Deep Web, Open Source Intelligence, and The Bad Guy
As Data Scientist, we are constantly asked to derive insights, in form of predictive and prescriptive models, from disparate sets of internal and external data. Of all the possible kinds of insights available, we are now seeing real business value from those that come in from Anticipatory Intelligence, which is actionable insights delivered through next best steps. While many think the real challenge is developing anticipatory intelligence is in their algorithms, the challenge in realizing reliable value is actually in complexity of disparate data sets themselves. In this discussion, we will explore how data science is impacted by one of the most complex data sets of all times, the deep web. In doing so we use an open source intelligence driven data science framework to explore dark kinetic world of anarchy (the deep dark web), while searching for one particular influential organization responsible for orchestrating violence amongst passive groups.
About the Presenter:
Dr. Jerry A. Smith is the Chief Data Scientist for Capgemini’s Advance Digital Intelligence (ADI) group and Data Science & Analytics (DSA) group. His work includes the develop of intelligence platforms that harvest the digital world through hadoop ecosystems, map reduce analytics, and distribute R predictive and prescriptive models. Dr. Smith also served in the United States Navy where he flew the A-6 Intruder and operated submarine-based nuclear reactors.
Data Scientist Insights: http://datascientistinsights.com
Dr. Jerry A. Smith is a visionary entrepreneurial data scientist with 16 years of machine learning, predictive modeling, digital and business analytics, big data, and enterprise data science experience. Jerry is recognized as a leading expert in simulation and modeling, leveraging Walsh-Hadamard transforms to identify spatial patterns in real-time temporal data. Over the last five years, he has worked on practical methods for unifying data science capabilities and big data infrastructure throughout the enterprise, in order to create organic and systemic Data Monetization capabilities.
He has created data Science practices, which focuses on helping companies realize actionable value from data and information sources - Data Monetization. Leveraging an Enterprise Data Science (EDS) methodology based on an hypotheses-driven framework, data science teams (data scientists and functional behavioral analysts) identify key business objectives, along with critical causal-levers that impact the value chain. Enterprise, IT, and third party data is inventoried and assessed for their causality and correlative characteristics. Exploratory visualization maps are created that lead to the design and development of predictive models. The models are aggregated into complex solution spaces; which represent comprehensive, cohesive predictive ecosystem. Using simulated annealing, optimal actionable structures are identified, which are implemented across their enterprise applications.
Dr. Smith is recognized as an technologist, innovator, and communicator: Technical evangelist to investors, stakeholders, and clients. Won major industry awards. Recognized for technology trend-setting prescience by Gartner & Ben Franklin Technology Partners. Excels at translating complex modeling into clear understandings. Cited by technology-oriented periodicals, industry reports, and media.