Eric Little, PhD

CDO

eric.little@osthus.com

Early AI Adoption Via

Advanced Analytics

Slide 2

Understanding the 4V’s of Big Data

Normally the focus – Big

Data Analysis is more than

just size

Performance is Critical to

Success

Data complexity is

increasing – Model

complexity

Uncertainty abounds –

requires statistics and

probabilities

Majority of Big Data analytics

approaches treat these two V’s

Semantic

technologies provide

clear advantages

Mathematical

Clustering

Techniques

provide clear

advantages

Slide 3

Analytics and Data Science for the 21st

Century

Integrating data is becoming more complex

The size of data sources continues to grow

Different user groups within organizations

Answers need to reflect increasingly complex patterns

The rate of change in digital information is growing exponentially

Cloud Computing is now critical for scaling an enterprise

New data types are being created - hold significant value

Data is becoming more personalized and context-based

The effect of data is changing the business landscape

$900 Billion/year: cost of lowered employee productivity and reduced

innovation from information overload (PR News Wire, 2008)

“Increasing volume and detail of enterprise information, multimedia, social media, and the

Internet of Things will fuel exponential growth in data for the foreseeable future.”

“The use of big data will become a key basis of competition and growth for individual firms.”

McKinsey: “Big data: The next frontier for innovation, competition, and productivity”, May 2011

Slide 4

The power of analytics is now just

beginning to be felt

Moore’s Law pertaining to

processing is not the problem

Focus on the growth of Analysis:

From 1988-2003 Computer

processing speed grew by

1000x

In the same period algorithm

dev grew by 43,000x

What does this tell you about

the direction in which we are

headed?

As data grows, so too will the

need to utilize it more effectively

The Growth of Analytics is Changing the Game

AN

ALY

TIC

S

Slide 5

The Dawn of Big Analysis

Big Analysis combines semantic

technologies with more traditional data

science methods involving mathematics.

Semantic Tech utilizes logic-based

reasoning

Traditional Data Science utilizes statistics-

based reasoning

Combining these approaches allows for a new way of doing

analysis

Data can be clustered statistically then use ontologies to provide a

deeper level of analysis of the clusters.

Data can be semantically integrated/modeled and have weights and

other approaches added to those models using statistics

Provides an informing-constraining type of relationship for advanced

analysis of complex data patterns

Slide 6

Interdisciplinary thought is the key

Thought is key in today’s industries

Answers are not found in books, manuals,

algorithms, etc.

Abstract thinking & problem solving is an

increasing commodity

Meta-ideas are driving today’s innovations

New paradigms for education are

needed that break down barriers

between disciplines

Industries need to look to non-

traditional hires and resources for

new skill sets

Break out of traditional molds/views

Connecting data, people and organisations