Eric Little, CDO at OSTHUS about "Early AI Adoption Via Advanced Analytics"

1. Eric Little, PhD
CDO
eric.little@osthus.com
Early AI Adoption Via
Advanced Analytics

2. 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

3. 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

4. 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
ANALYTICS

5. 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

6. 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

7. Connecting data, people and organisations