Konceptuelt overblik over Big Data, Flemming Bagger, IBM

Insight to Action – Big Data – Challenge and Opportunity

Smarter Business 2012

Mobility – bring your own

device

Smarter Analytics

Social Collaboration

Smarter Security

Smarter Commerce& Marketing Automation

Insight to Action – Big Data - Challenge

and Opportunity

Smarter Cities

Smarter Product

Innovation

Smarter Process

Optimization

Smarter Infrastructure Management

Agenda10:30 IBM Big Data Platform

Flemming Bagger, Big Data Analytics Leader, Nordic

11:15 Pause

11:30 Opnå konkrete resultater med Big Data Analytics

Lauren Walker, Big Data Analytics Leader, Europe

12:15 Frokost

13:30 Succes eller fiasko? Sådan håndteres Big Data i den finansielle sektor

Keith Prince, EMEA Industry Solutions Executive, Financial Services, IBM

14:15 Pause

14:30 Dataindsamling og overvågning på tværs af sociale medier

Ulrik Bo Larsen, Founder & CEO, FALCON Social

15:10 Afrunding



11:15 Pause


Lauren Walker, Big Data Analytics Leader, Europe

12:15 Frokost



14:15 Pause



15:10 Afrunding

© 2012 IBM Corporation

Information Management

Highlight from the IBM CEO Study 2012



6,000,000 users on Twitter

pushing out 300,000 tweets per day

500,000,000 users on Twitter

pushing out 400,000,000 tweets per day

83x

1333x



In 2

005

ther

e w

ere

1.3

bil

lio

n

RF

ID t

ags

in c

ircu

lati

on

…



2+ billion

people on the Web

by end 2011

30 billion RFID tags today

(1.3B in 2005)

4.6 billion camera phones

world wide

100s of millions of GPS

enabled devices

sold annually

76 million smart meters in 2009… 200M by 2014

12+ TBs of tweet data

every day

25+ TBs oflog data

every day

? T

Bs

of

dat

a ev

ery

day

Where is big data coming from?



In Order to Realize New Opportunities, You Need to Think Beyond Traditional Sources of Data

Transactional and Application Data

Machine Data Social Data

Volume

Structured

Throughput

Velocity

Semi-structured

Ingestion

Variety

Highly unstructured

Veracity

Enterprise Content

Variety

Highly unstructured

Volume



The Characteristics of Big Data

Collectively analyzing the broadening Variety

Responding to the increasing Velocity

Cost efficiently processing the growing Volume

Establishing the Veracity of big data sources

1 in 3 business leaders don’t trust the information they use to make decisions

50x 35 ZB

20202010

30 Billion RFID sensors and counting

80% of the worlds data is unstructured



Data AVAILABLE to an organization

Data an organization can PROCESS

The Big Data Conundrum 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



Big Data is a Hot topic - Because Technology Makes it Possible to Analyze ALL Available Data

Cost effectively manage and analyzeall available data in its native form

unstructured, structured, streaming…….Internal and external

ERPCRM RFID

Website

Network Switches

Social Media

Billing



Most Client Use Cases Combine Multiple Technologies

Pre-processing

Ingest and analyze unstructured data types and convert to structured data

Combine structured and unstructured analysis

Augment data warehouse with additional external sources, such as social media

Combine high velocity and historical analysis

Analyze and react to data in motion; adjust models with deep historical analysis

Reuse structured data for exploratory analysis

Experimentation and ad-hoc analysis with structured data



Business-centric Big Data enables you to start with a critical business pain and expand the foundation for future requirements

“Big data” isn’t just a technology—it’s a business strategy for capitalizing on information resources

Getting started is crucial

Success at each entry point is accelerated by products within the Big Data platform

Build the foundation for future requirements by expanding further into the big data platform

14



1 – Unlock Big Data

Customer Need– Understand existing data sources– Expose the data within existing content management

and file systems for new uses, without copying the data to a central location

– Search and navigate big data fromfederated sources

Value Statement– Get up and running quickly and discover and retrieve

relevant big data – Use big data sources in new information-centric

applications

Get started with: IBM Vivisimo Velocity



Single view of the information

Customer-Facing Professional/Knowledge Worker

Most Common Big Data Use Case = 360-Views



2 – Analyze Raw Data Customer Need

– Ingest data as-is into Hadoop and derive insight from it– Process large volumes of diverse data within Hadoop– Combine insights with the data warehouse– Low-cost ad-hoc analysis with Hadoop to test new

hypothesis

Value Statement– Gain new insights from a variety and combination of data

sources – Overcome the prohibitively high cost of converting

unstructured data sources to a structured format – Extend the value of the data warehouse by bringing in new

types of data and driving new types of analysis– Experiment with analysis of different data combinations to

modify the analytic models in the data warehouse

Get started with: InfoSphere BigInsights



3 – Simplify your Warehouse Customer Need

– Business users are hampered by the poor performance of analytics of a general-purpose enterprise warehouse – queries take hours to run

– Enterprise data warehouse is encumbered by too much data for too many purposes

– Need to ingest huge volumes of structured data and run multiple concurrent deep analytic queries against it

– IT needs to reduce the cost of maintaining the data warehouse

Value Statement– Speed and Simplicity for deep analytics (Netezza)– 100s to 1000s users/second for operation analytics

(IBM Smart Analytics System)

Get started with: IBM Netezza

18



4 – Reduce costs with Hadoop

Customer Need– Reduce the overall cost to maintain data in the warehouse –

often its seldom used and kept ‘just in case’– Lower costs as data grows within the data warehouse – Reduce expensive infrastructure used for processing and

transformations

Value Statement– Support existing and new workloads on the most cost effective

alternative, while preserving existing access and queries – Lower storage costs– Reduce processing costs by pushing processing onto

commodity hardware and the parallel processing of Hadoop

Get started with: IBM InfoSphere BigInsights



2020

IBM Significantly Enhances Hadoop

• Scalable– New nodes can be added on the fly.

• Affordable – Massively parallel computing on

commodity servers

• Flexible – Hadoop is schema-less, and can absorb

any type of data.

• Fault Tolerant – Through MapReduce software framework

IBM InnovationIBM Innovation• Performance & reliability

– Adaptive MapReduce, Compression, Indexing, Flexible Scheduler

• Analytic Accelerators

• Productivity Accelerators– Web-based UIs– Tools to leverage existing skills– End-user visualization

• Enterprise Integration – To extend & enrich your information

supply chain.



5 – Analyze Streaming Data Customer Need

– Harness and process streamingdata sources

– Select valuable data and insights to be stored for further processing

– Quickly process and analyze perishable data, and take timely action

Value Statement– Significantly reduced processing time

and cost – process and then storewhat’s valuable

– React in real-time to capture opportunities before they expire

Customer examples– Ufone – Telco Call Detail Record (CDR) analytics for

customer churn prevention

Get started with: InfoSphere Streams

Streams ComputingStreaming Data

Sources

ACTION



Entry points are accelerated by products within the big data platform

BI / Reporting

BI / Reporting

Exploration / Visualization

FunctionalApp

IndustryApp

Predictive Analytics

Content Analytics

Analytic Applications

IBM Big Data PlatformSystems

ManagementApplication

DevelopmentVisualization & Discovery

Accelerators

Information Integration & Governance

HadoopSystem

Stream Computing

Data Warehouse

2 – Analyze Raw RataInfoSphere BigInsights

5 – Analyze Streaming DataInfoSphere Streams

1 – Unlock Big DataIBM Vivisimo

3 – Simplify your warehouseNetezza

4 – Reduce costs with HadoopInfoSphere BigInsights

22



Is Big Data imperative?



THINK

24



11:15 Pause


Lauren Walker, Big Data Analytics Leader, Europe 12:15 Frokost



14:15 Pause



15:10 Afrunding

Pause