Dr. Kirk Borne is a Principal Data Scientist at Booz Allen Hamilton. With a rich background in Astrophysics and Computational Science, he was a precursor on implementing courses of big data in academia. He is one of the most important promotors of data literacy in the world. About Kirk and his view on data literacy and evolution On his first visit to Brussels, Kirk first activity was sharing his best practices to promote data literacy. While enjoying a magnificent view of Brussels from the ING headquarter building, Kirk playfully (with a pair of socks!) explained how subjectivity plays a major role in the way that data is understood, derived by the wide variety of involved. This keynote was delivered at the speakers reception, which took place the day before the DI Summit. The following day, Kirk wrapped up the DI summit with his closing keynote on how data has shifted into something that is sense-making, following the evolution from “data” to “big data” into “smart data” composed by both enriched and semantic data and essential for IoT. He also discussed the levels of maturity in a self-driving enterprise, wrapping up his participation sharing this equation: Big Data + IoT + Citizen Data Scientists = Partners in Sustainability Kirk’s impression on the DI Summit was that it was a fun and informative event to join. His favorite format were the 5” pitches, as they were properly structured, providing the most critical information to the attendees. He also think that the networking dynamic ensured that all attendees met interesting people. A takeaway from Kirk’s presentation “Big data is not about how big it is, but the value you extract from it” We look forward to have Kirk sometime soon back in Brussels! Kirk’s interview: Kirk’s presentation recording: Kirk’s decks: Kirk’s presentation drawing: 2) Here are some video interviews that I have done: https://www.youtube.com/watch?v=ku2na1mLZZ8 https://www.youtube.com/watch?v=iXjvht91nFk Here is my TedX talk: https://www.youtube.com/watch?v=Zr02fMBfuRA

1. Kirk Borne
Principal Data Scientist, Booz Allen Hamilton
Data Innovation Summit
March, 30 2017
#DIS2017
A Data-rich World for a Better World:
From Sensors to Sense-Making

2. Principal Data Scientist
Booz Allen Hamilton
http://www.boozallen.com/datascience
Kirk Borne
@KirkDBorne
A Data-rich World for a Better World:
From Sensors to Sense-Making

3. OUTLINE
• First came Data, then Big Data, now Smart Data
• IoT: Dynamic Data-Driven Application Systems
• The Self-Driving Enterprise
• Steps to Discovery: Data Science & Analytics
• Case Study: From Sensors to Sense-Making
• Data Rich for a Better World
@KirkDBorne ---- #disummit, March 2017 ---- http://www.boozallen.com/datascience 3

4. OUTLINE
• First came Data, then Big Data, now Smart Data
• IoT: Dynamic Data-Driven Application Systems
• The Self-Driving Enterprise
• Steps to Discovery: Data Science & Analytics
• Case Study: From Sensors to Sense-Making
• Data Rich for a Better World
@KirkDBorne ---- #disummit, March 2017 ---- http://www.boozallen.com/datascience 4

5. Ever since we first explored our world…
http://www.livescience.com/27663-seven-seas.html 5

6. …We have asked questions about everything around us.
https://jefflynchdev.wordpress.com/tag/adobe-photoshop-lightroom-3/page/5/
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!
y ~ 2 * x (linear growth)
y ~ 2 ^ x (exponential)
7
https://www.linkedin.com/pulse/exponential-growth-isnt-cool-combinatorial-tor-bair
Big
Wow!
y ~ x! ≈ x ^ x
→ Combinatorial Growth!
(all possible interconnections,
linkages, and interactions)

8. Semantic, Meaning-filled Data:
• Ontologies (formal)
• 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
Making Sense of the World with Smart Data
The Human Connectome Project:
mapping and linking the major
pathways in the brain.
http://www.humanconnectomeproject.org/
8

9. OUTLINE
• First came Data, then Big Data, now Smart Data
• IoT: Dynamic Data-Driven Application Systems
• The Self-Driving Enterprise
• Steps to Discovery: Data Science & Analytics
• Case Study: From Sensors to Sense-Making
• Data Rich for a Better World
@KirkDBorne ---- #disummit, March 2017 ---- http://www.boozallen.com/datascience 9

10. Internet of
Things
https://www.nsf.gov/news/news_images.jsp?cntn_id=122028

11. Everything
Interconnected
https://www.nsf.gov/news/news_images.jsp?cntn_id=122028

12. 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 Dynamic
Data-Driven Application Systems (dddas.org) =>
Combinatorial Explosive Growth of Smart Data!
12
https://www.linkedin.com/pulse/can-elephant-bigdata-dance-iot-internet-things-tune-lambba

13. • 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!
Ubiquitous Smart Data in the IoT:
Applications and Use Cases are everywhere…
… that demands dynamic action = Edge Analytics
13

14. OUTLINE
• First came Data, then Big Data, now Smart Data
• IoT: Dynamic Data-Driven Application Systems
• The Self-Driving Enterprise
• Steps to Discovery: Data Science & Analytics
• Case Study: From Sensors to Sense-Making
• Data Rich for a Better World
@KirkDBorne ---- #disummit, March 2017 ---- http://www.boozallen.com/datascience 14

15. What is Self-Driving? … ~Autonomous!
(e.g., cars, cities, deep space probes, organizations)
Defining Characteristics Data Analytics Maturity Organizational Posture
Sensing, Streaming
Responsive
Learning, Agile
Contextual, Optimizing
Deciding, Acting
Note: additional KEY CHARACTERISTICS = Stateful, Secure, Compliant, “Embedded Reporting”
= explosive growth of “in situ” data that delivers CONTEXT and SENSE-MAKING
15

16. Defining Characteristics Data Analytics Maturity Organizational Posture
Sensing, Streaming Descriptive
Responsive Diagnostic
Learning, Agile Predictive
Contextual, Optimizing Prescriptive
Deciding, Acting Cognitive
16
What is Self-Driving? … ~Autonomous!
(e.g., cars, cities, deep space probes, organizations)
Note: additional KEY CHARACTERISTICS = Stateful, Secure, Compliant, “Embedded Reporting”
= explosive growth of “in situ” data that delivers CONTEXT and SENSE-MAKING

17. Defining Characteristics Data Analytics Maturity Organizational Posture
Sensing, Streaming Descriptive Passive
Responsive Diagnostic Reactive
Learning, Agile Predictive Proactive
Contextual, Optimizing Prescriptive Predictive Reactive
Deciding, Acting Cognitive Sense-making
17
What is Self-Driving? … ~Autonomous!
(e.g., cars, cities, deep space probes, organizations)
Note: additional KEY CHARACTERISTICS = Stateful, Secure, Compliant, “Embedded Reporting”
= explosive growth of “in situ” data that delivers CONTEXT and SENSE-MAKING

18. OUTLINE
• First came Data, then Big Data, now Smart Data
• IoT: Dynamic Data-Driven Application Systems
• The Self-Driving Enterprise
• Steps to Discovery: Data Science & Analytics
• Case Study: From Sensors to Sense-Making
• Data Rich for a Better World
@KirkDBorne ---- #disummit, March 2017 ---- http://www.boozallen.com/datascience 18

19. Levels of Maturity in the
Self-Driving Enterprise
Defining Characteristics Data Analytics Maturity Organizational Posture
Sensing, Streaming Descriptive Passive
Responsive Diagnostic Reactive
Learning, Agile Predictive Proactive
Contextual, Optimizing Prescriptive Predictive Reactive
Deciding, Acting Cognitive Sense-making
19

20. 5 Levels of Analytics Maturity
1) Descriptive Analytics
– Hindsight (What happened?)
2) Diagnostic Analytics
– Oversight (real-time / What is
happening? Why did it happen?)
3) Predictive Analytics
– Foresight (What will happen?)
4) Prescriptive Analytics
– Insight (How can we optimize what
happens?)
5) Cognitive Analytics
– Right Sight (the 360 view , what is the
right question to ask for this set of data in
this context = Game of Jeopardy)
– Finds the right insight, the right action, the
right decision,… right now!
– Moves beyond simply providing answers, to
generating new questions and new
hypotheses, for your “next-best move”
20
@KirkDBorne ---- #disummit, March 2017 ---- http://www.boozallen.com/datascience

21. PREDICTIVE
Analytics
Find a function (i.e., the model) f(d,t) that
predicts the value of some predictive
variable y = f(d,t) at a future time t, given
the set of conditions found in the training
data {d}.
=> Given {d}, find y.
PRESCRIPTIVE
Analytics
Find the conditions {d’} that will produce a
prescribed (desired, optimum) value y at a
future time t, using the previously learned
conditional dependencies among the
variables in the predictive function f(d,t).
=> Given y, find {d’}.
21
@KirkDBorne ---- #disummit, March 2017 ---- http://www.boozallen.com/datascience

22. Context is King!
“It’s not what you look at that matters – it’s what you see.” – Henry David Thoreau
• Context is “other data” about your data = i.e., Metadata!
• The 3 most important things in your data are: Metadata, Metadata,
Metadata!
• Metadata are…
– Other Data that describes Other Data
– Other Data that describes Your Data
– Your Data that describes Other Data
• e.g., Connected “Smart” Cars = that car that is braking 3 vehicles
ahead of you = informs your vehicle to brake now!
• The Smart Business = predictive maintenance algorithm alerts the
corresponding asset, the right skilled technician, & the right tool to
converge at right place at the right time!
• IoT sensor data can provide a lot of context data (metadata!) 22

23. OUTLINE
• First came Data, then Big Data, now Smart Data
• IoT: Dynamic Data-Driven Application Systems
• The Self-Driving Enterprise
• Steps to Discovery: Data Science & Analytics
• Case Study: From Sensors to Sense-Making
• Data Rich for a Better World
@KirkDBorne ---- #disummit, March 2017 ---- http://www.boozallen.com/datascience 23

24. Case
Study:
Mars
Rovers
(metaphor)
24@KirkDBorne ---- #disummit, March 2017 ---- http://www.boozallen.com/datascience

25. Mars Rover: intelligent data-gatherer, mobile data mining
agent, autonomous decision-support system, and self-driving!
1) Drive around surface of Mars – take samples of items (Mars rocks):
• experimental data type = mass spectroscopy = data histogram = feature vector
2) Perform Intelligent Data Operations (Data Mining in Action):
• Supervised Learning
– Search for items with known characteristics, and assign items to known classes
• Unsupervised Learning
– Discover what types of items are present, without preconceived biases; find the set of
unique classes of items; discover unusual associations; discover trends and new directions
(new leads) to follow
• Semi-supervised Learning
– Find the rare, one-of-kind, most interesting items, behaviors, contexts, or events
3) Enact On-board Intelligent Data Understanding & Decision Support
= Science Goal (or Business Goal) Monitoring:
– “stay here and do more” ; or else “follow trend to a more interesting location”
– “send discoveries to human analysts immediately” ; or else “send results later”
25

26. Smart Sensors & Sentinels for Data-Driven
Sense-Making and Decision Support
• New knowledge and insights are acquired by monitoring and mining
actionable data from all digital inputs (Sensors!)
• Alerts are triggered autonomously, without intervention (if permitted),
applying machine learning and actionable business decision rules for
pattern detection and diagnosis. (Sentinels! = embedded machine
learning / data science algorithms, at the point of data collection =
trained to minimize False Positives and “Alarm Fatigue”)
• “Smart Sensors” (powered by Machine Learning-enabled sentinels) will
therefore deliver actionable intelligence (Sense!)
From Sensors to Sentinels to Sense
(for any application domain with streaming data from sensors)
@KirkDBorne ---- #disummit, March 2017 ---- http://www.boozallen.com/datascience 26

27. The MIPS Analytics Framework
for Dynamic Data-Driven Application Systems (DDDAS)
• MIPS =
– Measurement – Inference – Prediction – Steering
• This applies to any Network of Sensors:
– Web user interactions & actions (web analytics data), Cyber network usage logs, Customer
Service interactions, Social network sentiment, Machine logs (of any kind), Manufacturing
sensors, Health & Epidemic monitoring systems, Financial transactions, National Security, Utilities
and Energy, Remote Sensing, Tsunami warnings, Weather/Climate events, Astronomical sky
events, …
• Machine Learning enables the “IP” part of MIPS:
– Autonomous (or semi-autonomous) Classification
– Intelligent Data Understanding
– Rule-based
– Model-based
– Neural Networks
– Markov Models
– Bayes Inference Engines
Alert & Response systems:
• Actionable insights from
streaming sensor data
• Automation of any data-driven
operational system
http://dddas.org

28. The MIPS Analytics Framework
for Dynamic Data-Driven Application Systems (DDDAS)
• MIPS =
– Measurement – Inference – Prediction – Steering
• This applies to any Network of Sensors:
– Web user interactions & actions (web analytics data), Cyber network usage logs, Customer
Service interactions, Social network sentiment, Machine logs (of any kind), Manufacturing
sensors, Health & Epidemic monitoring systems, Financial transactions, National Security, Utilities
and Energy, Remote Sensing, Tsunami warnings, Weather/Climate events, Astronomical sky
events, …
• Machine Learning enables the “IP” part of MIPS:
– Autonomous (or semi-autonomous) Classification
– Intelligent Data Understanding
– Rule-based
– Model-based
– Neural Networks
– Markov Models
– Bayes Inference Engines
http://dddas.org
Alert & Response systems:
• Actionable insights from
streaming sensor data
• Automation of any data-driven
operational system

29. OUTLINE
• First came Data, then Big Data, now Smart Data
• IoT: Dynamic Data-Driven Application Systems
• The Self-Driving Enterprise
• Steps to Discovery: Data Science & Analytics
• Case Study: From Sensors to Sense-Making
• Data Rich for a Better World
@KirkDBorne ---- #disummit, March 2017 ---- http://www.boozallen.com/datascience 29

30. Big Data + IoT + Citizen Data Scientists = Partners in Sustainability
http://www.unglobalpulse.org
Sustainability Development Goals
Knowing the knowable via deep, wide,
and fast data from ubiquitous sensors!
The Internet of Things (IoT): Big Data:
In the Big Data era, Everything is
Quantified and Tracked :
– Populations & Persons
– Smart Cities, Farms, Highways
– Environmental Sensors
– IoE = Internet of Everything!
Discovery through Data Science:
– Class Discovery
– Correlation Discovery
– Novelty Discovery
– Association Discovery
17 SDGs are KPIs for the World!
(currently, the SDGs have 229
key performance indicators)
30

31. Big Data + IoT + Citizen Data Scientists = Partners in Sustainability
Sustainability Development Goals
Knowing the knowable via deep, wide,
and fast data from ubiquitous sensors!
The Internet of Things (IoT): Big Data:
In the Big Data era, Everything is
Quantified and Tracked :
– Populations & Persons
– Smart Cities, Farms, Highways
– Environmental Sensors
– IoE = Internet of Everything!
Discovery through Data Science:
– Class Discovery
– Correlation Discovery
– Novelty Discovery
– Association Discovery
17 SDGs are KPIs for the World!
(currently, the SDGs have 229
key performance indicators)
31
**********
The Art & Science of being Data-Rich :
From Sensors to a Better World
**********
@KirkDBorne ---- #disummit, March 2017 ---- http://www.boozallen.com/datascience