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?

PETER INGEHead Actionable Insights & [email protected]

@DataSciencePI