Data Enablement Lead

Overview

The Data Enablement Lead is accountable for driving the effective adoption and use of data, advanced analytics and AI across the enterprise, aligned to the strategic direction set by the Head of Data Culture. The role translates strategy into clearly defined enablement priorities, executable roadmaps and measurable outcomes that strengthen organisational capability, improve decision-making and accelerate business value realisation.

Requirements

• Bachelor's degree in Data Science, Information Systems, Business Analytics, Computer Science or a related field.
• 5+ years of experience in data enablement, data literacy, analytics enablement, AI adoption, digital learning, organisational change or a closely related field.
• Experience executing enterprise data culture, analytics adoption, AI enablement or digital adoption programmes with measurable outcomes.
• Experience leading cross-functional initiatives and influencing senior stakeholders without direct authority.
• Experience piloting, scaling and measuring capability-building or technology adoption interventions.

Responsibilities

• Translate the Data Culture, Analytics and AI strategy into clearly defined enablement priorities with agreed outcomes, success measures and delivery horizons.
• Define, maintain and execute roadmaps aligned to enterprise priorities, organisational maturity objectives and measurable business value.
• Continuously assess data, analytics and AI capability maturity to identify gaps, adoption barriers, risks and opportunities.
• Provide informed recommendations on trade-offs, sequencing, investment focus and optimisation of initiatives to maximise value delivery.
• Lead the design and delivery of integrated enablement interventions spanning data literacy and decision-readiness, advanced analytics skills and analytical thinking, and practical AI enablement and applied use cases.
• Drive adoption of relevant data products, analytics platforms, AI-assisted tools and supported ways of working across business teams.
• Ensure enablement initiatives are practical, scalable and outcome-focused, resulting in measurable capability uplift, behavioural change and sustained adoption.
• Embed responsible AI enablement into existing data and analytics frameworks, learning pathways and operating models.
• Translate complex technical concepts into accessible, business-relevant enablement tailored to different roles and maturity levels.
• Take end-to-end accountability for multiple concurrent initiatives, including scope, prioritisation, timelines, quality standards, dependencies and outcomes.
• Lead pilot initiatives, scale proven interventions, and discontinue or redesign initiatives that do not deliver sufficient value.
• Establish and maintain effective delivery cadence, governance structures, tracking mechanisms and reporting rhythms.
• Actively remove delivery impediments, resolve blockers and maintain momentum to ensure delivery at pace.
• Partner with senior leaders, product teams, data teams and business stakeholders to embed data-driven and AI-enabled decision-making into day-to-day ways of working.
• Identify barriers to adoption and implement targeted interventions that improve readiness, confidence, utilisation and ownership.
• Build and enable a network of champions, practitioners and communities of practice across data, analytics and AI domains to support scale and sustainability.
• Influence across functions without direct authority, using insight, evidence and outcomes to align decisions, behaviours and delivery commitments.
• Ensure enablement initiatives are owned, sustained and embedded by the business rather than remaining dependent on central enablement teams.
• Define and track clear success metrics for each initiative, including adoption, active utilisation, capability uplift, behavioural change, decision quality and business impact.
• Use data, feedback and delivery insight to continuously refine enablement models, learning pathways and adoption approaches.
• Provide concise, evidence-based reporting on progress, risks, decisions, adoption and realised value.
• Determine execution approaches, sequencing and delivery models within the agreed strategic direction, securing stakeholder alignment where required.
• Recommend changes to priorities, investment focus and course corrections based on delivery insight, maturity findings and business needs.
• Make day-to-day delivery, design and operational decisions within the defined scope and mandate of the role.

How to apply

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