Module 1
Analytics Strategy and Maturity
- Strategic decisions and analytics value pools
- Current-state maturity and capability assessment
- Demand, opportunities and pain points
- Strategic principles and target outcomes
Advanced and Strategic
Analytics Strategy, Operating Model and Value Realisation
Define analytics priorities, operating models, governance and value measures so capability scales beyond isolated reports and models.
Course overview
Turn analytics from a collection of projects into an enterprise capability.
Analytics Strategy, Operating Model and Value Realisation equips leaders to decide where analytics should create value, how work should be organised and governed, and how benefits should be evidenced.
Participants assess maturity, prioritise use cases, define roles and delivery models, structure portfolios, connect data and model governance with decision accountability, and design value-realisation measures.
The course is designed for organisations that need a coherent path from experimentation and local reporting to scalable analytical products and decision capability.
Learning outcomes
What participants will be able to do.
- Assess analytics maturity and strategic capability gaps.
- Translate strategic priorities into an analytics opportunity portfolio.
- Prioritise use cases using value, feasibility, risk and readiness criteria.
- Design centralised, federated or hybrid analytics operating-model options.
- Clarify product ownership, data, analytics and business roles.
- Establish governance and decision rights across the analytics lifecycle.
- Define adoption, outcome and value-realisation measures.
- Develop a phased analytics capability roadmap.
Who should attend
Designed for professionals who use data, analysis or evidence to improve decisions.
- Heads of analytics, BI and data functions.
- Strategy and transformation leaders.
- Data and AI programme leaders.
- Business-unit leaders sponsoring analytics investment.
- PMO, portfolio and capability-development professionals.
Prerequisites
Professionally relevant and application-focused.
Participants should have practical exposure to organisational analytics, BI, data or transformation initiatives. No specific technical platform is required.
Course structure
A five-Module journey from analytical understanding to workplace application.
The sequence may be delivered across five days or adapted to another approved format while preserving the learning outcomes and the five connected Modules.
Module 2
Use-Case Portfolio and Prioritisation
- Use-case framing and business sponsorship
- Value, feasibility, risk and readiness criteria
- Portfolio balance and sequencing
- Funding and stage-gate decision logic
Module 3
Analytics Operating Model
- Centralised, federated and hybrid models
- Business, data, analytics and product roles
- Intake, delivery and lifecycle processes
- Platforms, reusable assets and communities of practice
Module 4
Governance, Risk and Decision Accountability
- Data and model governance
- AI and analytical model risk
- Decision rights, human oversight and escalation
- Standards, assurance and lifecycle controls
Module 5
Value Realisation, Adoption and Roadmap
- Adoption and behaviour change
- Outcome and value measures
- Benefits tracking and learning loops
- Capability roadmap and executive action plan
Learning experience
Move from understanding to application, production and workplace value.
Understand
Connect concepts with business decisions
Clarify methods, assumptions, evidence requirements and the decision context before applying tools.
Apply
Work through realistic analytical cases
Use datasets, scenarios and decision questions to practise analytical judgement in context.
Produce
Create practical analytical outputs
Develop artefacts that can be adapted to reporting, modelling, governance or decision-support work.
Review
Challenge evidence and analytical choices
Use peer review, validation criteria and facilitated critique to improve analytical reasoning.
Transfer
Apply the learning at work
Identify how to adapt the methods to organisational data, decisions, systems and governance requirements.
Completion requirements
Demonstrate participation, application and professional judgement.
- Participate actively in case discussions, data exercises and analytical workshops.
- Complete the principal analytical or decision-support outputs assigned during the programme.
- Contribute to the integrated case, model, dashboard or application workshop.
- Complete knowledge checks and a workplace application or study plan.
Participant outputs
Leave with practical analytical artefacts.
- Analytics maturity assessment.
- Use-case portfolio and prioritisation matrix.
- Target operating-model canvas.
- Governance and decision-rights framework.
- Value-realisation scorecard.
- Phased analytics capability roadmap.
Delivery options
Select the learning format that fits your people and analytical environment.
Instructor-led
Live Classroom
Facilitated face-to-face learning with analytical cases, datasets, modelling tasks, discussion and immediate feedback.
Instructor-led
Live Virtual Classroom
Interactive online delivery using collaborative workspaces, data exercises, breakout analysis and guided model development.
Flexible
Blended Learning
A structured combination of preparation, live facilitation, applied assignments, analytical work and follow-up application.
Organisation-specific
Corporate and In-Company
Tailored delivery aligned with organisational datasets, measures, tools, governance, decisions and analytics maturity where appropriate.
Frequently asked questions
Course information and participation.
Is this a technical data-platform strategy course?
No. Technology is considered as an enabler, but the primary focus is analytics priorities, operating model, governance, portfolio choices and value realisation.
Is it suitable for business leaders as well as analytics leaders?
Yes. The course is designed around joint business-and-analytics accountability for priorities, decisions and outcomes.
Does the course cover AI governance?
Yes, where AI is part of the analytics portfolio. The focus is on proportionate governance, validation, oversight and decision accountability.
Can we use our current analytics roadmap?
Yes. Corporate delivery can use approved current-state materials to support assessment, prioritisation and roadmap refinement.
Does completion provide professional certification?
The course develops analytics-strategy and operating-model capability and may provide an INDENTRA course-completion record where applicable. It is not a third-party certification.
Take the next step
Scale analytics through clear priorities, governance and value evidence.
Discuss an organisation-specific programme aligned with your analytics maturity, operating model, portfolio and value-realisation priorities.