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DN2017 | From Big Data to Smart Data | Kirk Borne | Booz Allen Hamilton

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Smart data are essential when faced with massive-scale data collections. "Smart" refers to data that are tagged or indexed with meaning-filled metadata that carry information about the semantic meaning of the data, its applications, use cases, content, context, and more. Such meta-tags enable efficient and effective discovery, description, and delivery of the right data at the right time, both to humans and to automatic processes.

Kirk Borne is a data scientist and an astrophysicist who has used his talents at Booz Allen since 2015. He was professor of astrophysics and computational science at George Mason University (GMU) for 12 years. He served as undergraduate advisor for the GMU data science program and graduate advisor in the computational science and informatics Ph.D. program.

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DN2017 | From Big Data to Smart Data | Kirk Borne | Booz Allen Hamilton

  1. 1. Principal Data Scientist Booz Allen Hamilton Kirk Borne @KirkDBorne From Big Data to Smart Data: Top Trends in Data Science, AI, and ML http://www.boozallen.com/datascience @KirkDBorne #DN2017
  2. 2. OUTLINE • First came Data, then Big Data, now Smart Data • Data is the Fuel for AI and ML • Top Trends in Data Science, AI, and ML • Get Smart! 2 @KirkDBorne #DN2017
  3. 3. OUTLINE • First came Data, then Big Data, now Smart Data • Data is the Fuel for AI and ML • Top Trends in Data Science, AI, and ML • Get Smart! 3 @KirkDBorne #DN2017
  4. 4. Ever since we first explored our world… http://www.livescience.com/27663-seven-seas.html 4
  5. 5. …We have asked questions about everything around us. https://atillakingthehun.wordpress.com/2014/08/07/atlantis-not-lost/ 5
  6. 6. The Blind Men and the Elephant With a limited set of signals, there are many possible interpretations of what the source is! 6
  7. 7. So, we have collected evidence (data) to answer our questions, which leads to more questions, which leads to more data collection, which leads to more questions, which leads to… BIG DATA! https://www.linkedin.com/pulse/exponential-growth-isnt-cool-combinatorial-tor-bair 7
  8. 8. More data can help! … Or does it? 8
  9. 9. We have collected evidence (data) to answer our questions, which leads to more questions, which leads to more data collection, which leads to more questions, which leads to… BIG DATA! y ~ 2 * x (linear growth) y ~ 2 ^ x (exponential growth) https://www.linkedin.com/pulse/exponential-growth-isnt-cool-combinatorial-tor-bair y ~ x! ≈ x ^ x → Combinatorial Growth! (all possible interconnections, linkages, and interactions) 3+1 V’s of Big Data: Volume = the most annoying V Velocity = most challenging V Variety = most rich V for discovery Value = the most important V 9
  10. 10. “All the World is a Graph” – Shakespeare? (…or a network) (Graphic by Cray, for Cray Graph Engine CGE) http://www.cray.com/products/analytics/cray-graph-engine 10
  11. 11. Semantic, Meaning-filled Data: • Ontologies (formal) • Taxonomies (class hierarchies) • Folksonomies (informal) • Tagging / Annotation – Automated (Machine Learning) – Crowdsourced – “Breadcrumbs” (user trails) Broad, Enriched Data: • Linked Data (RDF) – All of those combinations! • Graph Databases • Machine Learning • Cognitive Analytics • Context • The 360o view What makes your data smart? The Human Connectome Project: mapping and linking the major pathways in the brain. http://www.humanconnectomeproject.org/ 11
  12. 12. 12 Smart Data Techniques and Applications (from http://smartdata2017.dataversity.net)
  13. 13. OUTLINE • First came Data, then Big Data, now Smart Data • Data is the Fuel for AI and ML • Top Trends in Data Science, AI, and ML • Get Smart! 13 @KirkDBorne #DN2017
  14. 14. Ubiquitous Smart Data from the Internet of Things (IoT): Deploying intelligence at the point of data collection! (Machine Learning at the edge of the network = Edge Analytics!) Internet of Everything https://www.nsf.gov/news/news_images.jsp?cntn_id=122028 The Internet of Things (IoT) will be an interconnected universe of Sensor Networks and Dynamic Data-Driven Application Systems (dddas.org) => Combinatorial Explosive Growth of Smart Data! 14
  15. 15. • Smart Health • Precision Medicine • Precision Farming • Personalized Financial Services • Smart Organizations • Predictive Maintenance • Prescriptive Maintenance • Smart Grid • Smart Apps • Predictive Retail • Precision Marketing • Smart Highways • Precision Traffic • Smart Cities • Predictive Law Enforcement • Personalized Learning Smart => Predictive, Precision, Personalized! Smart Data in the IoT + Edge Analytics = Dynamic Data-Driven Application Systems 15
  16. 16. OUTLINE • First came Data, then Big Data, now Smart Data • Data is the Fuel for AI and ML • Top Trends in Data Science, AI, and ML • Get Smart! 16 @KirkDBorne #DN2017
  17. 17. Top 10 Trends 1) IoT (Internet of Things, Internet of Everything, Analytics of Things, …, Internet of Context) = “The Age of Context” 2) Hyper-Personalization (Location-aware, Digital exhaust, Social trails) 3) AI (not only Artificial, but Augmented & Assisted Intelligence) 4) Machine Intelligence (process automation, chatbots, Deep Learning in images, text, voice, and other complex data) 5) AR (Augmented Reality: in the field, emergency response, training for complex tasks, search & pick, gamification of learning, 3D data/info viz) 6) Behavioral Analytics (predictive and prescriptive modeling of human interests, intents, motivations, actions = Maslow’s hierarchy of needs?) 7) Graph Analytics (“All the world is a graph” = linked data, …) 8) Journey Sciences (people, processes, products, …) 9) The Experience Economy (Design Thinking for User, Customer, Employee) 10) Agile – DataOps (Incremental, Iterative, Fail-fast, Minimum Viable Product) 17 (in no particular order)
  18. 18. 1) IoT (Internet of Things, Internet of Everything, Analytics of Things, …, Internet of Context) = “The Age of Context” 2) Hyper-Personalization (Location-aware, Digital exhaust, Social trails) 3) AI (not only Artificial, but Augmented & Assisted Intelligence) 4) Machine Intelligence (process automation, chatbots, Deep Learning in images, text, voice, and other complex data) 5) AR (Augmented Reality: in the field, emergency response, training for complex tasks, search & pick, gamification of learning, 3D data/info viz) 6) Behavioral Analytics (predictive and prescriptive modeling of human interests, intents, motivations, actions = Maslow’s hierarchy of needs?) 7) Graph Analytics (“All the world is a graph” = linked data, …) 8) Journey Sciences (people, processes, products, …) 9) The Experience Economy (Design Thinking for User, Customer, Employee) 10) Agile – DataOps (Incremental, Iterative, Fail-fast, Minimum Viable Product) 18 Top 10 Trends (in no particular order)
  19. 19. 1) IoT (Internet of Things, Internet of Everything, Analytics of Things, …, Internet of Context) = “The Age of Context” 2) Hyper-Personalization (Location-aware, Digital exhaust, Social trails) 3) AI (not only Artificial, but Augmented & Assisted Intelligence) 4) Machine Intelligence (process automation, chatbots, Deep Learning in images, text, voice, and other complex data) 5) AR (Augmented Reality: in the field, emergency response, training for complex tasks, search & pick, gamification of learning, 3D data/info viz) 6) Behavioral Analytics (predictive and prescriptive modeling of human interests, intents, motivations, actions = Maslow’s hierarchy of needs?) 7) Graph Analytics (“All the world is a graph” = linked data, …) 8) Journey Sciences (people, processes, products, …) 9) The Experience Economy (Design Thinking for User, Customer, Employee) 10) Agile – DataOps (Incremental, Iterative, Fail-fast, Minimum Viable Product) 19 Top 10 Trends (in no particular order)
  20. 20. 1) IoT (Internet of Things, Internet of Everything, Analytics of Things, …, Internet of Context) = “The Age of Context” 2) Hyper-Personalization (Location-aware, Digital exhaust, Social trails) 3) AI (not only Artificial, but Augmented & Assisted Intelligence) 4) Machine Intelligence (process automation, chatbots, Deep Learning in images, text, voice, and other complex data) 5) AR (Augmented Reality: in the field, emergency response, training for complex tasks, search & pick, gamification of learning, 3D data/info viz) 6) Behavioral Analytics (predictive and prescriptive modeling of human interests, intents, motivations, actions = Maslow’s hierarchy of needs?) 7) Graph Analytics (“All the world is a graph” = linked data, …) 8) Journey Sciences (people, processes, products, …) 9) The Experience Economy (Design Thinking for User, Customer, Employee) 10) Agile – DataOps (Incremental, Iterative, Fail-fast, Minimum Viable Product) 20 Top 10 Trends (in no particular order)
  21. 21. 1) IoT (Internet of Things, Internet of Everything, Analytics of Things, …, Internet of Context) = “The Age of Context” 2) Hyper-Personalization (Location-aware, Digital exhaust, Social trails) 3) AI (not only Artificial, but Augmented & Assisted Intelligence) 4) Machine Intelligence (process automation, chatbots, Deep Learning in images, text, voice, and other complex data) 5) AR (Augmented Reality: in the field, emergency response, training for complex tasks, search & pick, gamification of learning, 3D data/info viz) 6) Behavioral Analytics (predictive and prescriptive modeling of human interests, intents, motivations, actions = Maslow’s hierarchy of needs?) 7) Graph Analytics (“All the world is a graph” = linked data, …) 8) Journey Sciences (people, processes, products, …) 9) The Experience Economy (Design Thinking for User, Customer, Employee) 10) Agile – DataOps (Incremental, Iterative, Fail-fast, Minimum Viable Product) 21 Top 10 Trends (in no particular order)
  22. 22. 1) IoT (Internet of Things, Internet of Everything, Analytics of Things, …, Internet of Context) = “The Age of Context” 2) Hyper-Personalization (Location-aware, Digital exhaust, Social trails) 3) AI (not only Artificial, but Augmented & Assisted Intelligence) 4) Machine Intelligence (process automation, chatbots, Deep Learning in images, text, voice, and other complex data) 5) AR (Augmented Reality: in the field, emergency response, training for complex tasks, search & pick, gamification of learning, 3D data/info viz) 6) Behavioral Analytics (predictive and prescriptive modeling of human interests, intents, motivations, actions = Maslow’s hierarchy of needs?) 7) Graph Analytics (“All the world is a graph” = linked data, …) 8) Journey Sciences (people, processes, products, …) 9) The Experience Economy (Design Thinking for User, Customer, Employee) 10) Agile – DataOps (Incremental, Iterative, Fail-fast, Minimum Viable Product) 22 Top 10 Trends (in no particular order)
  23. 23. 1) IoT (Internet of Things, …, Internet of Context) = “The Age of Context” 2) Hyper-Personalization (Location-aware, Digital exhaust, Social trails) 3) AI (not only Artificial, but Augmented & Assisted Intelligence) 4) Machine Intelligence (process automation, chatbots, Deep Learning) 5) AR (Augmented Reality: in the field, training, logistics, 3D data/info viz) 6) Behavioral Analytics (predictive and prescriptive modeling of humans…) 7) Graph Analytics (“All the world is a graph” = linked data, the social graph, activity graph, product graph, interest graph, influence graph, … “connecting the dots that aren’t connected” = Anti-Money Laundering, Fraud Rings, Root Cause Analysis, Action Attribution, Marketing Attribution, …) 8) Journey Sciences (people, processes, products = data-to-insights for predictive and prescriptive decision-making and data-storytelling) 9) The Experience Economy (Design Thinking for User, Customer, Employee) 10) Agile – DataOps (Incremental, Iterative, Fail-fast, Minimum Viable Product) 23 Top 10 Trends (in no particular order)
  24. 24. 1) IoT (Internet of Things, …, Internet of Context) = “The Age of Context” 2) Hyper-Personalization (Location-aware, Digital exhaust, Social trails) 3) AI (not only Artificial, but Augmented & Assisted Intelligence) 4) Machine Intelligence (process automation, chatbots, Deep Learning) 5) AR (Augmented Reality: in the field, training, logistics, 3D data/info viz) 6) Behavioral Analytics (predictive and prescriptive modeling of humans…) 7) Graph Analytics (“All the world is a graph” = linked data, the social graph, activity graph, product graph, interest graph, influence graph, … “connecting the dots that aren’t connected” = Anti-Money Laundering, Fraud Rings, Root Cause Analysis, Action Attribution, Marketing Attribution, …) 8) Journey Sciences (people, processes, products = data-to-insights for predictive and prescriptive decision-making and data-storytelling) 9) The Experience Economy (Design Thinking for User, Customer, Employee) 10) Agile – DataOps (Incremental, Iterative, Fail-fast, Minimum Viable Product) 24 Top 10 Trends (in no particular order)
  25. 25. 1) IoT (Internet of Things, …, Internet of Context) = “The Age of Context” 2) Hyper-Personalization (Location-aware, Digital exhaust, Social trails) 3) AI (not only Artificial, but Augmented & Assisted Intelligence) 4) Machine Intelligence (process automation, chatbots, Deep Learning) 5) AR (Augmented Reality: in the field, training, logistics, 3D data/info viz) 6) Behavioral Analytics (predictive and prescriptive modeling of humans…) 7) Graph Analytics (“All the world is a graph” = linked data, the social graph, activity graph, product graph, interest graph, influence graph, … “connecting the dots that aren’t connected” = Anti-Money Laundering, Fraud Rings, Root Cause Analysis, Action Attribution, Marketing Attribution, …) 8) Journey Sciences (people, processes, products = data-to-insights for predictive and prescriptive decision-making and data-storytelling) 9) The Experience Economy (Design Thinking for User, Customer, Employee) 10) Agile – DataOps (Incremental, Iterative, Fail-fast, Minimum Viable Product) 25 Top 10 Trends (in no particular order)
  26. 26. 1) IoT (Internet of Things, …, Internet of Context) = “The Age of Context” 2) Hyper-Personalization (Location-aware, Digital exhaust, Social trails) 3) AI (not only Artificial, but Augmented & Assisted Intelligence) 4) Machine Intelligence (process automation, chatbots, Deep Learning) 5) AR (Augmented Reality: in the field, training, logistics, 3D data/info viz) 6) Behavioral Analytics (predictive and prescriptive modeling of humans…) 7) Graph Analytics (“All the world is a graph” = linked data, the social graph, activity graph, product graph, interest graph, influence graph, … “connecting the dots that aren’t connected” = Anti-Money Laundering, Fraud Rings, Root Cause Analysis, Action Attribution, Marketing Attribution, …) 8) Journey Sciences (people, processes, products = data-to-insights for predictive and prescriptive decision-making and data-storytelling) 9) The Experience Economy (Design Thinking for User, Customer, Employee) 10) Agile – DataOps (Incremental, Iterative, Fail-fast, Minimum Viable Product) 26 Top 10 Trends (in no particular order)
  27. 27. Top 10 Trends …delivering deeper insights from data for your next-best action (that’s Smart !) 1) IoT (Internet of Things, …, Internet of Context) = “The Age of Context” 2) Hyper-Personalization (Location-aware, Digital exhaust, Social trails) 3) AI (not only Artificial, but Augmented & Assisted Intelligence) 4) Machine Intelligence (process automation, chatbots, Deep Learning) 5) AR (Augmented Reality: in the field, training, logistics, 3D data/info viz) 6) Behavioral Analytics (predictive and prescriptive modeling of humans…) 7) Graph Analytics (“All the world is a graph” = linked data, …) 8) Journey Sciences (people, processes, products, …) 9) The Experience Economy (Design Thinking for User, Customer, Employee) 10) Agile – DataOps (Incremental, Iterative, Fail-fast, Minimum Viable Product) 27
  28. 28. OUTLINE • First came Data, then Big Data, now Smart Data • Data is the Fuel for AI and ML • Top Trends in Data Science, AI, and ML • Get Smart! 28 @KirkDBorne #DN2017
  29. 29. 29http://ghostednotes.com/category/semantic-web Chapters Indexes Covers Tablesof Contents Get Smart (Data)!
  30. 30. Get Smart (Data)! • Collect, Create, Connect smart data across your repositories! • Build Knowledge, not databases! … then exploit the top trends in AI and ML using Smart Data. 30http://ghostednotes.com/category/semantic-web Chapters Indexes Covers Tablesof Contents
  31. 31. Get Smart (Data)! • Collect, Create, Connect smart data across your repositories! • Build Knowledge, not databases! … then exploit the top trends in AI and ML using Smart Data. 31http://ghostednotes.com/category/semantic-web Chapters Indexes Covers Tablesof Contents https://www.quora.com/What-is-the-main-goal-of-semantic-web Query your data for Patterns (POI / BOI) & Knowledge

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