Embed Size (px)
Citation preview
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
Moving from Big Data to Big Content
Big Content was first introduced by Gartner’s Craig Roth in 2012.
“Big Content combines technologies to go beyond traditional search […]
to text analytics, sentiment analysis, video analysis, semantic web
technologies, and attention management.”
http://blogs.gartner.com/craig-roth/2012/10/17/big-content/
Big Content stresses more what your data is about and who is using it
to make informed decisions.
The move to Big Content shows a natural evolution of combining
technologies such as Semantic Tech with Analytics.
This is still focused on data structure & data integration – we need to
go a step farther…
Slide 6
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
THANK YOU!
Contact Information:
Eric Little, PhD
VP Data Science
