Company Confidential - For Internal Use OnlyCopyright © 2010, SAS Institute Inc. All rights reserved.

Data AnalyticsChallenges in Data Growth and Information DistillationDeepak RamanathanHead - Technology PracticeAsia Pacific

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Anticipate & Manage Change

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Enterprise Information: Doubling at an Unprecedented Rate

Falling storage costs continue to drive the appetite for higher data volumes and data stores, including transactional systems; office automation and collaboration; LAN file systems; databases and warehouses/marts; e-mail, instant messaging, wikis, blogs, voice; digital images, video, RFID, etc.

20052005 1,1001,100 daysdays

20072007 1111 monthsmonths

20102010 1111 hourshours

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Company Confidential - For Internal Use OnlyCopyright © 2010, SAS Institute Inc. All rights reserved.

The Data Explosion

In 2009, despite the global recession, digital data grew by 69% to 800,000 petabytes

In 2010, the Digital Universe grew almost as fast to 1.2 zettabytes

By 2020, the data will be 44 times as big as in 2009

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Key Challenges for Data Management

Growth in embedded systems for Smart Grid RFID Telemetry data from automobiles

How much data do you keep?

What data do you keep?

How quickly can you access data?

How do you distill information from this data?

How do you analyze this data?

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What makes up this Data Explosion? Structured data Relational

databases, structured data files, system/application data and logs that reside in a data store, defined by a catalog (table definitions)/data model accessible via SQL or Object definitions. This data has a characteristic of being contextualized by the heading (field name) and possibly defined in relation to other "fields.” This data is also capable of being processed in a simple manner, summed or aggregated, etc.

Semistructured Data houses structure with freeform elements (e.g., e-mails) and has structure and context to specific elements in the header, but is freeform text in the body..

Unstructured data Most of the information that

resides in organizations is unstructured in nature – images, content of Web

documents, standard documents, audio, video

and correspondence.This type of information is

typically difficult to find effectively if nothing has been done to make the

data accessible, such as putting it into a content

management system and tagging it with metadata.

70%

25%

5%

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Après Structured Data – The Deluge !

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Top Predictions for Business Analytics*

Social

Self service

Pervasive

Scalable

Cloud, and

Real-time

James Kobielus, Forrester Research

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Social

"We have the ability to run an open, transparent, participatory and collaborative government.” 1

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Text AnalyticsImproving Staff ProductivityImproving Staff Productivity

Increasing Operational EfficiencyIncreasing Operational Efficiency

Collaboration & ExchangeCollaboration & Exchange

Leveraging the Power Leveraging the Power of Technologyof Technology

Caring for the EnvironmentCaring for the Environment

Delivering Social CareDelivering Social Care

Improving Citizen & Business Improving Citizen & Business Service DeliveryService Delivery

Improving Compliance Improving Compliance & Accountability& Accountability

Raising Standards in EducationRaising Standards in Education

Enhancing Public Services with insights from Gov 2.0

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The InDatabase Approach – Scale

SASScoring

Data

EDW

Traditional Capabilities

Data

Teradata EDW

SAS In-DatabaseCapabilities

SAS

Analytic Modeling

SAS

Analytic Modeling

SASScoring

Data Modeling Preparation

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Pervasive Computing

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Pervasive Analytics

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Cloud is here..

Grid Manager

Distributed Enterprise Scheduling

Workload Balancing

Parallelized Workload Balancing

Distribute parallelized workloads to a shared pool of resources.

Distribute workloads to a shared pool of resources.

Distribute jobs within workflows to a shared pool of resources.

Optimize the Efficiency and Utilization of Computing Resources

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Key to Building Analytical Solutions

Collaborating with interdisciplinary teams that combine Industry experience Specialized software development skills for data management,

analytical computing, reporting Expertise in in statistics, data mining, forecasting, optimization

Understanding complex data

Synthesizing effective analytical algorithms

Building models that work with large volumes of data

Evaluating the performance of models

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SAS Innovation Continues

Solving challenging problems for our customers using the analytical tools directly

Working closely with the analytical solutions in vertical domains which provide increasingly challenging problems in terms of scale and complexity

…Responding to multiple challenges

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Thank you

Enjoy the conference