big security with big data: myths and truths behind the hype surrounding big data deployments for...

© 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?


History of big data Where we’ve been…


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

Facebook

LinkedIn

TripIt

Pinterest

Zynga

Zynga

Baidu

Twitter

Twitter

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

Xing

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

Google

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


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


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


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


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…


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

http://mashable.com/2010/11/08/u-s-mobile-data-traffic-exabyte/

http://mashable.com/category/google/

http://mashable.com/category/ebay/

http://mashable.com/category/amazon/


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


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

http://en.wikipedia.org/wiki/Petabyte

http://en.wikipedia.org/wiki/Exabyte

http://en.wikipedia.org/wiki/Social_media


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


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



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



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


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


“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


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


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


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


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


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.

http://www.hp.com/haven

http://www.hp.com/whitepaperforbigdata

http://www.hp.com/whitepaperforbigdata


Thank you

big security with big data: myths and truths behind the hype surrounding big data deployments for...

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