1.
Unlocking Value in (Big) Data
Oscar Renalias, Accenture
oscar.renalias@accenture.com

2.
About the presenter
Oscar Renalias
Oscar is a Technology Architect and has been working at
Accenture in the Helsinki office for the last 5 years. He holds a
Bachelor’s Degree in Computer Science from the Universitat
Politècnica de Catalunya (UPC), in Barcelona.
Oscar currently belongs to the global organization within
Accenture responsible for pushing technology
innovation, working with selected new and emerging
technologies together with clients to generate business value.
Hadoop/Big Data is one of those areas.
Oscar.renalias@accenture.com
+358407725915
Copyright © 2012 Accenture All rights reserved.

3.
Agenda
• Top 4 things about Big Data & Analytics
• What is Big Data?
• Big Data Analytics – what is it?
• What does it contain?
• How is it integrated?
• How do we manage it?
• What next?
Copyright © 2012 Accenture All rights reserved.

4.
Top 4 things about Big Data Analytics
Resistance is futile,
you will be assimilated
Competitive advantage
It’s different
Data wants to be open
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5.
Data is growing
It’s growing. Quickly. And it’s everywhere.
Data stored in Exabytes (1018)
9000
7910
8000
7000
6000
5000
4000
3000
2000
1227
1000
130
0
2005 2010 2015
Source: IDC’s Digital Universe Study (sponsored by EMC), June 2011
Copyright © 2012 Accenture All rights reserved.

6.
New kinds of data
Structured data vs. Unstructured data growth
Complex, Unstructured
Analysis
gap
Our ability
Relational to analyze
Source: An IDC White Paper - sponsored by EMC. As the Economy Contracts, the Digital Universe Expands. May 2009.
.
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7.
Big Data Technologies
New technologies, new approaches
Source: Wordle for Credit Suisse, Does Size Matter Only?, September 2011
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8.
Where do analysts see Big Data?
Gartner’s Hype Cycle for Emerging Technologies 2011
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9.
MapReduce and Hadoop
MapReduce revolutionized how we handle large amounts of
data, Hadoop made it simple and affordable
• Originally designed and first developed in
Google as part of their efforts to more
efficiently index the web
• MapReduce splits input data into smaller
chunk that can be processed in parallel
• Scales linearly with number of nodes
• Yahoo’s implementation of MapReduce
• Open source, top-level project in the
Apache Foundation
• Designed to run on commodity software
(Linux) and hardware (consumer-grade
computers with directly attached storage)
• Large ecosystem of additional
components (both open source and
commercial)
Copyright © 2012 Accenture All rights reserved.

10.
Big Data Analytics
What is it?
Big Data Analytics is a shift in the mindset of how we
think about analytics as an internal component to the
organization
Focuses on letting data be productized in a way that
drives meaningful insights in a rapid fashion and
innovation to exploit missed opportunities in areas
previously unlooked…
… providing a path to competitive advantage
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11.
Big Data Analytics vs. traditional analytics
Where do they differ?
Technology Skills Processes &
Organization
Assumes Basic knowledge of “Siloed” data
condensed, structured, an reporting and analysis organizations
Traditional
Analytics
d feature rich datasets that tools, few specialized
can be modeled: relational resources Only specific “views” of
databases, data data visible across the
warehouses, dashboards enterprise
A stack of tools that Advanced Data is productized and
enables an organization to analytical, mathematical shared across the
Big Data
Analytics
build a framework that and statistical knowledge enterprise
allows them to extract required to develop new
useful features from a models – the data scientist Dedicated data
large dataset to further organizations with well-
understand how to model defined data management
their data. processes and ownership
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12.
Everything will be analyzed
The three Vs
Real-time
Event
In- processing, H
memory, NoS adoop +
QL, Event NoSQL
processing, E
DW
Velocity
Relational, ET
Hadoop, ETL
L
Batch
Volume Structured Unstructured
Variety Source: IDC
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13.
Big Data and Analytics in the Enterprise
Many technology choices in a rapidly changing environment.
Which one is right for you?
Distributed Non-Relational Storage and Processing
Big Data-Enabled Intelligence and Analysis
Analytics-Focused Massively Parallel Processing
(MPP) Software Platforms
Hardware Optimized MPP Data Warehouses
Distributed In-memory
Cloud
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14.
Technology
Augmenting existing analytics with Big Data technologies
Emerging
Data
Technologies
Big Data
Analytics
Traditional
Tools
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15.
SAS-Hadoop integration
An example of how traditional analytics tools are evolving to interoperate
with Hadoop
SAS/Access Interface to Hadoop
• Enable SAS user to analyze data stored in Hadoop
• Allow Hadoop data processing from SAS client software such as Data Integration Studio, Enterprise Guide and
Enterprise Miner.
• The Access Engine not only move data into and out of Hadoop, but you can also run data processing and have it
“pushed-down” into Hadoop
SAS Data Integration Studio Transformation for Hadoop
• New sets of Hadoop transformations that enable DI studio user to load and unload data from Hadoop faster than
Sqoop (Can connect to Oracle)
• Perform “ETL-like” processing with Hive and Pig.
• Hadoop specific scoring transform that enable models to be developed with Enterprise Miner to be deployed to
Hadoop via DI Studio.
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16.
The impact of Big Data Analytics on our landscapes
Hybrid landscapes, where old and new converge
Internal
apps, customer-
facing
apps, mobile Analysis tools
apps (SAS, SPSS, R,
Data Services (REST, WS) Tableau)
Relational DBs
Pig Hive
HBase
MapReduce
HDFS Enterprise
DW
ETL Real-time analytics
Time
Series Files Social Logs
Web ERP CRM
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17.
Data Science and the skill gap
Closing the loop – it’s not just about technology skills
Data science
“The sexy job in the next 10 years will
be statisticians”
– Hal Varian, Chief Economist at
Google
Data scientists are the next-generation
analytics professional, responsible for
turning the data into insight
Copyright © 2012 Accenture All rights reserved.

18.
Big Data Analytics Management
How does Big Data Analytics Management Style Differ?
In big data analytics resources generally have a hybrid cross between
Software Engineering and Advanced Statistics. This dynamic of skill sets
produces a challenge in project methodology.
Analytics Methodologies
Software Methodologies
Copyright © 2012 Accenture All rights reserved.

19.
Wrapping up
Big Data is challenging current patterns of thought
Cost-effective
Data
computing and Big Data and Analytics
“explosion”
storage
Everything can be Data everywhere: Resistance is futile
stored structured, unstru
ctured, other Are the path to competitive
advantage and create value
Cheap large scale people’s
computing power data, geolocation Compared to traditional
readily available data analytics, they’re different; adapt
or become irrelevant
Open your data
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20.
Wrapping up
How to get started
• Identify business processes that you could do
more effectively with the help of big data and
analytics
• Start with well-funded but small trials and proof-of-
concepts, evolve towards a solid roadmap
• Open up your data, transformation towards a “data
as a service” architecture
• Acquire or grow the needed technology and
analytical skills
Copyright © 2012 Accenture All rights reserved.

21.
Accenture Technology Vision
Strong advice on data for 2012
http://bit.ly/accenturetechnologyvision2012
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