Top Trends in Data Science and AI: Analytics By...

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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/

Transcript of Top Trends in Data Science and AI: Analytics By...

Page 1: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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/

Page 2: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

Analytics by Design – Summary

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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?

Page 3: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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/

Page 4: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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/

Page 5: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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)

Page 6: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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)

Page 7: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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)

Page 8: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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)

Page 9: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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)

Page 10: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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

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(in no particular order)

Page 11: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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)

Page 12: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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

Page 13: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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)

Page 14: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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)

Page 15: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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

Page 16: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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/

Page 17: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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

Page 18: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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/

Page 19: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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

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(Graphic by S. G. Djorgovski, Caltech)

Page 20: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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.

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Page 21: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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.

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Page 22: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

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?

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Page 23: Top Trends in Data Science and AI: Analytics By Designkirkborne.net/DataWest2017/KirkBorne-DataWest2017.pdf · 1) IoT (Internet of Things, Internet of Everything, Analytics of Things,

Analytics by Design – Posture & Principles

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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)