Busting Big Data Myths with an Analytics-First StrategyBusting Big Data Myths with an Analytics-First Strategy KIRK BORNE Principal Data Scientist, Booz Allen Hamilton ... Demystifying

Busting Big Data Myths

with an Analytics-First

Strategy

KIRK BORNE Principal Data Scientist, Booz Allen Hamilton

Booz | Allen | Hamilton @KirkDBorne

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Find competitive

advantage for the

business with machine

learning and AI

Side-step the Big Data

hype bandwagon and

derive Big Value from

your data assets

Think Big, Start

Small, Learn Fast

with DataOps

Go for Analytics-First

by focusing on

purpose, products,

and outcomes

Adopt a Culture of

Experimentation

Acquire, nurture,

benefit from, and

retain key data

science talent

Machine Learning and AI are

big scary things

c

Data Science is a side project for data scientists

Data-first is the right strategic

posture for success

Three Responses Three Challenges Three Myths

Booz | Allen | Hamilton @KirkDBorne

Busting Big Data Myths – part 1:

Demystifying AI, Machine Learning, Data Science, and

DataOps

Data-informed , Analytics-driven

Innovation

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Booz | Allen | Hamilton @KirkDBorne

Source for graphic: https://www.forbes.com/sites/chunkamui/2016/01/03/6-words/

“The distinction between

success and failure

in innovation efforts

boils down to six words:

Think Big,

Start Small,

Learn Fast.”

- Chunka Mui

innovation advisor

Are you ready for DataOps? … Agile Data Science and a Fail-fast, Learn-fast Culture of Experimentation!

The Learn Fast

culture of DataOps

helps you to avoid

an episode of

“Data Oops!”

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Booz | Allen | Hamilton @KirkDBorne

DataOps – Agile Data Science

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… Incremental, Iterative, Continuous, Agile

… Nurtures a Culture of Experimentation

… Builds The Learning Organization

… Focus on POVs (Proofs of Value), not POC (proof of concept)

… Think Big, Start Small = the MVP (Minimally Viable Product)

and the MLP (Minimally Lovable Product)

… Fail-fast Learn-fast!

DataOps — DevOps for Data Analytics

https://oreil.ly/2zZWRvk

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Data Science: 4 Types of Discovery from Data! Which are you doing?

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 objects / events.

4)Association (or Link) Discovery: Finding unusual (“interesting”) co-occurring associations.

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Booz | Allen | Hamilton @KirkDBorne

5 Levels of Analytics Maturity in Data-intensive Applications

1) Descriptive Analytics

– Hindsight (What happened?)

– Asks the required questions.

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!)

5) Cognitive Analytics

– Right Sight (the 360 view; what is the right

action, right decision, right now, for this set

of data within this specific context.

– Moves beyond simply providing answers, to

generating new questions and hypotheses.

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Metaphorical Use Case of Data Science, AI, Machine Learning, DataOps and Agile Analytics in a Data-Driven System

The Mars Rover : • intelligent data-gatherer

• mobile data mining agent

• autonomous decision system • A self-driving “enterprise”

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Mars Rover:

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Metaphorical Use Case of Data Science, AI, Machine Learning, DataOps and Agile Analytics in a Data-Driven System

Booz | Allen | Hamilton @KirkDBorne

Busting Big Data Myths – part 2:

Becoming the Data and Analytics Catalyst

Data-informed , Analytics-driven

Innovation

10

Booz | Allen | Hamilton @KirkDBorne

Source for graphics: https://bit.ly/2zF2MUY

The Role of Data and Analytics Catalysts :

Be the agent of change in your organization!

Culture is the key ingredient to analytics success.

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The mature data science organization…

1) …democratizes all data and data access.

2) …uses Agile for everything and leverages DataOps.

3) ...leverages the crowd and works collaboratively (hackathons, etc.)

4) …follows rigorous scientific methodology (i.e., experimental, disciplined,…).

5) …attracts & retains diverse participants; grants them freedom to explore.

6) …relentlessly asks the right questions, and searches for the next one.

7) …celebrates a fast-fail collaborative culture.

8) …shows insights through illustrations and tells stories.

9) …builds proof of value, not proof of concepts.

10)…personifies data science as a way of doing things, not a thing to do.

How to Attract, Nurture, and Retain Key Talent

Booz | Allen | Hamilton @KirkDBorne

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Booz | Allen | Hamilton @KirkDBorne

Busting Big Data Myths – part 3:

Taking “Data to Action” for Big Value through “Analytics by Design”

Data-informed , Analytics-driven

Innovation

13

Booz | Allen | Hamilton @KirkDBorne

Analytics By Design – (a) Organizational Posture

Analytics-first Posture: Focus on Business Outcomes (Products) –

this focus explicitly induces the corporate messaging, culture, and

strategy to be better aligned with what matters => Outcomes!

Deliver business value from the products of Data Science, AI, and

Machine Learning – products deliver ROI and Value from your

data assets.

Examples of products: enriched data sets, curated open data,

APIs, applications, models, cloud services, models, data science

notebooks, open source tools, …

Analytics-first is not the same as Data-first. (Data are the input.

Analytics are the output.)

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Booz | Allen | Hamilton @KirkDBorne

#2: Identify Desired Results: outcomes, priorities, purpose,

strategic objectives

#3: Determine Acceptable Evidence (proofs): data, KPIs,

measurement instruments

#4: Plan and Design Activities: machine learning applications,

data experiences, data products, areas of AI and automation

#1: Adopt a Culture of Experimentation – “test or get fired!”

https://bit.ly/2JPFQIN

https://en.wikipedia.org/wiki/Understanding_by_Design

Analytics By Design – (b) Organizational Principles

Analytics By Design avoids the 2 biggest problems: (a) FTH due to

FOMO (Following The Hype due to Fear Of Missing Out); (b) Being

activity-oriented (i.e., focused on “busy work” instead of outcomes).

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16

Find competitive

advantage for the

business with machine

learning and AI

Side-step the Big Data

hype bandwagon and

derive Big Value from

your data assets

Think Big, Start

Small, Learn Fast

with DataOps

Go for Analytics-First

by focusing on

purpose, products,

and outcomes

Adopt a Culture of

Experimentation

Acquire, nurture,

benefit from, and

retain key data

science talent

Machine Learning and AI are

big scary things

c

Data Science is a side project for data scientists

Data-first is the right strategic

posture for success

Three Responses Three Challenges Three Myths

Booz | Allen | Hamilton @KirkDBorne

Nurture and empower your analytics talent within a culture of

experimentation: A data-driven experimental orientation (which is

the essence of Data Science and DataOps) is an essential

“innovation best practice.”

The organizational cultural change (including democratized data

access) that is required to adopt data science as a way of

doing things (and not just a thing to do) is perhaps a greater

challenge than the technological challenges.

Demonstrating value and ROI (Return On Innovation) from small

implementations and POVs (Proofs of Value) will inspire the

cultural change needed for the larger implementations that will

come.

Take-away Messages

Image Credit: Qubole

DataOps

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Booz | Allen | Hamilton @KirkDBorne

Thank you!

KIRK BORNE Principal Data Scientist Booz Allen Hamilton

@KirkDBorne https://bit.ly/2qbqa7l

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Adopting a culture of

experimentation is good data

science, and adopting an

analytics-first big data

strategy is good business.