Great new technologies that can Radically Transform your ...

Big Data, Artificial Intelligence, Robotics, Machine Learning:

Great new technologies

that can Radically Transform

your business!

Dr. Sreerama KV Murthy

CEO & Chief Data Scientist, Quadratyx

12th September 2017, Bogota

1. Computing Technology

is fundamentally changing. Software, hardware, IT, programming, GUI, OS, Databases, …

Three key take-aways of this seminar:

2. Every area of human

endeavour will be massively

impacted. Business, Government, Service, Education, Healthcare, Financial, …

Three key take-aways:

3. Your business can

benefit Here & Now! Those embracing the change are reaping rich benefits, world-wide.

Three key take-aways:

Structure of

this

symposium

3. How to adopt the change here

& now

Steps of implementation, pitfalls and precautions, getting things done in low cost with clearly measurable ROI, getting things done locally

2. Impact is across all spheres of

human endeavour

Real use cases from multiple industry verticals world-wide – Agriculture, Banking, Communication, Education, Government, Healthcare, Hospitality, IT, Logistics, …

1. Computing technology is

radically changing

Jargon-free introduction to Artificial Intelligence, Machine Learning, Robotic Process Automation, Big Data, Deep Learning, IOT, Cognitive Computing, …

Computing is

fundamentally changing. Section 1

Can your IT

department do

this? Write

software to record

all sales. Report

profitability by

item.

This problem can be answered by a step-by-step calculation.

Can your IT

department do

this? Write software

to predict items that

will have the least sales

next week. Suggest the

best way to increase

sales.

This problem can only be answered through business experience,

and by studying a lot of past data.

Step-by-step computation versus

data-driven decision making

Write a program to calculate total distances

travelled by every mega bus route, and its fuel

requirements.

Vs.

Write a program to predict what bus routes will

be most expensive to run for the city next year

(based on expected traffic, load, road conditions,

etc).

Data Driven Decision Making - Advantages

More accurate – Calculations may make assumptions.

Directly Relevant - Based on true observations, not theories

Tailored – Conclusions uniquely fit your present data

Adaptive – Gets refined automatically as data changes

Less prone to human error – Less chances of human brilliance also!

Less costly to make and maintain – Same methods/tools can solve multiple

problems.

The AI Revolution

Video Trailer:

The Human Face of Big Data

Questions Please

Big Data Technologies to cheaply & reliably store & parallel-process high volume, high variety and high velocity data

Artificial Intelligence Systems that exhibit intelligence akin to that of living things

Statistics & Mathematical

Data Analysis

Machine

Learning Building self-

improving systems

Human-like

Interfaces (Speech, Vision,

Auditory, Printed

Docs, Natural

Language,

Tactile, Smell)

Advanced Analytics (Descriptive, Predictive, Prescriptive)

Deep Learning

Other AI

Technologies Theorem proving, Planning,

Game Playing, Multi-agent

Systems, Qualitative Physics,

Intuition / Imagination

Data Science

Big Data has a lot of Unstructured Data

Orders of Magnitude Multiples of bytes

SI decimal prefixes Binary

usage Name

(Symbol) Value

kilobyte (kB) 103 2

10

megabyte (MB) 106 2

20

gigabyte (GB) 109 2

30

terabyte (TB) 1012

240

petabyte (PB) 1015

250

exabyte (EB) 1018

260

zettabyte (ZB) 1021

270

yottabyte (YB) 1024

280

Why & When do

companies move to Big

Data?

External Factors

New varieties of

data

Data size

growing

Data at high

speed

Other Considerations

Avoid Vendor

Lock-in

Move to

Open Source.

Minimize licensing

Lower TCO

Internal Factors

Connect scattered

data sources for

insight

Harvest data

that is

thrown away now

Source of

competitive

advantage

Why Big Data?

Batch

Iterative

Interactive

Real Time

Transactional

Descriptive: What happened?

Predictive: What will happen?

Prescriptive: What can I do about it?

MIS Reports

BI & Dashboards

Scenario Analysis,

What-if

Business

Layer

Big Data Evolution

Computing Evolutions – Big Data

Databases & Data marts

Data Warehouses

Data Lakes

Statistics

Machine Learning

AI & Deep Learning

Tightly Coupled

Technology Stacks

Loosely Coupled

Ecosystems

Proprietary

OR

supported

Open Source

Open Source

Tech

Layer

Computing Evolutions – Big Data (contd.)

Methods in Big Data Analytics

23

24

age income student credit_rating buys_computer

<=30 high no fair no

<=30 high no excellent no

31…40 high no fair yes

>40 medium no fair yes

>40 low yes fair yes

>40 low yes excellent no

31…40 low yes excellent yes

<=30 medium no fair no

<=30 low yes fair yes

>40 medium yes fair yes

<=30 medium yes excellent yes

31…40 medium no excellent yes

31…40 high yes fair yes

>40 medium no excellent no

A decision tree

25

age?

overcast

student? credit rating?

<=30 >40

no yes yes

yes

31..40

fair excellent yes no

Forecasting

Yield (profit/acre) of a seed as a function of altitude

Prediction and forecasting

Classification

Classification; Unsupervised Clustering

Making Predictions Through Causality

Optimization

e.g., A manufacturer has fixed

amounts of different resources

such as raw material, labor, and

equipment.

These resources can be

combined to produce any one of

several different products.

The quantity of the ith resource

required to produce one unit of

the jth product is known.

The decision maker wishes to

produce the combination of

products that will maximize total

income.

Simulation

I want to price a home insurance product.

The inflation may change between 5-8%.

People may opt for 15-30 years of maturity.

The defaults may be 1-3%. There may be 2-

4% of the cases, where we may need to make

the payouts.

How much should the pricing be?

Questions Please

Synopsis of Key Hadoop Vendors Pure play Hadoop vendors: Cloudera, Hortonworks, MapR, IBM OpenPlatform,

Huawei FusionInsight, Seabox, Transwarp

Cloud infrastructure as a service (IaaS): Hadoop on AWS, Hadoop on Azure

Platform as a service (PaaS): IBM BigInsights, Microsoft HDInsight, Google Cloud

Platform, Amazon EMR, Oracle Big Data Cloud Service, Qubole

Big Data Appliances: Teradata, Oracle Big Data Appliance, Cray

http://www.slideshare.net/adersberger/big-data-landscape-2016-58917032

Section 1: Computing is fundamentally changing.

All aspects of business are

being impacted. Section 2

PHARMACEUTICA

L BANKING & FINANCIAL

AGRICULTURE HEALTHCARE

BIG DATA & ADVANCED ANALYTICS products & solutions are

APPLICABLE ACROSS DOMAINS

RETAIL HOSPITALITY AVIATION

OIL & GAS CPG SUPPLY CHAIN

WEB ANALYTICS MARKETING ANALYTICS MULTIMEDIA ANALYTICS

Pre-packaged Vertical-agnostic Products

Each product is deployed on live client locations delivering significant ROI

Copyright © 2016, Quadratic Insights Private

Limited.

Copyright © 2016, Quadratic Insights Private

Limited.

Some of our esteemed clients

Corporate Training in Colombia through

Product Video

Questions Please

In any complicated system, making decisions

without accurate data can lead to decisions

that are worse than random.

The problem is not ignorance.

The problem is preconceived ideas.

Hans Rosling

Thank you!

Questions & Discussion

www.Quadratyx.com