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.

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. 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. 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. Ever since we first explored our world…
http://www.livescience.com/27663-seven-seas.html
4

5. …We have asked questions about everything around us.
https://atillakingthehun.wordpress.com/2014/08/07/atlantis-not-lost/
5

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. 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. More data can help! … Or does it?
8

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. “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. 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
Smart Data Techniques and Applications
(from http://smartdata2017.dataversity.net)

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. 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. • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 29http://ghostednotes.com/category/semantic-web
Chapters
Indexes
Covers
Tablesof
Contents
Get Smart (Data)!

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. 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