Entry Points – How to Get Rolling with Big Data Analytics

The Briefing Room

Entry Points – How to Get Rolling with Big Data Analytics

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Welcome

Host: Eric Kavanagh

[email protected]


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!   Reveal the essential characteristics of enterprise software, good and bad

!   Provide a forum for detailed analysis of today’s innovative technologies

!   Give vendors a chance to explain their product to savvy analysts

!   Allow audience members to pose serious questions... and get answers!

Mission


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Topics

This Month: ANALYTICS

October: DATA PROCESSING

November: DATA DISCOVERY & VISUALIZATION


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Analytics


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Analyst: Robin Bloor

Robin Bloor is Chief Analyst at The Bloor Group

[email protected]


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IBM

!   IBM offers an enterprise class big data platform with capabilities such as Hadoop-based analytics, stream computing and data warehousing

!   The platform includes InfoSphere BigInsights, InfoSphere Streams and InfoSphere Data Explorer

!   The portfolio of products combines traditional technologies that are ideal for structured tasks with new technologies that address ad hoc data exploration, discovery and unstructured analysis


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Guests: Rick Clements & Vijay Ramaiah Rick Clements is Program Director, Worldwide Big Data Product Marketing for IBM. In his current role, he is responsible for global product marketing for the IBM big data platform including positioning and messaging for InfoSphere BigInsights, InfoSphere Streams and InfoSphere Data Explorer. Mr. Clements has 14 years experience in the software industry and deep knowledge and understanding in the areas of enterprise application integration, business to business integration, business process management, service oriented architecture, web services, business activity monitoring, master data management and big data technologies.

Vijay Ramaiah is Worldwide Product Manager, IBM Big Data Portfolio for IBM. He is responsible for driving portfolio strategy for the IBM big data software platform and accelerators, and leading cross-organizational strategy and execution plans. Mr. Ramaiah also manages the portfolio of Big Data Accelerators, which includes Social Data Analytics, Machine Data Analytics and Telco Call Data Analytics. He has 23 years of software business, market and technology experience.

© 2013 IBM Corporation

Unlocking New Insights and Opportunities with Big Data

Richard Clements, Program Director, Big Data Product Marketing

10 © 2013 IBM Corporation

Big Data Exploration Find, visualize, understand all big data to improve decision making

Enhanced 360o View of the Customer Extend existing customer views by incorporating additional internal and external information sources

Operations Analysis Analyze a variety of machine data for improved business results

Data Warehouse Augmentation Integrate big data and data warehouse capabilities to increase operational efficiency

Security/Intelligence Extension Lower risk, detect fraud and monitor cyber security in real-time

Big Data – the 5 Key Use Cases


Enhanced 360º View of the Customer: Needs

Requirements Create a connected picture of the customer

Mine all existing and new sources of

information Analyze social media to uncover sentiment about products

Add value by optimizing every client interaction

Industry Examples •  Smart meter analysis •  Telco data location monetization •  Retail marketing optimization •  Travel and Transport customer

analytics and loyalty marketing •  Financial Services Next Best

Action and customer retention •  Automotive warranty claims •  …

Optimize every customer interaction by knowing everything about them


Professional Life Employers, professional groups, certifications …

Legal/Financial Life Property, credit rating, vehicles, …

Contact Information Name, address, employer, marital…

Business Context Account number, customer type, purchase history, …

Leisure Hobbies, interests …

Social Media Social network, affiliations, network …

A customer is a puzzle made up of many pieces

Every interaction requires someone to piece together parts of the puzzle

Information about your customers is dispersed, forcing your employees to extract it piece-by-piece

13 © 2013 IBM Corporation © 2013 IBM Corporation

Analy&cs based on accurate data and contextual intelligence

Customer info from MDM

Recent conversa&ons from mul&ple sources: e.g., CRM, e-‐mail, etc.


Exploit technology advances to deliver more value from an existing data warehouse investment while reducing cost!

Data Warehouse Augmentation: Needs

Requirements Add new sources to existing DW investments Optimize storage & provide query-able archive Rationalize for greater simplicity and lower cost Enable complex analytical applications with faster queries Improve DW performance by determining which data to put into it Scale predictive analytics and business intelligence Leverage variety of data for deep analysis

Examples • Pre-Processing Hub • Queryable Data Store • Exploratory Analysis • Operational Reporting • Real-time Scoring • Segmentation and Modeling


3 Ways to Augment Your Data Warehouse

Pre-Processing Hub Queryable Data Store Exploratory Analysis 1 2 3


How some organizations are using this today…

To glean more information about customers at the individual level by analyzing social media with operational data

Discover and visualize fraud patterns, account closings, activity patterns from data that was once unable to be leveraged

Increase the spectrum for data analysis from 30 days to multiple years – allowing for more accurate decision making

Reducing costs and increasing the quality of service by offloading colder data onto Hadoop with commodity hardware


Explore and mine big data to find what is interesting and relevant to the business for better decision making!

Big Data Exploration: Needs

Requirements Explore new data sources for potential value

Mine for what is relevant for a business imperative

Assess the business value of unstructured content

Uncover patterns with visualization and algorithms

Prevent exposure of sensitive information

Industry Examples •  Customer service knowledge

portal •  Insurance catastrophe modeling •  Automotive features and pricing

optimization •  Chemicals and Petroleum

conditioned base maintenance •  Life Sciences drug effectiveness

…


Enhance traditional security solutions to predict, prevent and take action against crime by analyzing all types and sources of big data!

Security Intelligence Extension: Needs

Requirements Industry Examples •  Government threat and

crime prediction and prevention

•  Insurance claims fraud •  Utilities are terror targets,

disrupt power and water •  Retailers vulnerable to

internal and external threats due to PCI data

Enhanced Intelligence and Surveillance Insight

Real-time Cyber Attack Prediction and Mitigation

Analyze network traffic to: •  Discover new threats sooner •  Detect known complex threats •  Take action in real-time

Analyze telco and social data to: •  Gather criminal evidence •  Prevent criminal activities •  Proactively apprehend criminals

Crime Prediction and Protection

Analyze data-in-motion and at rest to: •  Find associations •  Uncover patterns and facts •  Maintain currency of information


Apply analytics to machine data for greater operational efficiency !

Operations Analysis: Needs

Requirements Analyze machine data to identify events of interest Apply predictive models to identify potential anomalies Combine information to understand service levels Monitor systems to avoid service degradation or outages Gain real-time visibility into operations, customer experience, transactions and behavior Proactively plan to increase operational efficiency

Industry Examples

•  Automotive advanced condition monitoring

•  Chemical and Petroleum condition-based Maintenance

•  Energy and Utility condition-based maintenance

•  Telco campaign management •  Travel and Transport real-time

predictive maintenance •  …


§  Assemble and combine relevant mix of information

§  Discover and explore with smart visualizations

§  Analyze, predict and automate for more accurate answers

§  Take action and automate processes

§  Optimize analytical performance and IT costs

§  Reduced infrastructure complexity and cost

§  Manage, govern and secure information

Enabling organizations to

Performance Management

Content Analytics

Decision Management

Risk Analytics

Business Intelligence and Predictive Analytics

Information Integration and Governance

BIG DATA PLATFORM

SECURITY, SYSTEMS, STORAGE AND CLOUD

Sales | Marketing | Finance | Operations | IT | Risk | HR

ANALYTICS

SOLUTIONS

Industry

CONSULTING and IMPLEMENTATION SERVICES

Content Management

Data Warehouse

Stream Computing

Hadoop System

IBM Provides a Holistic and Integrated Approach to Big Data and Analytics


Accelerators

Information Integration & Governance

Data Warehouse

Stream Computing

Hadoop System

Discovery & Navigation

Application Development

Systems Management

Data Media Content Machine Social

BIG DATA PLATFORM

The Platform for New Insight and Applications

InfoSphere Streams Analyze streaming data and large data bursts for real-time insights

InfoSphere BigInsights for Hadoop Cost-effectively analyze Petabytes of unstructured and structured data

InfoSphere Data Explorer Discover, understand, search, and navigate federated sources of big data


New Architecture to Leverage All Data and Analytics

Data in Mo&on

Data at Rest

Data in Many Forms

Information Ingestion and Operational Information

Decision Management

BI and Predictive Analytics

Navigation and Discovery

Intelligence Analysis

Landing Area, Analytics Zone and Archive §  Raw Data §  Structured Data §  Text Analytics §  Data Mining §  Entity Analytics §  Machine Learning

Real-time Analytics

§  Video/Audio §  Network/Sensor §  Entity Analytics §  Predictive Exploration,

Integrated Warehouse, and Mart Zones

§  Discovery §  Deep Reflection §  Operational §  Predictive

§  Stream Processing §  Data Integration §  Master Data

Streams

Information Governance, Security and Business Continuity

Thank you


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Perceptions & Questions

Analyst: Robin Bloor

Big Data Means ???

In reality BIG DATA is really

BIG PROCESSING POWER MORE DATA: Yes, for sure if it’s useful

DATA SCIENCE: Yes, if it’s needed

REAL-TIME ANALYSIS: Yes, for sure if it’s useful

NEW BUSINESS OPPORTUNITIES: Yes, possibly

A Disturbance in the Force

Disruption by Acceleration We observe the following:

Small Scale Parallelism At the processor level, possibly including GPUs, FPGAs, etc.

SSD Replacing Spinning Disk Faster I/O

Large Scale Parallelism Massively parallel architectures

Cloud Deployment Faster external or internal deployments

Where the Rubber Meets the Road

In respect of BIG DATA, many of the new applications are improvements on “familiar” applications:

u  THE USUAL SUSPECTS – security, fraud, telco churn, banking (trading & risk), etc.

u  GRADUATES – Retail, insurance, healthcare, risk management, etc.

u  NEW KIDS ON THE BLOCK – mobile apps, social media, gaming, web advertising

u  OPPORTUNITY PLAYERS – smart products (transport, machines, devices, etc.)

The Implications

How do we exploit the additional

power? This is a BUSINESS question, not a TECHNICAL question.

The question for most organizations is:

u  Who is the “data explorer,” in IBM’s view?

u  Does IBM believe that data streaming (with analysis) is now ready for prime time?

u  The customer context has particular interest since it affects most companies. Does IBM see this as mainly an operational (i.e., near-real time) application?

u  There seems to be a conflict to resolve between “new Hadoop” and “traditional data warehouse.” What is IBM’s perspective?

u  How is it possible to define and monitor service levels with big data?

u  Big data naturally raises issues about data governance. In IBM’s view, does more data mean that governance become more difficult?

u  Does IBM view its Watson technology as a component of big data applications?


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Upcoming Topics

www.insideanalysis.com

September: ANALYTICS

October: DATA PROCESSING

November: DATA DISCOVERY & VISUALIZATION


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Thank You for Your

Attention Image credits: 1.  Jonathan Zander: http://en.wikipedia.org/wiki/File:MicroATX_Motherboard_with_AMD_Athlon_Processor_2_Digon3.jpg 2.  Nisky.com: http://niskey.com/ssd-drive-the-new-wave/ 3.  Answers.com: http://www.answers.com/topic/massively-parallel

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Entry Points – How to Get Rolling with Big Data Analytics