GS Caltex Corporation August 2012 R&D Center GS Caltex Corporation KOREA.
Peter Inge, Head of Actionable Insight, Caltex
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Transcript of Peter Inge, Head of Actionable Insight, Caltex
THE INSIGHTS VALUE CHAINDelivering Value as Data Scientist in a Commercial Organisation
@DataSciencePI
THE INSIGHTS VALUE CHAINAdvanced Analytical Techniques to derive Insights and Actionable Strategy to drive Performance and Value across the OrganisationTHE INSIGHTS VALUE CHAIN: combining a business problem with a data lab, cross functional team and agile to generate insightful analysis to solve a business problem. A process to create a product that resonates with management and delivers sustainable value.
IVC KEY POINTSDelivering value from Data Science requires multi-disciplinary cross functional collaboration across the organisation. Data Science drives Value when it is commercially alignedValue is sustained when a data driven culture is embedded into the fabric of the organisation.
PATH TO COMMERCIAL VALUEMAKING DATA SCIENCE WORK FOR YOUR ORGANISATION
Technology
Commercial Alignment
Data Culture
•Optimization•Prescriptive Analytics•Python•Deep Neural Nets•Event Stream Processing•Machine Learning•Self-Service Data Preparation•Data Lakes•Spark•Predictive Analytics•Hadoop-Based Data Discovery
https://www.gartner.com/doc/3388917/hype-cycle-data-science-
INSIGHTS VALUE CHAINPROVIDING COMMERCIAL ALIGNMENT
Strategic Data (Data Strategy, Data Governance, Data Culture) Hygiene
SponsorshipStrategic Objective &
Executive Sponsorship
AccountabilityOwnership &
Operational Execution
SolutionAdvanced Data, High
Performance Compute, Advanced Analytics, ML,
Measurement & Optimisation
AlignmentInsights, Strategies & Decision
Frameworks
SustainabilityData Custodian, Solution
Implementation, Deployment & Sustainability
Strategy & Leadership
The Business
Insights
Data Science
IT
These are the strategies for executing these outputs into your existing or augmented operating
model to deliver value
How are we going to answer the
question and inform the decision for the
business?
Business execute these strategies and AI helps measure the success of the initiatives and optimise.
These are the quantitative answers to the businesses
questions and decisions
This is how we answered the question, informed the
decision for the stakeholder
This is how we can/will support continued delivery and
optimisation
Who in the business is accountable for
execution?What Business
Question(s) are we answering?
What Decision are we influencing?
What is the Strategic
Objective?
Value
What needs and wants of our Customers are
we fulfilling?
This is how users in the business get sustained access
to insights and performance
The Customer
Needs & Wants
INSIGHTS VALUE CHAINTHE TOOLKIT – A FEW PIECES
Strategic Data (Data Strategy, Data Governance, Data Culture) Hygiene
SponsorshipStrategic Objective &
Executive Sponsorship
AccountabilityOwnership &
Operational Execution
SolutionAdvanced Data, High
Performance Compute, Advanced Analytics, ML,
Measurement & Optimisation
AlignmentInsights, Strategies & Decision
Frameworks
SustainabilityData Custodian, Solution
Implementation, Deployment & Sustainability
Strategy & Leadership
The Business
Insights
Data Science
ITThe Customer
Needs & Wants
SOQDOG Stakeholder Objective Question Decision Output Grain
Customer Wedge Need/Want/Willing Support Segment Intervention Impact
Stakeholder Hierarchy Sponsor Owner Consumer Responder
Insight Stories Process Mapping
Culture of Measurement
The Bank
Data Science Technical Method
INSIGHTS VALUE CHAINCHECKLIST – QUESTIONS I ASK MY TEAM FOR EVERY DATA
SCIENCE PROJECT
Does my Data Science Project meets the current or evolving Needs & Wants of the Customer?
Is my Data Science Project Strategically important to the Organisation? Is my project supported/sponsored by the Senior Executive? Who in the Business owns and is accountable for execution of the Insights and
Strategies that my Data Science project will generate? Are the Insights I’m generating going to be leveraged to support important
decisions? Can the business operationally execute the recommendations/strategies our Insights
are suggesting? Can I measure the performance of the recommendations reliably & sustainably? Can business users readily access the data and insights to support the decisions
they need to make? Can IT support the data sets and models that the Data Science will create?