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TRANSCRIPT
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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