated Databases
Internet SaaS
Analytic Appliances
Cloud
Webscale Big Data
Analytic Applications
Internet Of Things
The New Normal Everyone has the power to process terabytes and petabytes of data… at a lower cost.
7 CSC Proprietary and Confidential August 25, 2014
Across Industries, Clients See the Possibilities of Analytics…
Financial Services
Utilities Transportation Health & Life Sciences
Retail Telecomunications
Fraud detection Risk management 360° View of the
Customer
Real time route optimization based on traffic and weather Maintenance optimization
and asset tracking
360° View of the Customer Click-stream analysis Real-time promotions
Law Enforcement Real-time multimodal
surveillance Situational awareness Cyber security detection
CDR processing Churn prediction Geomapping / marketing Network monitoring
Epidemic early warning system ICU monitoring Remote healthcare
monitoring
Weather impact analysis on power generation Transmission monitoring Smart grid management
Predictive maintenance Real-time parts flow
monitoring Product configuration
planning
Manufacturing
8 CSC Proprietary and Confidential August 25, 2014
1 Setting up and operating a big data and analytics platform
2 Applying the right data science
3 Integrating insights into their business processes
4 Identifying and managing big data skills
…but they consistently struggle with the same four challenges
9 CSC Proprietary and Confidential August 25, 2014
BI to Big Data…What’s Changed: Dimensionality
D i m e n s i o n a l i t y BUSINESS INTELLIGENCE
search
display
direct
mobile
catalog
POS
Tele
TV
Web
BIG DATA
10 CSC Proprietary and Confidential August 25, 2014
BI to Big Data…What’s Changed: Growing Data Types and Sources
WEB LOGS SOCIAL MEDIA
TRANSACTIONAL DATA
SMART GRIDS
OPERATIONAL DATA DIGITAL CONTENT
R&D DATA AD IMPRESSIONS
FILES
Internal, structured data
New types and sources
BUSINESS INTELLIGENCE BIG DATA
11 CSC Proprietary and Confidential August 25, 2014
Big Data includes BI, which includes Data Visualizations
Big Data is essentially BI on steroids.
In addition, new techniques for data analysis are emerging such as
unsupervised modeling
BI has evolved from static reports with a single data visualization to an interactive, dynamic data discovery tools that incorporate many visualizations.
Data Viz Google Pie Chart Demo
Business Intelligence SAS Insurance Demo
Big Data Customer Sentiment
Analysis
12 CSC Proprietary and Confidential August 25, 2014
Data Warehousing to Big Data…What’s Changed
Schema on Write (Schema required)
Clean Trusted Structured
“As Is” Unknown
Unstructured
Schema on Read (Schema-less)
More data, more types DATA WAREHOUSING BIG DATA
13 CSC Proprietary and Confidential August 25, 2014
Google Evolved Search by Taking More Data into Account
Google’s page rank didn’t revolutionize the search engine business because of the algorithm. 1. Google recognized that
hyperlinks were an important measure of popularity (a link to a webpage counts as a vote for it)
2. The use of anchortext (the text of the hyperlinks) in a web index, giving it a weight close to the page title
More Data Usually Beats Better Algorithm. Retrieved from http://anand.typepad.com/datawocky/2008/03/more-data-usual.html
B 38.4%
C 34.3%
F 3.9%
D 3.9%
E 8.1%
1.6%
A 3.3%
1.6% 1.6%
1.6%
1.6%
14 CSC Proprietary and Confidential August 25, 2014
Google Made AdWords More Competitive by Leveraging More Data
Overture had previously proved that the model of having advertisers bid for keywords could work. Overture ranked advertisers for a given keyword based purely on their bids.
Google added some additional data: the clickthrough rate (CTR) on each advertiser's ad. Thus, to a first approximation, Google ranks advertisers by the product of their bid and their CTR (this was true in the first version of AdWords; they now use more considerations).
More Data Usually Beats Better Algorithm. Retrieved from http://anand.typepad.com/datawocky/2008/03/more-data-usual.html
15 CSC Proprietary and Confidential August 25, 2014
Netflix Found More Data Drove Better Recommendations
More Data Usually Beats Better Algorithm. Retrieved from http://anand.typepad.com/datawocky/2008/03/more-data-usual.html
The Netflix challenge exists for competitors to try to create a better recommendation algorithm for Netflix’s viewers than the one that exists today. Stanford professor Anand Rajaraman, found that a complex algorithm with Netflix data alone is regularly beat by a simple algorithm that incorporates additional data sources such as IMDB. The additional data source increases the dimensionally of the movie to help pinpoint what users are attracted to.
DATABASES
DATA INTEGRATION
VISUALIZATION / BUSINESS
INTELLIGENCE
ADVANCED DATA
ANALYTICS DATA
STREAMING
Requires Embracing Heterogeneity… The Way Forward
17 CSC Proprietary and Confidential August 25, 2014
InterSystems Progress
Objectivity Versant
…Recognizing the Cooperation Between Relational and Non-Relational Databases
RELATIONAL NON-RELATIONAL ANALYTIC
OPERATIONAL
Hadoop Horton
Cloudera MapR
Hadapt Impala
Shark/Spark
Teradata Aster
EMC
Greenplum
IBM Netezza
HP Vertica
Sybase IQ
Infobright ParAccel Calpont
VectorWise
Storm Oracle IBM DB2 JustOneDB
SQLSrvr
MarkLogic McObject
KEY VALUE
DOCUMENT
BIG TABLES
‘DATA AS A SERVICE’
GRAPH
Lotus Notes
CouchDB MongoDB RavenDB
Riak Redis
Membrain Voldemort
BerkeleyDB HyperTable
Hbase Accumulo
Titan FlockDB
InfiniteGraph Neo4j
AllegroGraph
Casandra
Couchbase Cloudant App Engine SimpleDB
Amazon RDS SQL Azure
Database.com
SchoonerSQL Tokutek
Continuent Translattice
ScaleBase CodeFutures
VoltDB
HandlerSocket Akiban
MySQL Cluster Clustrix Drizzle
GenieDB ScalArc
NimbusDB
Xeround FathomDB
MySQL Ingress PostgreSQL
EnterpriseDB Sybase ASE NoSQL NewSQL
18 CSC Proprietary and Confidential August 25, 2014
…Exploiting Batch
Cloud::Hadoop Cloudera MapR Hortonworks Spark Altiscale Metascale Continuuity Motor Data HP Bamboo Zdata Zettaset
19 CSC Proprietary and Confidential August 25, 2014
Cloud::Queries Hbase Impala Shark MongoDB (10gen/mongoHQ) Cassandra (Datastax) Aerospike ElasticSearch Riak Redis CouchDB Gridgain Netezza ParAccel Greenplum Vertica Hadapt
…Enabling Ad-hoc / Fast Query
20 CSC Proprietary and Confidential August 25, 2014
Cloud::Streams Storm S4 AccelOps HStreaming Streambase SQLStream
InfoStreams OpenCQ NiagaraCQ TelegraphCQ RapideGemfire DistCEP CEDR
Cayuga Raced Sase+ Amit TESLA/T-Rex Progress Apama Tibco Business
Events Esper Aleri/Coral8 Oracle CEP
…And Integrating Stream Processing
PROCESSORS
STREAM 1
STREAM 2
21 CSC Proprietary and Confidential August 25, 2014
Big Data Can Be Done in the Cloud…
Analytic Application
Platforms
New Infrastructure
Platforms
Analytic Vertical SaaS Applications
Incumbents Emerging Players
22 CSC Proprietary and Confidential August 25, 2014
…Or On-Prem
ETL
Batch
Ad-Hoc
Real-Time
BI
Analytics
Platform
DataStage
Big Insights
PureData
Streams
Cognos
SPSS
System X
Informatica Ab Initio
Hortonworks
AsterData
N/A
Tableau+
SAS SW
Big Data Appliance
Oracle Data Integration
Cloudera
Oracle NoSQL Times Ten
Oracle EP
OBIEE
Oracle R
Exalytics, Exadata
Data Integrator
Hortonworks
Hana
Sybase EP
BOBJ
SAP Predictive
IBM/Dell
Informatica
Pivotal HD
Greenplum
Gemfire
Partners
Cetas, SAS, Alpine
Greenplum DCA,
Isilon
23 CSC Proprietary and Confidential August 25, 2014
INFRASTRUCTURE SERVICES (Bare Metal, AWS IaaS,
CSC Cloud, Open Stack, Vsphere)
REPEATABLE BD&A CORE PLATFORM
FUNCTIONAL INTELLIGENCE & ANALYTICS Customer | Finance | Marketing | Operations | Supply Chain
INDUSTRY SOLUTIONS
OUTCOMES
Retained insurance customers
Refined diagnoses
in clinical care
Efficient operations
in manufacturing
Underwriting risk based on
climate predictions
CSC offers Business, Data Science, and Platform Consulting Services around packaged offerings, best-of-breed technologies, and a repeatable platform that combines traditional and open source
Data Science Consulting
Big Data Business Consulting
Big Data Platform Consulting
24 CSC Proprietary and Confidential August 25, 2014
…and brings together proprietary and open source technologies to solve the most unique use cases
+ +
But, what happens when parts go out of
tolerance? What is causing these defaults?
How do I manage this
large analytics platform?
Store structured, manufacturing data such
as data on quality constraints
Analyze measurement system data and manufacturing data together with
unstructured machine data, such as temperatures, to identify correlations
Stand up a managed big data platform and start finding insights in
less than 30 days
A USE CASE IN MANUFACTURING
25 CSC Proprietary and Confidential August 25, 2014
CSC’s Infochimps Platform Enables Insights from your Data in Less than 30 Days
Cloud::Streams
Cloud::Hadoop
Cloud::Queries
Wukong
Cloud API
Command Center
« PR
OC
ESS DATA Q
UERY D
ATA »
COLLECT DATA
HTTP
Logs
Data Partners
Batch Upload
Custom Connectors
INFOCHIMPS CLOUD
26 CSC Proprietary and Confidential August 25, 2014
EMBRACE HETEROGENEITY. DIFFERENT CLOUDS SUPPORT DIFFERENT WORKLOADS.
CSC’s Cloud Offerings Give Options Across Workloads, Infrastructures, and Deployment
27 CSC Proprietary and Confidential August 25, 2014
CSC’s Modular Approach Ensures a Focus on Time-to-Value
SHAPE TRANSFORM MANAGE / AS A SERVICE
Transform Decision Making
Enable Business Strategy
Achieve Business Process Optimization
28 CSC Proprietary and Confidential August 25, 2014
This Approach Is Supported By Proven, Pre-Packaged Accelerators
Maturity Model BenchmarkingCurrent State
Assessment
Competitive Benchmarking
Future State Design
Business Case Phase 1
Phase 2
Workshops
Implementation Plan
No real integration capability available. Data exists in
Operational silos
Integration, either/or at batch and real-time level exists at the sub-enterprise level. This
may be Business Unit or Functional Area. No integration capability exists across the
Enterprise
Limited, targeted integration capability exists across the Enterprise, some at real-time level.
Strategies and plans exist to expand that to completion
Full integration at both real-time and historical timeframes exist across the
Enterprise
Full Realization
Partial Integration
Building Blocks to Success
At the Starting Gate
DISTINCTIVE
ADVANCED
FOUNDATIONAL
BASIC
• CSC Big Data maturity model • Project profiles and prioritization • Sample KPIs • Business case • Big Data roadmap • Sample implementation plans
• Conceptual solution architecture • Template data strategy • Logical technical model • Reference architectures
• Pre-built data extract accelerators • Pre-built models • Pre-built visualizations/dashboards • Sample governance models
29 CSC Proprietary and Confidential August 25, 2014
Through Experience with Many Projects, We’ve Learned What It Takes to Succeed…
Operationalize the resulting
insights
Start from the business need/
opportunity
Imagine the potential from disparate data
sources
Contribute CSC’s industry understanding
to create advanced analytic models
Avoid costly mistakes with attention to architecture
Present the output in an easily assimilated and visualized form
30 CSC Proprietary and Confidential August 25, 2014
…And Created a Short List of Do’s and Don’ts
DO Start simple (K.I.S.S. principle) Initial big data projects should be done with the minimal amount of plumbing and a well scope problem. Think like a marine…land and expand quickly!
DON’T Translate a data warehouse into a Big Data system table for table NoSQL approaches to data mining are different for a reason. Don’t try to just translate something you already have.
DO Tweak Today’s Big Data systems take a lot of care and feeding to continue in a stable manner. In addition, most Big Data models need to be retrained periodically.
DON’T Confuse a domain expert for a data scientist Data science is a team approach that takes development, mathematics and domain expertise.
31 CSC Proprietary and Confidential August 25, 2014
Purpose-Fit Industrialized Solutions Leveraging CSC’s deep industry and business process knowledge Full Life-Cycle Approach Using our uniquely modular, Shape-Transform-Manage, full life-cycle approach keeps focus on business outcomes
Expertise in Turning Data into Recommendations Combining the right data sets and applying the right analytic techniques to unveil new insights Leading Data Catalog 15,000+ data sources
Our Clients Choose CSC Because of Our Unique Strengths in Big Data and Analytics
TECHNOLOGY
DATA SOLUTIONS
Technology Leadership Working with Hadoop since its creation
Faster Time to Value Standing up a big data platform in less than 30 days for rapid business value
Technology Independence Delivering best-of-breed solutions, from open source to global partners, optimized for your need and budget
32 CSC Proprietary and Confidential August 25, 2014
How Can You Get Started?
Ask your CSC team how a Shape service can help you determine how Big Data can make a difference for
your organization
ASK FOR A DEMO AT www.csc.com/bigdata
32 August 25, 2014 © 2013 Computer Sciences Corporation. All Rights Reserved.
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