Demystifying Data Science

for more efficient

Government DataOps

KIRK BORNE Principal Data Scientist, Booz Allen Hamilton

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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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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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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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 DataOps, Data Science, 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 DataOps, Data Science, and Agile Analytics in a Data-Driven System

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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Thank you!

KIRK BORNE Principal Data Scientist Booz Allen Hamilton

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

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