Top Trends in Data Science and AI:
Analytics By Design Kirk Borne (on twitter @KirkDBorne)
Principal Data Scientist and Executive Advisor
Booz Allen Hamilton
Data West Senior Executive Forum, December 14, 2017
@KirkDBorne #DataWest 2017
https://www.authenticeducation.org/ubd/ubd.lasso
http://www.boozallen.com/datascience http://www.kirkborne.net/DataWest2017/
Analytics by Design – Summary
2
We will address these issues: • Should your organization be “Data-first”, “AI-first, “Data-driven”,
or “Technology-driven”?
• … or rather, shouldn’t your organization be Analytics-driven, Data-informed, and Technology-empowered?
• Analytics are the products and outcomes (= the ROI) of your Data, Data Science, AI, and Machine Learning investments!
• Focus on outcomes first (that’s why my Summary slide is here!)
• This focus explicitly induces the corporate messaging, strategy, and culture to be better aligned with what matters => Outcomes!
• Big Data should not be about “Big” volume, but Big Value!
• What are the top trends that can lead to ROI and Big Value?
Outline
• Top 10 Trends in AI and Data Science
• The CDO and Analytics
• Analytics by Design
@KirkDBorne #DataWest 2017
http://www.boozallen.com/datascience http://www.kirkborne.net/DataWest2017/
Outline
• Top 10 Trends in AI and Data Science
• The CDO and Analytics
• Analytics by Design
@KirkDBorne #DataWest 2017
http://www.boozallen.com/datascience http://www.kirkborne.net/DataWest2017/
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, Fail-fast, Iterative, Minimum Viable Product) 5
(in no particular order)
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, Fail-fast, Iterative, Minimum Viable Product) 6
(in no particular order)
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, Fail-fast, Iterative, Minimum Viable Product) 7
(in no particular order)
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, Fail-fast, Iterative, Minimum Viable Product) 8
(in no particular order)
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, Fail-fast, Iterative, Minimum Viable Product) 9
(in no particular order)
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, Fail-fast, Iterative, Minimum Viable Product)
Top 10 Trends
10
(in no particular order)
Top 10 Trends
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, …)
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, Fail-fast, Iterative, Minimum Viable Product) 11
(in no particular order)
Top 10 Trends
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, …)
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, Fail-fast, Iterative, Minimum Viable Product) 12
(in no particular order)
Decoding the “Entity’s DNA” = Ballistic Trajectory vs Impulse Forces
Top 10 Trends
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, …)
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, Fail-fast, Iterative, Minimum Viable Product) 13
(in no particular order)
Top 10 Trends
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, …)
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, Fail-fast, Iterative, Minimum Viable Product) 14
(in no particular order)
Top 10 Trends
delivering deeper insights
for next-best action (NBA) (delivering big value from big data!)
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, Fail-fast, Iterative, Minimum Viable Product) 15
Outline
• Top 10 Trends in AI and Data Science
• The CDO and Analytics
• Analytics by Design
@KirkDBorne #DataWest 2017
http://www.boozallen.com/datascience http://www.kirkborne.net/DataWest2017/
4 ACTIONS FOR THE CDO IN THEIR FIRST YEAR http://www.informationbuilders.com/about_us/whitepapers/download_form/25791
1) Increase Analytics Availability
2) Transform the Corporate Culture
3) Monetize Your Data
4) Promote Data Governance
… but… “don’t focus too heavily on data
governance, because you may spend
your first year doing nothing else. In
that case, you won’t have a second
year!” 17
Outline
• Top 10 Trends in AI and Data Science
• The CDO and Analytics
• Analytics by Design
@KirkDBorne #DataWest 2017
http://www.boozallen.com/datascience http://www.kirkborne.net/DataWest2017/
1) Class Discovery: Finding new classes of objects (population segments), events, and behaviors. This includes: learning the rules that constrain the class boundaries.
2) Correlation (Predictive and Prescriptive Power) Discovery: Finding patterns and dependencies, which reveal new governing principles or behavioral patterns (the “customer DNA”).
3) Novelty (Surprise!) Discovery:
Finding new, rare, one-in-a-[million / billion / trillion] objects and events.
4) Association (or Link) Discovery: Finding unusual (improbable) co-occurring associations.
Data Science – 4 Types of Discovery
19
(Graphic by S. G. Djorgovski, Caltech)
5 Levels of Analytics Maturity
in Data-Driven Applications 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 hypotheses.
20
5 Levels of Analytics Maturity
in Data-Driven Applications 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 hypotheses.
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’}.
Predictive vs Prescriptive: What’s the Difference?
22
Analytics by Design – Posture & Principles
23
Analytics Posture: Focus on Outcomes (Products) • Analytics-first ≠ Data-first (Data are the input; Analytics are the output)
• Focus on the products of Data Science, AI, and Machine Learning
• Examples of products: enriched data sets, curated open data, APIs, applications, models, data science notebooks, open source tools
• Products deliver ROI and Value from your data assets & top trends!
Principles of “Understanding by Design” for Analytics: 1) Identify Desired Results (outcomes, priorities, purpose, strategic objectives)
2) Determine Acceptable Evidence (data, KPIs, measurement instruments)
3) Plan and Design Activities (data products, data experiences, machine learning applications, areas for machine intelligence & automation)
4)
That’s “Understanding by Design” … which avoids twin problems:
“Following the hype” (FOMO) and “Activity-oriented” (not outcomes)