a4_predictive analytics on big data to improve insight, decision making and profitability

© 2011 IBM Corporation

Predictive Analytics on Big Data to Improve Insight, Decision Making and Profitability

Vincent Toh – Predictive Analytics Solution Architect, IBM SWG, ASEAN

1st March 2012


INSTRUMENTED

INTERCONNECTED

INTELLIGENT

The World is Changing and Becoming More…

The resulting explosion of information creates a need for a new kind of intelligence

…to help build a Smarter Planet


There is an Explosion in Data and Real World Events

4.6 Billon Mobile Phones World Wide

1.3 Billion RFID tags in 2005

30 Billion RFID

tags by 2010

2 Billion Internet

users by 2011

Twitter process

7 terabytes of data every day

Facebook process

10 terabytes of data every day

World Data Centre for Climate 220 Terabytes of Web data 9 Petabytes of additional data

Capital market

data volumes grew

1,750%, 2003-06


Information is Exploding…

2009

800,000 petabytes

2020

35 zettabytes

as much Data and Content

Over Coming Decade 44x Of world‟s data

is unstructured 80%

Source: IDC, The Digital Universe Decade – Are You Ready?, May 2010


What is “BIG DATA”?

All kinds of data

Large volumes

Valuable insight, but difficult to extract

Often extremely time sensitive

Existing sources of data continue to grow

New sources of data are now available

detailed customer data

internet sources

instrumentation

Data arrives at an increasing rate


Optimize capital investments

based on 6 Petabytes of

information

Analyze 100k records/

second to address customer

satisfaction in real time

Analyze telemetry, fuel

consumption, schedule and

weather patterns to optimize

shipping logistics.

Big Data Analytics – Already a Reality

• Cognos Consumer Insight –

Social Media Data

• SPSS Modeler in Netezza

• Cognos Real-time Monitoring

v10

• SPSS for very fast model scoring

• Analyze, plan, align with Cognos

v10 on Hadoop and InfoSphere

BigInsights


Organizations Need Deeper Insights From Their Data

Business leaders frequently make decisions based on information they don’t trust, or don’t have

1 in 3 83% of CIOs cited “Business intelligence and analytics” as part of their visionary plans to enhance competitiveness

Business leaders say they don’t have access to the information they need to do their jobs

1 in 2 of Customers will look to replace their

current warehouse with a pre-integrated

Warehouse solution in the next 3 years,

only 14% have today

35%

Source (TDWI: Next Generation Data Warehouse Platforms Q4 2009)


Business Analytics can be applied to all big data problems

Integrated

Enterprise-ready

Open Source Based

Real-time Scoring

Predictive Analytics

Sentiment Analysis

Real-time Monitoring

IBM Business Analytics

10

IBM Big Data Platform


The problem….

Wasting thousands of dollars annually on its direct marketing campaigns by focusing on products rather

than customer knowledge and behavior

Implementing predictive analytics….

Blend customer segment profiles with profitability data to identify and target the most attractive

segments

First Tennessee Bank

Results

• 3.1% increase in marketing response rate through more accurate targeting of offers to high-value customer segments

• 20% reduction in mailing costs and 17% reduction in printing costs due to the ability to target the most attractive segment for specific offers

• 600% overall return on its investment through more efficiently allocated marketing resources

12

Predictive

Customer

Analytics


The problem….

To provide a more systematic, efficient and accurate way to pinpoint fraud


Created a smarter claims processing workflow, which transformed the way Infinity’s agents

handle and route claims

Infinity Property & Casualty

Results

• Increase of $12 million in subrogation recoveries with success rates in pursuing fraudulent claims increasing from 50% to 88%

• As much as 95% reduction in time required to refer questionable claims for investigation

• 400% ROI with six months of implementation

13

Predictive

Operational

Analytics


The problem….

Faced with rising crime, frozen budgets, growing disenchantment among citizens


Predict future crime hot spots and deploy resources proactively

Memphis Police Department

Results

• 15% reduction in violent crime and a 30% reduction in serious crime overall, including a 36.8% reduction in crime in one targeted area

• 4x increase in the share of cases solved in the MPD‟s Felony Assault Unit (FAU), from 16% to nearly 70%

• Overall improvement in the ability to allocate police resource in a budget-constrained fiscal environment

14

Predictive

Risk & Threat

Analytics


Telecommunications CDR processing

Churn prediction

Geomapping / marketing

Network monitoring

What can you do with big data Analytics?

Utilities Weather impact on power generation Transmission monitoring Smart grid management

Retail 360° View of the Customer

Click-stream analysis

Real-time promotions

Law Enforcement Real-time multimodal surveillance

Situational awareness

Cyber security detection

Financial Services Fraud detection

Risk management

360° View of the Customer

IT Transition log analysis for

multiple systems Cybersecurity

Health & Life Sciences

Epidemic early warning

ICU monitoring

Healthcare monitoring

Transportation Weather and traffic

impact on logistics and

fuel consumption


Applications for BIG Data Analytics are Endless

Neonatal Care Trading Advantage

Law Enforcement Radio Astronomy

Environment

Telecom

Manufacturing Traffic Control Fraud Prevention


A semiconductor wafer manufacturer

The Challenge:

•produces 50,000+ semiconductor wafers/month

•working to incredibly precise specifications on the scale of 0.25 microns to 0.14 microns

•manually compiled data from many sources, which provided inaccurate information, slowed production and left expensive machinery idle.

• It needed real-time monitoring and sophisticated analytics in producing higher-quality wafers, faster

The Solution: •IBM Cognos® Business Intelligence •IBM InfoSphere® •IBM Netezza 1000 •IBM SPSS® •IBM Tivoli® Monitoring

The Benefits:

•Expected to generate USD27 million over a period of three years ~ ROI of 344 %

•Increased wafer fabrication yields and minimized defects by providing near real-time information on wafer fabrication and testing

•Optimized use of valuable manufacturing equipment by removing expensive, inefficient delays that leave machines idle

© 2011 IBM Corporation 18 18

“Because of (Netezza’s) in-database technology, we believe we'll be able to do

600 predictive models per year (10X as many as before) with the same staff”.

- Eric Williams, CIO & Executive VP

The Challenge:

• Collected POS data from 35,000+ retail stores

• 450 millions records daily transactions

• Better targeted marketing campaigns

The Solution:

• Use IBM Netezza EDW as Data Storage

• Use IBM SPSS to analyze past purchase behavior and provide deep insight into shopper

specific buying behavior

The Benefits:

• Scans data at 675 million rows per second, equivalent to more than 163 TB/hour

• Process 70 times more queries on 5 times the amount of data ~100,000 SQL queries/

day

• Decreased analytics processing from half a day to 60 seconds –over 99%

reduction.


The Challenge:

•Identify at risk mid-sized customers

•Reduce customer churn

•Slow and cumbersome processing of massive transaction data

The Solution

•IBM Netezza

•IBM SPSS

The Benefits:

•60 % improvement in revenue retention rates

•Realizing millions of dollars in annualized revenue protection

•Fewer client services managers are needed for the same level of risk coverage

“By enabling our client services managers to prioritize their proactive outbound calls – basically, a

„health check‟ on the customer – we can cover more risk with our existing Client Services team . It‟s

been a very successful business model for us and has helped us

organize our resources better.”

Trent Taylor, Director – Customer Intelligence, XO Comm.


IBM Smartphone Event Application

Question:

Which option below is one of the three pillars of Predictive Analytics on Big Data?

a) Customer Analytics

b) Financial Analytics

c) Sentiment Analytics


Answer…..

Question:

Which option below is one of the three pillars of Predictive Analytics on Big Data?

a) Customer Analytics

b) Financial Analytics

c) Sentiment Analytics

Answer is ( a )


Vietnam contact:

Business Analytics

Đào Quốc Hưng

Sales Manager - Business

Analytics

Software Group - IBM

Vietnam

Mobile: +84 986 988 741

Email: [email protected]

Information

Management Cao Thi Phuong Lien

Sales Manager - Information

Management

Software Group - IBM

Vietnam

Mobile: 84-912 31 52 59

Email: [email protected]

mailto:[email protected]

mailto:[email protected]

a4_predictive analytics on big data to improve insight, decision making and profitability

Documents