unlocking value in your (big) data

Unlocking Value in (Big) Data

Oscar Renalias, [email protected]

Copyright © 2012 Accenture All rights reserved.

About the presenter

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.

[email protected]

+358407725915

Oscar Renalias

mailto:[email protected]


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.Copyright © 2012 Accenture All rights reserved.

Resistance is futile,you will be assimilated

Competitive advantage

It’s different

Data wants to be open

Top 4 things about Big Data Analytics


Data is growing

It’s growing. Quickly. And it’s everywhere.

Source: IDC’s Digital Universe Study (sponsored by EMC), June 2011

2005 2010 20150

1000

2000

3000

4000

5000

6000

7000

8000

9000

130

1227

7910

Data stored in Exabytes (1018)


Source: An IDC White Paper - sponsored by EMC. As the Economy Contracts, the Digital Universe Expands. May 2009.

.

Complex, Unstructured

Relational

New kinds of data

Structured data vs. Unstructured data growth

Our ability to analyze

Analysis gap


Big Data Technologies

New technologies, new approaches

Source: Wordle for Credit Suisse, Does Size Matter Only?, September 2011


Where do analysts see Big Data?

Gartner’s Hype Cycle for Emerging Technologies 2011


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)


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

Big Data Analytics

What is it?


Big Data Analytics vs. traditional analytics

Where do they differ?

Technology Skills Processes & Organization

Big

Dat

a A

naly

tics

Tra

ditio

nal

Ana

lytic

s

Assumes condensed, structured, and feature rich datasets that can be modeled: relational databases, data warehouses, dashboards

Basic knowledge of reporting and analysis tools, few specialized resources

“Siloed” data organizations

Only specific “views” of data visible across the enterprise

A stack of tools that enables an organization to build a framework that allows them to extract useful features from a large dataset to further understand how to model their data.

Advanced analytical, mathematical and statistical knowledge required to develop new models – the data scientist

Data is productized and shared across the enterprise

Dedicated data organizations with well-defined data management processes and ownership


Everything will be analyzed

The three Vs

Structured Unstructured

Batch

Real-time

Velocity

Variety Source: IDC

Hadoop, ETLRelational,

ETL

In-memory, NoSQL, Event

processing, EDW

Event processing, Hadoop + NoSQL

Volume


Analytics-Focused Massively Parallel Processing (MPP) Software Platforms

Distributed In-memory

Big Data and Analytics in the Enterprise

Many technology choices in a rapidly changing environment. Which one is right for you?

Cloud

Hardware Optimized MPP Data Warehouses

Distributed Non-Relational Storage and Processing

Big Data-Enabled Intelligence and Analysis


Technology

Augmenting existing analytics with Big Data technologies

Emerging Data

Technologies

Traditional Tools

Big Data Analytic

s


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.

SAS-Hadoop integration

An example of how traditional analytics tools are evolving to interoperate with Hadoop


The impact of Big Data Analytics on our landscapes

Hybrid landscapes, where old and new converge

ERP CRMWebLogsTime

Series Files Social

Relational DBs

EnterpriseDW

Real-time analytics

HDFS

HBaseMapReduce

Hive

Data Services (REST, WS)

Pig

ETL

Internal apps, customer-facing apps, mobile apps Analysis tools

(SAS, SPSS, R, Tableau)


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

Data Science and the skill gap

Closing the loop – it’s not just about technology skills


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.

Big Data Analytics Management

How does Big Data Analytics Management Style Differ?

Strategy

Release

Iteration

Daily

Continuous

Requirements

Design

Implement

Verify

Maintain

Software MethodologiesAnalytics Methodologies


Wrapping up

Big Data is challenging current patterns of thought

Cost-effective computing and

storage

Data “explosion”

Everything can be stored

Cheap large scale computing power readily available

Data everywhere: structured, unstructured, other people’s data, geolocation data

Big Data and Analytics

Resistance is futile

Are the path to competitive advantage and create value

Compared to traditional analytics, they’re different; adapt or become irrelevant

Open your data


• 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

Wrapping up

How to get started


Accenture Technology Vision

http://bit.ly/accenturetechnologyvision2012

Strong advice on data for 2012

unlocking value in your (big) data

Technology

data scientist

hadoopbig data

big data analyticswhat

big data analyticsresistance

hadoop data processing

data large dataset

data integration studio

defined data management