Наші у … IBM

Oleksandr Romanko, Ph.D. Senior Research Analyst, Risk Analytics – Business Analytics, IBM Adjunct Professor, University of Toronto

Chasopys, Kyiv July 31, 2015

2

Please note:

IBM Risk Analytics statements regarding its plans, directions, and intent are subject to

change or withdrawal without notice at IBM’s sole discretion.

Information regarding potential future products is intended to outline our general product

direction and it should not be relied on in making a purchasing decision.

The information mentioned regarding potential future products is not a commitment,

promise, or legal obligation to deliver any material, code or functionality. Information about

potential future products may not be incorporated into any contract. The development,

release, and timing of any future features or functionality described for our products

remains at our sole discretion.

Performance is based on measurements and projections using standard IBM benchmarks

in a controlled environment. The actual throughput or performance that any user will

experience will vary depending upon many factors, including considerations such as the

amount of multiprogramming in the user's job stream, the I/O configuration, the storage

configuration, and the workload processed. Therefore, no assurance can be given that an

individual user will achieve results similar to those stated here.

3

About me

Dr. Oleksandr Romanko

Senior Research Analyst, Quantitative Research at Risk Analytics, Business Analytics, IBM, with the company since 2010

Ph.D. in Computer Science from McMaster University

Author of over 20 papers and reports

Adjunct professor at University of Toronto and lecturer at McMaster University

Was Mitacs Elevate Industrial Postdoctoral Fellow

Research areas:

operational research, optimization, finance

portfolio optimization, multi-objective optimization

market and credit risk modeling and optimization

numerical methods for risk management

design of numerical algorithms and their software implementation

4

Quick facts about IBM Risk Analytics (formerly Algorithmics Inc.)

Algorithmics offers risk solutions, software and advisory services for – Banking

– Insurance

– Asset Management

– Hedge Funds

– Pension Funds

Founded in 1989

Acquired by IBM in 2011 for $387M USD

Over 800 employees worldwide – 200 in Research and Development

– 250 in Professional Services

– 110 in Business Lines

Head Office in Toronto – Primary offices in London and New York

– 23 offices globally in all major financial centers

– Clients in 55 countries

2010 revenue over $163M USD

5

Some of our clients

Being an IBMer

7

IBM Centennial: A Century of Progress

1911

2011

Incorporated on June 16, 1911 in US as the

Computing Tabulating Recording Company

CTR changed its name to International

Business Machines Corporation

globally in 1924

CTR changes name in Canada to International

Business Machines Company in 1917

8

Making the world work better – pioneering the science

2008

1973 1969

1981

9

IBM Centennial: 100 Years of Innovation

Business Analytics

Predictive Analytics What will happen?

Descriptive Analytics What has happened?

Prescriptive Analytics What should we do?

What is analytics?

Data Insight Action

Decide Analyze

Business Value

11

Analytics is the scientific process of deriving insights from

data in order to make decisions

12

Applications of big data analytics

Homeland Security

Finance Smarter Healthcare Multi-channel sales

Telecom

Manufacturing

Traffic Control

Trading Analytics Fraud and Risk

Log Analysis

Search Quality

Retail: Churn, NBO

13

History of business analytics

14

History of business analytics

15

Movies

Business Analytics Examples

17

Smarter Cities

18

Use of camera phones at the Papal inauguration in 2005 and 2013

19

We can collect information from almost everything to make better decisions

Camera phones in

existence able to

document accidents,

damage, and crimes

1 billion RFID tags

embedded into our

world and across

entire ecosystems

30 billion Of new automobiles

will contain event data

recorders collecting

travel information

85%

Instrumented Interconnected Intelligent

Pit stop analytics

7

Calculations showed that time spent changing tires and refilling the tank was more than offset by the improved performance of the car on the track. 1. Softer tires stuck to the track better during turns than their harder cousins,

though they wore out more quickly. 2. Less gas in the tank translated into a lighter, and therefore faster, car.

Optimized F1 pit teams can change four tires in two seconds

21

Artificial intelligence

Source: A Brief Overview and Thoughts for Healthcare Education and Performance Improvement by Watson Team

22

Artificial intelligence

Source: A Brief Overview and Thoughts for Healthcare Education and Performance Improvement by Watson Team

On 27th May 1498, Vasco da Gama landed in Kappad Beach

On 27th May 1498, Vasco da Gama landed in Kappad Beach

celebrated

May 1898 400th anniversary

arrival in

In May 1898 Portugal celebrated the 400th anniversary of this explorer’s arrival in India.

Portugal

landed in

27th May 1498

Vasco da Gama

Temporal Reasoning

Statistical Paraphrasing

GeoSpatial Reasoning

explorer

On 27th May 1498, Vasco da Gama landed in Kappad Beach

On the 27th of May 1498, Vasco da Gama landed in Kappad Beach

Kappad Beach

Para-phrases

Geo-KB

Date Math

India

Search Far and Wide Explore many hypotheses

Find Judge Evidence

Many inference algorithms

IBM Educational Initiatives

IBM Academic Initiative program

Cognos SPSS ILOG

Master of Business Analytics programs – top 20 universities

Industry support for Master of Business Analytics programs

Industry support for Master of Business Analytics programs

Example analytics degree programs with university partners

Master of Business Analytics programs – curriculum

Прикладная статистика и теория вероятности (Applied Statistics and Probability)

Основы вычислительной математики (Fundamentals of Computational Mathematics)

Интеллектуальный анализ данных и эксперные системы (Data Mining and Knowledge Discovery)

Имитационное моделирование (Simulation Modelling)

Оптимизация (Optimization)

Вычислительные финансы и риск менеджмент (Computational Finance and Risk Management)

Визуальная аналитика и представление знаний (Visual Analytics and Knowledge Representation)

Математическое моделирование в бизнесе (Mathematical Modelling for Business)

Аналитика в маркетинге (Marketing Analytics)

Стратегии менеджмента инноваций (Strategies for Managing Innovations)

Аналитика Интернета, социальных сетей и бизнес новостей (Analytics of Web, Social Networks and Business News)

Analytics Consortium

31

Analytika PH http://newsbytes.ph/2014/04/22/consortium-launched-to-make-ph-as-global-analytics-hub-by-2015/

Online Education

33

Online education – Coursera, EdX, Prometheus

34

Online education - Big Data University (bigdatauniversity.com)

Flexible on-line delivery allows learning @your place and @your pace

Free courses, free study materials.

Cloud-based sandbox for exercises – zero setup with Robust Course Management System and Content Distribution infrastructure

169,000 registered students.

Free IBM Hadoop, BigInsights Publications

35

TED and TEDx

Startups

37

Ecois.me

38

Nymi

Volunteering and Networking

40

Networking

Ukrainian Diaspora in Canada

EuroMaidan Canada

43

Questions