Data & AI/BI Manager

Data & AI/BI Manager

Purpose

The Data & AI / BI Manager is responsible for leading the local CAC cluster data, business intelligence, and AI-enabled analytics agenda.

This role combines:

• hands-on data ownership,
• business partnering,
• BI product ownership,
• and insight delivery,

to ensure that Customer, Brand, Sales, and Commercial teams can make faster, better, and more data-driven decisions.

This position represents the evolution of the business data, AI, and BI manager role, moving beyond static reporting toward ensuring that:

• data flows,
• data quality,
• business rules,
• and analytical tools

are robust enough to support both human decision-making and AI-augmented intelligence.

The manager anticipates business needs, identifies pain points and opportunities for data products, and leads improvements in analytics tools, BI processes, and reporting capabilities that support the daily work of Business Intelligence and commercial stakeholders.

This role is a primary driver of insights influencing sell-out and inventory decision-making, translating large internal and external commercial data assets into actionable recommendations.

The role owns and improves key BI tools, dashboards, business rules, data structures, and operational processes, while ensuring that trusted, activation-ready data is available for:

• performance management,
• commercial planning,
• and decision support.

For advanced analytics, modelling, and data mining use cases, the Data & AI / BI Manager collaborates closely with Advanced Analytics Home and/or CCG Home to:

• shape analytical questions,
• prepare business-ready datasets,
• validate outputs,
• and translate model results into practical insights for local business teams.

The role is not expected to operate in isolation, but to act as the local business and data anchor that connects CAC cluster needs with specialist advanced analytics capabilities.

This role operates with an AI-first mindset. It ensures data is:

• structured,
• documented,
• governed,
• and contextually enriched,

so that both BI dashboards and AI products deliver trusted, explainable, and actionable outputs.

This includes developing the habit of thinking beyond “is this data correct?” toward “can a person and a machine interpret this data accurately, consistently, and at scale?”

As the organization transitions from manual, request-driven analytics to self-service, automated, and AI-augmented intelligence, this role provides the local operational and analytical backbone by managing:

• data feeds,
• BI tools,
• taxonomy updates,
• remediation workflows,
• sell-out and inventory analytics,
• and AI-ready datasets,

in close collaboration with global data, advanced analytics, IT, and business teams.

Scope

Local / CAC cluster, with close collaboration with:

• Global Data teams
• Advanced Analytics Home
• CCG Home
• IT teams

Key Tasks / What You’ll Do

1. Anticipate business needs and develop data products

• Anticipate business needs and develop new analytical tools, dashboards, and AI-enabled data products that improve data-driven decision-making for the CAC cluster.
• Identify pain points and opportunities for data products, translating business requirements into scalable BI, analytics, and automation solutions.

2. Build and improve BI tools and processes

• Build, maintain, and improve analytics tools and processes that support the daily work of:
  • Business Intelligence,
  • Customer teams,
  • Brand teams,
  • Sales teams,
  • and Commercial teams.

3. Drive business insights

• Serve as a primary driver of insights that influence:
  • sell-out decisions,
  • inventory decisions,
  • and commercial performance actions.

4. Own data quality and business rules

• Ensure strong ownership of:
  • data flows,
  • data quality,
  • business rules,
  • taxonomy structures,
  • and reporting logic.
• Ensure data is reliable, business-ready, and activation-ready for decision-making.

5. Improve reporting and decision support capabilities

• Lead improvements in reporting capabilities, dashboards, and BI processes to support:
  • performance management,
  • commercial planning,
  • and operational decision support.

6. Collaborate on advanced analytics use cases

• Partner with Advanced Analytics Home and/or CCG Home for advanced analytics, modelling, and data mining initiatives.
• Help define analytical questions and business needs.
• Prepare and validate datasets and outputs.
• Translate model outputs into practical, business-relevant recommendations for local teams.

7. Enable AI-ready data foundations

• Ensure data is structured and managed in a way that supports both BI dashboards and AI-driven solutions.
• Promote explainable, trusted, and actionable outputs for both human and machine consumption.
• Support the shift toward self-service, automated, and AI-augmented intelligence.

8. Act as local business and data anchor

• Serve as the connection point between local CAC cluster needs and specialist global capabilities in:
  • data,
  • advanced analytics,
  • IT,
  • and commercial strategy.

9. Manage operational analytics backbone

• Support and manage the operational backbone for data and analytics, including:
  • data feeds,
  • BI tools,
  • taxonomy updates,
  • remediation workflows,
  • sell-out analytics,
  • inventory analytics,
  • and AI-ready datasets.

Executive Summary Version

If you want a shorter version for leadership review:

Position Summary

The Data & AI / BI Manager leads the local CAC cluster data, BI, and AI-enabled analytics agenda, ensuring that commercial stakeholders have trusted, scalable, and actionable data products to support decision-making.

This role combines business partnership, BI product ownership, insight generation, and data governance to move the organization from static reporting toward self-service, automated, and AI-augmented intelligence. The role acts as the local anchor between business teams and specialist global analytics capabilities.

Core Responsibilities

• Lead the local data, BI, and AI-enabled analytics agenda.
• Build and improve dashboards, data products, and reporting processes.
• Translate business needs into scalable analytics and automation solutions.
• Drive actionable insights for sell-out, inventory, and commercial decisions.
• Ensure strong data quality, business rules, and AI-ready data structures.
• Partner with advanced analytics teams to shape and operationalize modelling use cases.
• Support self-service and AI-first ways of working.