1

Kirk Borne @KirkDBorne

Principal Data Scientist

Booz Allen Hamilton

Journey Science for Better Experience (CX, UX, PX, DX, EX, …)

(Customer, User, Patient, Digital, Employee,…)

https://venturebeat.com/2015/12/29/these-5-marketing-tech-trends-will-be-huge-in-2016/ http://www.datasciencecentral.com/profiles/blogs/a-sneak-peek-at-the-future-of-artificial-intelligence-the-newes-1

Ever since we first explored our world…

http://www.livescience.com/27663-seven-seas.html

2

…We have asked questions about everything around us.

https://atillakingthehun.wordpress.com/2014/08/07/atlantis-not-lost/

3

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 growth)

4

https://www.linkedin.com/pulse/exponential-growth-isnt-cool-combinatorial-tor-bair

y ~ x! ≈ x ^ x → Combinatorial Growth! (all possible interconnections, linkages, and interactions)

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/

5

“All the World is a Graph” – Shakespeare?

(Graphic by Cray, for Cray Graph Engine CGE)

http://www.cray.com/products/analytics/cray-graph-engine

6

Simple Example of the Power of Graph: Semi-Metric Space

• Entity {1} is linked to Entity {2} (small distance A)

• Entity {2} is linked to Entity {3} (small distance B)

• Entity {1} is *not* linked directly to Entity {3} (Similarity Distance C = infinite)

• Similarity Distances between A, B, and C violate the triangle inequality!

{1} {3} {2}

7

• Entity {1} is linked to Entity {2} (small distance A)

• Entity {2} is linked to Entity {3} (small distance B)

• Entity {1} is *not* linked directly to Entity {3} (Similarity Distance C = infinite)

• Similarity Distances between A, B, and C violate the triangle inequality!

• The connection between black hat entities {1} and {3} never appears explicitly in a

link network, or within a transactional database.

• Examples: (a) Customer Journey modeling; (b) Safety Incident Causal Factor

Analysis; (c) Medical Research Discoveries across disconnected journals, through

linked semantic assertions; (d) Marketing Attribution Analysis; (e) Fraud networks,

Illegal goods trafficking networks, Money-Laundering networks.

{1} {3} {2}

Simple Example of the Power of Graph: Semi-Metric Space

8

360o View of the Aviation Customer Journey with Linked / Graph Data

… Omnichannel devices… … Travel reservation info…

… Context (Location, weather, and other contextual features)

… Demographics…

9

… FAA Open Data…

Customer Journey Science by Clickfox.com (the model predicts Customer outcomes)

10

Customer Journey Science by Clickfox.com (how it really looks to map the customer journey)

11 https://blog.clickfox.com/topic/wireless-telecom

Design Thinking and UX are at the heart of it! http://www.iotcentral.io/blog/user-experience-ux-is-at-the-heart-of-digital-transformation

Getting it Right – Learning from experience!

https://guycookson.com/2015/06/26/design-vs-user-experience/

12

So, the aviation industry has a choice: The usual way of doing things, or using data-driven predictive science!

13

https://www.linkedin.com/pulse/exponential-growth-isnt-cool-combinatorial-tor-bair

4 Types of Discovery from Data –

Using Algorithms that “learn from experience” 1) Class Discovery: Find the categories of objects

(population segments), events, and behaviors in your data. + Learn the rules that constrain the class boundaries (that uniquely distinguish them).

2) Correlation (Predictive and Prescriptive Power) Discovery: Find trends, patterns, and

dependencies in data, which reveal new governing principles or behavioral patterns (the “DNA”).

3) Novelty (Surprise!) Discovery: Find new,

rare, one-in-a-[million / billion / trillion] objects, events, and behaviors.

4) Association (or Link) Discovery: (Graph and

Network Analytics) – Find the unusual (interesting) co-occurring associations / links / connections.

14

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?)

(Follow the dots / connections in the graph!)

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.

15

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?

16

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’}.

Confucius says…

“Study your past to know

your future”

Predictive vs Prescriptive: What’s the Difference?

17

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’}.

Confucius says…

“Study your past to know

your future”

Baseball philosopher Yogi Berra says…

“The future ain’t what it

used to be.”

Predictive vs Prescriptive: What’s the Difference?

18

Context is King! “You can see a lot just by looking.” – Yogi Berra

• 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 Enterprise = predictive / prescriptive maintenance algorithm

alerts the corresponding asset, the right skilled technician, & the right

tool to converge at right place at the right time!

• Contextual data empowers both Prescriptive and Cognitive Analytics.

• Open Data + IoT sensor data provide a lot of context data (metadata!) 19

• The 7 V’s of Open Data: • VALIDITY (data quality, usability)

• VISIBILITY (exposing your data’s issues)

• VARIETY (heterogeneous types, formats)

• VOICE (to all stakeholders)

• VOCABULARY (data models, semantics)

• VULNERABILITY (open to everyone!)

• proVenance (lineage, chain of custody)

• The most important V is: • VALUE (innovation, insight creation)

Big Value from (Big) Open Data • The 3 V’s of Big Data:

• VOLUME

• VARIETY

• VELOCITY

• The most important V is: • VALUE

20 http://rocketdatascience.org/?p=410

The Journey Science Roadmap for Better Experience

• Design Patterns for Streaming Data Analytics: – Detecting POI (Person of Interest, or Pattern of Interest, or any Point of Interest)

– Detecting BOI (Behavior of Interest from any “dynamic actor”)

– Precomputed scenarios and their responses (to speed up “next best action”)

– Design Thinking : DX, UX, CX, EX (Digital / User / Customer / Employee eXperience)

• Edge Analytics (what else is happening now at the point of data collection?)

– Locality in Time

• Near-field Analytics (who / what is local to this person / place / thing?)

– Locality in Geospace

• Related-entity Analytics (what else is similar to this entity / event?)

– Locality in Feature Space

• Agile Analytics: DataOps, Fail-fast, Iterative, MVP, Learning Systems

Minimum Viable Product = your POV (Proof of Value)

21

@KirkDBorne

@BoozDataScience

LISTEN

READ, BUILD, and EXPLORE www.boozallen.com/datascience

Tips for Building a Data Science Capability

The Mathematical Corporation

10 Signs of Data Science Maturity

The Field Guide to Data Science

The Data and Analytics Catalyst

Explore: sailfish.boozallen.com

Booz | Allen | Hamilton

PARTICIPATE datasciencebowl.com

© Copyright 2017 Booz Allen Hamilton 22

Pre

-ord

er n

ow

…

The

Mat

hem

atic

al C

orp

ora

tio

n

http

://b

it.ly

/2p

TxjK

r