big security with big data: myths and truths behind the hype surrounding big data deployments for...
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© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Big data – myths and truths Roopak Patel, Product Management
© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 2
Agenda
• History of big data
• Big data opportunities
• Big data myths
• Big data truths
• What to do?
© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
History of big data Where we’ve been…
© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 4
Accelerating innovation and time to value
695,000 status updates
98,000+ tweets
YouTube
Viber
Qzone
Amazon Web Services
GoGrid
Rackspace
LimeLight
Jive Software
salesforce.com
Xactly
Paint.NET
Business
Education
Entertainment
Games
Lifestyle
Music
Navigation
News
Photo & Video
Productivity
Reference
Social Networking
Sport
Travel
Utilities
Workbrain
SuccessFactors
Taleo
Workday
Finance
box.net
TripIt
Zynga
Zynga
Baidu
Yammer
Atlassian
Atlassian
MobilieIron SmugMug
SmugMug
Atlassian
Amazon
Amazon iHandy
PingMe
PingMe
Associatedcontent
Flickr
Snapfish
Answers.com
Tumblr.
Urban
Scribd. Pandora
MobileFrame.com
Mixi
CYworld
Renren
Yandex
Yandex
Heroku
RightScale
New Relic
AppFog
Bromium
Splunk
CloudSigma
cloudability
kaggle
nebula
Parse
ScaleXtreme
SolidFire
Zillabyte
dotCloud
BeyondCore
Mozy
Fring Toggl
MailChimp
Hootsuite
Foursquare
buzzd
Dragon Diction
SuperCam
UPS Mobile
Fed Ex Mobile
Scanner Pro
DocuSign
HP ePrint
iSchedule
Khan Academy
BrainPOP
myHomework
Cookie Doodle
Ah! Fasion Girl
PaperHost
SLI Systems
NetSuite
OpSource
Joyent
Hosting.com
Tata Communications
Datapipe
PPM
Alterian
Hyland
NetDocuments
NetReach
OpenText
Xerox
Microsoft
IntraLinks
Qvidian
Sage
SugarCRM
Volusion
Zoho
Adobe
Avid
Corel
Microsoft
Serif
Yahoo
CyberShift
Saba
Softscape
Sonar6
Ariba
Yahoo!
Quadrem
Elemica
Kinaxis
CCC
DCC
SCM ADP VirtualEdge
Cornerstone onDemand
CyberShift
Kenexa Saba
Softscape
Sonar6
Workscape
Exact Online
FinancialForce.com
Intacct NetSuite
Plex Systems
Quickbooks
eBay
MRM
Claim Processing
Payroll
Sales tracking & Marketing
Commissions Database
ERP
CRM
SCM
HCM
HCM
PLM
HP
EMC
Cost Management
Order Entry
Product Configurator
Bills of Material Engineering
Inventory
Manufacturing Projects
Quality Control
SAP
Cash Management
Accounts Receivable
Fixed Assets Costing
Billing
Time and Expense
Activity Management Training
Time & Attendance
Rostering
Service
Data Warehousing
The internet Gigabytes
Client/server Megabytes
Every 60 seconds
IBM
Unisys
Burroughs
Hitachi
NEC Bull
Fijitsu
Mainframe Kilobytes
Mobile, social, big data & the cloud
Zettabytes
Yottabytes
11million instant messages
168 million+ emails sent
1,820TB of data created
698,445 Google searches
217 new mobile web users
© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 5 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Innovative companies are changing rules of the game
All driven by the power of big data
• Develop disruptive business models
• Create better products and services
• Enhance customer experience
• Drive sustained competitive advantage
Leverage your data: make it matter
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Exponential rise
Growth of data to accelerate
Source: Multiple
0
5
10
15
20
25
30
35
40
45
50
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Data is growing at a 40% compound annual rate, reaching nearly 45 ZB by 2020
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We cover the spectrum of use-cases and growth paths for Security
Big data hype cycle
Maturity
Vis
ibil
ith
y
Technology Trigger
Peak of Inflated Expectations
Trough of Disillusionment
Slope of Enlightenment
Plateau of Productivity
Source: Gartner 2013
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Innovative analytic use cases are cutting across structured, semi-structured, and unstructured data
Big data opportunities across industries and use cases
Government Telecom Manufacturing Healthcare
• Sentiment analysis
• Social CRM/network analysis
• Churn mitigation
• Brand monitoring
• Cross and up sell
• Loyalty and promotion analysis
• Web application optimization
• Marketing campaign optimization
• Brand management
• Social media analytics
• Pricing optimization
• Internal risk assessment
• Customer behavior analysis
• Revenue assurance
• Logistics optimization
• Clickstream analysis
• Influencer analysis
• IT infrastructure analysis
• Legal discovery
• Equipment monitoring
• Enterprise search
• Drug development
• Scientific research
• Evidence based medicine
• Healthcare outcomes analysis
• Supply chain optimization
• Defect tracking
• RFID Correlation
• Warranty management
• Broadcast monitoring
• Churn prevention
• Advertising optimization
• Law enforcement
• Counter terrorism
• Traffic flow optimization
Horizontal use cases
Sources: IDC: 2012 “Worldwide Big Data Technology and Services Forecast: 2011-2015, Gartner: 2012 “Big Data Drives Rapid Changes in Infrastructure and $232 Billion in IT Spending Through 2016
Finance
• Fraud detection
• Anti-money laundering
• Risk management
Energy
• Weather forecasting
• Natural resource exploration
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One platform for structured, semi, and unstructured to profit from 100% of data
Big data needs a unified approach
Capture
Store
Manage
Analyze
Optimize
100% of data Enable me to: on
Universal log management
Structured warehouses
Unstructured
CRM, transactions, sales, marketing…
IT logs, security logs, social, tweets, JSOn’s
Audio, video, emails, sentiments, threat…
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Is it too late?
• Easy to forget that it is just the first inning
• More than three exabytes of new data are created each day
• Expansion underway for more than a decade
• Important to not big data references more than just Google, eBay, or Amazon-sized data sets
• Opportunity for a company of any size to gain advantages from big data stem from data aggregation, data exhaust, and metadata — the fundamental building blocks to tomorrow’s business analytics. Combined, these data forces present an unparalleled opportunity
• Despite how broadly big data is being discussed, it is still a very big mystery to many
• Misunderstandings around big data seem to have reached mythical proportions
© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 11
Top myths
Big data is only about massive data volume
Big data means Hadoop
Open source is the only option
Big data is new
Big data is really difficult
My RDBMS can handle it
Big data is only for social media feeds and sentiment analysis
Big data means unstructured data
Big data is for historical reporting
My current IT solutions will suffice
© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 12
Top myths – how big is big?
Stop worrying about size Even "big data" stirs up myths, with "big" being a very relative term
Should we only be concerned about this when we have more data than we can manage? What is the relative position of big data and what are some of the myths around the size issue?
Is there a certain threshold of petabytes that you have to get to? Or, if you're dealing with petabytes, is it not a problem until you get to exabytes?
If it's not a size issue, then what? It's a trend that has happened as a result of digitizing so much more of the information that we all have already and that we all produce. Machine data, sensor data, all the social media activities, and mobile devices are all contributing to the proliferation of data
© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 13
Top myths – Hadoop and open source
Greatest name recognition but not the only class
Purpose-built to process very large quantities of semi- structured data
Mostly open source, runs on low-cost server hardware
Other two options are NoSQL and Massively Parallel Processing (MPP) data stores
Hadoop includes large number of components - consider that some components can be replaced to better address a need
Focus on need for large-scale distributed data storage, analysis and retrieval tasks
Big data too varied and complex for one size-fits-all
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Top myths – continued
Big data is new Three “V’s” of big data originally posited by Gartner’s Doug Laney in a 2001
Big data is really difficult • Not if the data set is handled
correctly – with proper programming
• Focus more on the analytics for addressing a business problem
• Right people with the right tools
My RDBMS can handle it • Good for problems they were
meant to solve
• Many problems today don’t need relational capability, two-phase commits, complex transactions etc.
• Not an either-or, typically an addition
Main cause is likely to be the disconnect between the technical side and business value of big data
© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 15
Top myths – continued
Big data is only for social media feeds and sentiment analysis • Early adapters
• Opportunity for virtually every vertical
• Begins with the business problem or need
Big data means unstructured data • Imprecise and doesn’t account for the many
varying and subtle structures
• Different data types within same set
• Multi-structured better term
• Data model applied at time of analysis
Modern onslaught of data that could generate economic value if properly utilized
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Top myths – continued
Big data is for historical reporting • Depends of definition of historical
• Requirement to look at it faster and to make decisions faster
My current IT solutions will suffice • Needs a combination of technology, skilled people
and sufficient data sets
• BI and Big Data are merging
• Not just reports, dashboards and graphs
Most important aspect of big data is the analytics, not just a data storage problem that’s being solved
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Big data and BI – fitting together
Big data Data size Large data
An
aly
tic
cap
ab
ilit
y
Rea
ctiv
e P
roac
tive
Big analytics Big data analytics
BI Big data BI
Source: SAS
© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
“If you have people in the loop, it’s not real time. Most people take a second or two to react, and that’s plenty of time for a traditional transactional system to handle input and output. That doesn’t mean that developers have abandoned the quest for speed.”
Joe Hellerstein, Chancellor’s Professor of Computer Science at
UC Berkeley
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Top truths
Security and data governance are overlooked – lack of stewardship
Big data is not for real time
Big data is immature and lacks tools
Volume, velocity and variety
Skilled, experienced staff difficult to find
Big data is difficult
Frustrations– not objective, not impartial and not anonymous
No choice to opt out
© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 20
Evolution of big data
Computing timeline
Da
ta s
ize
an
d c
om
ple
xit
y
Data generation and storage Data utilization Data driven
Pre-relational (1970s and before)
Relational (1980s and 1990s)
Relational+ (2000s and beyond)
Mainframes Basic data storage
Relational databases Data-intensive applications
Structured data
Unstructured data
Multimedia
Very complex, unstructured
Complex relational
Primitive and structured
Focus areas
Source: A.T.Kearney
Exponential growth in data volume
© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 21
What to do?
Net findings • Big data is a reality and an opportunity
• Either you or your competition is taking advantage of it
• Enough momentum to add business value
Suggested steps • Pick a project that's going to address a business issue that you've been unable to address in the past
• Identify questions that need to be answered to move forward – cost reduction, new markets, customer behavior discovery, suspect activity?
Don’t start with the technology layer
IT and business owners need to work together
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HAVEn – big data platform
HAVEn
Catalog massive volumes of distributed data
Hadoop/ HDFS
Process and index all information
Autonomy IDOL
Analyze at extreme scale in real-time
Vertica
Collect and unify machine data
Enterprise Security
Powering HP Software and your apps
nApps
hp.com/haven Social media Video Audio Email Texts Mobile
Transactional data Documents IT/OT Search engine Images
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For more information
Attend these sessions
• Big Data – Tools and Tricks
• Jeremy Kelley
• Session Id 1324
Visit these demos
• Autonomy – ESM Data Leak Demo
• Booth Area
• Demo number 23434
After the event
• Contact your sales rep
• Visit the www.hp.com/haven
• Download the whitepaper at: www.hp.com/whitepaperforbigdata
Your feedback is important to us. Please take a few minutes to complete the session survey.
© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
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