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1.
BIG
DATA IS CHANGING THE WORLD © Copyright 2010 EMC Corporation. All rights reserved. 1
2.
IN THIS DECADE
THE DIGITAL UNIVERSE WILL GROW 44X FROM 0.9 ZETTABYTES TO 35.2 ZETTABYTES Source : 2010 IDC Digital Universe Study © Copyright 2010 EMC Corporation. All rights reserved. 2
3.
90% OF THE
DIGITAL UNIVERSE IS UNSTRUCTURED Source: 2011 IDC Digital Universe Study © Copyright 2010 EMC Corporation. All rights reserved. 3
4.
Big Data Has
Arrived Electronic Payments Video Rendering Video Mobile Sensors Social Media Surveillance Medical Imaging Gene Sequencing Geophysical Smart Grids Exploration © Copyright 2010 EMC Corporation. All rights reserved. 4
5.
Deliver Better Healthcare
With Big Data Billion Dollar Specialty Care Service Provider Legacy System & New System & International Traditional Data Results Big Data Quality Of Patient Care Treatment Pathways On Treatment All The Data Pathways On Individual Summary Data Patient History Social & Economic Factors © Copyright 2010 EMC Corporation. All rights reserved. 5
6.
Increase Profit Margins
With Big Data Retail Banking Firm Aligns Offers To Customers Legacy System & New System & Traditional Data Big Data Profit-Based Customer Profit Recommendations Identify Agent “At-Risk” “Best Guess” Customers User Based Recommendations © Copyright 2010 EMC Corporation. All rights reserved. 6
7.
Classifying and segmenting
Big Data • Rich content stores—original intellectual property or value-added – Media, VOD, content creation, special effects, satellite imagery, GIS data • Generated from workflow—must be managed/processed quickly & cheaply – Manufacturing, simulation, electronic design • Develop new intellectual property based on big data – Pharmaceutical companies doing customised drug development • Companies, public sector, utilities mining data for business advantage • Some mine consumer data—higher-volume and potentially higher-value © Copyright 2010 EMC Corporation. All rights reserved. 7
8.
Big Data is
File & Unstructured Data 90 80 70 60 EXABYTES 50 40 30 20 10 0 2009 2010 2011 2012 2013 2014 File Based: 60.7% CAGR Block Based: 21.8% CAGR By 2012, 80% of all storage capacity sold will be for file-based data Source: IDC © Copyright 2010 EMC Corporation. All rights reserved. 8
9.
Why is Big
Data appearing now? Source: IDC © Copyright 2010 EMC Corporation. All rights reserved. 9
10.
Gartner’s 3 V’s
of Big Data © Copyright 2010 EMC Corporation. All rights reserved. 10
11.
“The Internet of
Things” • Massive explosion of smart devices, all sending, receiving, storing data – handhelds, tablets, cameras – Human-oriented devices • Non-human-oriented devices – sensors, embedded CPUs • Social networking messages & data grow exponentially – Twitter feeds, Facebook updates, LinkedIn messages • Increasingly, business is conducted digitally – or digitized • Big Data is global – any source to any target © Copyright 2010 EMC Corporation. All rights reserved. 11
12.
Source:
GoGlobe © Copyright 2010 EMC Corporation. All rights reserved. 12
13.
Companies want to
store big data—Why? • Google – Originally thought of as “search engine” – Now: Storing the Internet, storing every search query • Facebook, Twitter – Just social media? – Storing every message you send, monitoring every market trend • Amazon – your every purchase, forever • Carriers – Storing location-based data on everyone © Copyright 2010 EMC Corporation. All rights reserved. 13
14.
Social Networking Analysis
Courtesy of NSF Workshop on Social Modeling © Copyright 2010 EMC Corporation. All rights reserved. 14
15.
The race is
on • Big Data leads to the Optimised Organisation • Takes a long time to build a functioning data warehouse, analytics tools, connect to business • Many companies have a head start • Every CIO needs to consider Big Data in their strategy to stay ahead – How to manage, how to leverage © Copyright 2010 EMC Corporation. All rights reserved. 15
16.
A little retailer
I once knew • Why can Amazon beat everyone on price? • Purchase information used to adjust supply chain • Shipping and logistics adjusted according to conditions on the ground and supply chain • Other customers’ information used to provide recommendations, improve experience • Not just Amazon: Tesco, Carrefour, Metro, etc all taking advantage © Copyright 2010 EMC Corporation. All rights reserved. 16
17.
How do we
make decisions? • Good data is hard to get—so often on no data at all • Often on information from peers, colleagues, reports, or because it’s always been done that way • Many companies fail because they fail to detect shifts in consumer demand • Internet has made customers more segmented, and causes customer choice to change faster © Copyright 2010 EMC Corporation. All rights reserved. 17
18.
Moving to a
Data-Driven Model • Managing with the facts • Making a science out of data! • Experimental model—different than BI • Moving from “gut feel” to rational, scentific decisions © Copyright 2010 EMC Corporation. All rights reserved. 18
19.
Big-Data-based Decisions
• Unlock value by making information transparent and useable at higher frequency • More accurate information (e.g. inventories, trends) • Tailor products more precisely • Sophisticated analytics makes for better decisions • Better products (via web feedback, sensors, etc) Source: McKinsey © Copyright 2010 EMC Corporation. All rights reserved. 19
20.
What holds back
big data? • Not ICT—compute & storage getting bigger, cheaper, easier • Not the quantity of data (see slide 1) • Not the value—large-scale Big Data projects generally have great ROI • Real problems are organisational change and talent acquisition © Copyright 2010 EMC Corporation. All rights reserved. 20
21.
© Copyright 2010
EMC Corporation. All rights reserved. 21
22.
How are people
doing it? • Enterprises ingesting > 1PB data per day within 5 yrs • Big data is often largely unstructured • Hadoop is an application written to analyze big data – open source, Java-based • Big data can mean billions to trillions of files – Each file can be gigabytes to terabytes in size • Directed graph analysis, Collaborative Filtering, A/B testing, Associative Rule Learning, Classification, Natural Language processing, Data Mining, Pattern Matching, Sentiment Analysis, Comparative Effectiveness, Clinical Decision Support are examples of big data techniques • This means petabytes to exabytes of data © Copyright 2010 EMC Corporation. All rights reserved. 22
23.
How do you
manage and design for Big Data? • Scale and parallelism are the keys – Big data is far too big to process sequentially – Too much coming in too quickly – Example: Banks seeking to process market data more quickly, reducing decision making time from days to minutes • Answer: Scale-out storage and scale-out processing © Copyright 2010 EMC Corporation. All rights reserved. 23
24.
Cramming big data
onto traditional models Server Scalability Network Performance Management Availability Cost Storage © Copyright 2010 EMC Corporation. All rights reserved. 24
25.
A different idea
– scale-out Server Scalability Network Performance Management Availability Cost Storage © Copyright 2010 EMC Corporation. All rights reserved. 25
26.
Enterprise Hadoop: Greenplum
& Isilon • Easier and more reliable – Packaged Hadoop distribution with Isilon storage • Purpose-built Hadoop infrastructure – Faster, less risk • Sharing expertise to address the talent gap – Architecture, data science, and roadmap services • Proven at scale with worldwide support – 24x7 one call Hadoop support from EMC – Key component of Greenplum UAP – Unstructured data processing © Copyright 2010 EMC Corporation. All rights reserved. 26
27.
Increasing Demand for
Advanced Analytics • Complex – Deep, rich analysis of big data sets – Ad hoc, interactive analysis, not structured reports • Timely – On-going, frequent analysis (e.g. daily, weekly) – Insights delivered in minutes/seconds • Actionable – Forward looking, predictive insight – Create new business value © Copyright 2010 EMC Corporation. All rights reserved. 27
28.
EMC Greenplum: Purpose-built
for Big Data • EMC Greenplum is a shared nothing, massively parallel processing (MPP) data warehouse system • Core principle of data computing is to move the processing dramatically closer to the data and to the people Fast Data Loading Extreme Performance Unified & Elastic Scalability Data Access © Copyright 2010 EMC Corporation. All rights reserved. 28
29.
MPP Shared-Nothing Architecture
Greenplum’s Massively MapReduce Parallel Processing (MPP) Database has extreme scalability on general purpose Master systems Servers ... ... Query planning Automatic parallelization and dispatch – Load and query like any Network database Interconnect Scan and process in parallel Segment ... – Extremely scalable and I/O Servers ... optimized Storage and query ... ... ... ... ... ... ... ... ... ... Linear scalability by adding processing nodes External – Each adds storage, query Sources performance and loading MPP loading, streaming, etc. performance © Copyright 2011 EMC Corporation. All rights reserved. EMC Confidential – NDA Required 29
30.
EMC Hadoop.
Open Source. Fully Supported By EMC. © Copyright 2010 EMC Corporation. All rights reserved. 30
31.
The EMC Big
Data “Stack” 4 Collaborative Act Documentum xCP ? 3 Real Time Analyze Greenplum, Hadoop 2 Structured & Unstructured Store 1 Petabyte Scale Isilon and Atmos © Copyright 2010 EMC Corporation. All rights reserved. 31
32.
THANK
YOU HAVE A GREAT CONFERENCE! © Copyright 2010 EMC Corporation. All rights reserved. 32
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