Rodney Hite is a product manager for Big Data solutions at ViON. The document discusses the history and evolution of big data, from the earliest disk formats in the 1970s-80s that held kilobytes of data, to the present day where a variety of data sources generate huge volumes, velocities, and varieties of data. It outlines analytical techniques like semantic extraction, sentiment analysis, and predictive pattern analysis that can gain valuable insights from big data across domains like sports, security, fraud detection, and social media. The key to success is having an iterative strategy that focuses on desired results, future-proof technologies, integration, and using data scientists and engineers efficiently.

1. Rodney Hite
Lightning Round
Product Manager,
Big Data Solutions,
ViON

2. Analytics in the Age of Big Data

3. Big Data #1
“Most Ambiguous Terms”
Global Language Monitor

4. Big Data Is Not New
4
1976 – physical disk formats:
hard-sectored 90 KB and soft-
sectored 110 KB
1983 - single-sided media, with
formatted capacities of 360 KB
1984 – double-sided media,
with formatted capacities of
720 KB
1986 - What became the most
common format, the double-
sided, high-density (HD) 1.44
MB disk drive.

5. The New “Big Data”
5
Gartner. In 2001, a Meta (now Gartner) report noted the increasing size of
data, the increasing rate at which it is produced and the increasing range
of formats and representations employed.
This report predated the term “Big Data” but proposed a three-fold
definition encompassing the “three V’s”: Volume, Velocity and Variety.
2008 - Apache Hadoop is an open-source software framework for storage
and large-scale processing of data-sets on clusters of commodity hardware.

6. 6
Semantic Extraction
Sentiment Analysis
Entity Extraction
Link Analysis
Temporal Analysis
Geospatial Analysis
Time Event Matrices
Predictive Pattern Analysis
Video/Imagery Analytics
Machine Created - logs
Email
Video – Predator Surveillance
Audio – Phone recordings
Sensor - Weather
Social Media - Twitter
Databases – Structured Text
Reports – Semi-Structured Text
Documents – Unstructured Text
Graphs – Graph Dbs
Data Analytics
A new world of analytical possibilities is opened.
Data and Complexity

7. 7
It’s All About the Data

8. Getting Value
8
Visualization Is A Critical Accelerator For Data Exploration
The Best Big Data integration technology allow visual exploration of data
independent of the type of data or the source from which it came

9. 9

10. Gartner Hype Curve
10
What’s really new is the technology available that allows us to
make sense of the data.

11. Visualization versus Analytics
11
Data Visualization - data that is
available to those who know how to
get it and make it presentation
friendly and easier to digest by
your average audience member.
Data Analytics - is a multi-
dimensional discipline using
mathematics and statistics to
gain valuable knowledge from
data - data analysis.

12. Top 100 NFL Players of All Time
12

13. NFL Graphs
13
• Predictive Analytics used to determine probability of success based on
Down and Distance.
• Correlation Analytics conducted on Tom Brady’s individual statistics and
his affect on game outcome.

14. MLB Pitching Analysis
14
Analyze multiple data sources to include video analytics to maximize the
usage of the data providing valuable insight.
TruMedia's MLB analytics platform
Pitch Frequency Strikeout Pitches

15. Geospatial Analysis – Data Fusion
15
Data integration with mapping features allows interactive visualization of
data fusion with Geospatial and Temporal references.

16. Cyber Security Analysis
16
• Analysis to identify tactics, techniques and processes to identify,
isolate and eliminate risks to the environment.
• Discover actionable, often unforeseen, insight because the Semantic
Analysis highlights interdisciplinary relationships and unexpected data
combinations

17. Fraud Detection
Fraud involves cell phones, insurance claims, tax return claims, credit card
transactions etc
Combine historical and transactional data to detect fraudulent activity,
identify transactional behavior that indicates a high likelihood of illegal
activities.
17

18. Predictive Pattern Analytics
Analytical tool for predicting the location of future incidents
This analytic provides an awareness of the general situation, and additionally
it provides a series of tools for decision support
18

19. Investigations - Pattern of Life
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• Pattern-of-life analysis is a method of surveillance specifically used for
documenting or understanding a subject’s habits.
• This information can then be used to predict future actions by the
subject(s) being observed.

20. Social Media Analysis – NLP & Entity Extraction
Advanced text analytics tools analyze the unstructured text to
gain understanding of the context, identify entities and their
relationships, conduct topic clustering, determine contextual
sentiment, and conduct time-event trending.
20

21. What Is A Successful Big Data Strategy
Defined Desired Results – Design an Iterative Approach
Future - Be future proof through design – Hadoop and NoSQL
Cost - Understand the Licensing Model vs Professional Services
Resources – Use your Data Scientist and Engineers on the Data not the
Infrastructure
Integration - Big Data integrations are built to be embedded in other
environments

22. Thank You!
Rodney Hite
Product Manager – Big Data
Rhite@vion.com
O: 571-353-6097/c: 919-604-0809