Quick Navigation
Project Overview AI Enablement Plan Interim Organisation Training Requirements Hiring Needs Implementation Roadmap Long-Term Team Structure Core Assumptions & Risks Data & AI Function

Proposal: Project Lantern Delivery Plan

This document outlines the proposal and implementation plan for Project Lantern, responding to the requirement to deliver a comprehensive self-serve reporting capability for iD Mobile by end of June. The plan addresses all deliverables specified in the brief, including dataset engineering for all function areas, comprehensive self-service data models with full documentation (topics, descriptions, measures), and the concurrent definition of the long-term team structure.

Key Question Addressed: What additional contract resources are required to deliver Project Lantern alongside continuing current deliverables and agreed prioritised projects?

Answer: This proposal details the contract resources needed, including Omni specialists, training resources, CI/CD expertise, and support capabilities, alongside one permanent Full-Time FTE position (Senior Tech Product Manager) for product delivery management covering both Lantern delivery and BAU sustainment, working with central change teams on demand sizing and roadmap adherence.

Project Lantern One-Off Delivery Costs

This section outlines the one-off contract resource costs required to deliver Project Lantern by end of June. The costs exclude the permanent Full-Time FTE position (Senior Tech Product Manager), which is a permanent hire and not a one-off project cost.

Loading cost data...

Enablement Credits: As part of vendor enablement plans, we may receive free enablement support days that reduce billable costs:

  • Omni Enablement Credits: Up to 20 days per Omni resource (Omni Specialists and Self-Serve Support Specialist)

These credits have been included in the cost calculations above, reducing the billable days for applicable contract roles. The CI/CD Training Specialist engagement is a shorter, focused 20-30 day engagement and does not include enablement credits.

Day Rates: Day rates are estimates based on market rates for contract specialists and FTC positions. Senior FTC roles (Senior Analytics Engineers and Senior Data Engineer) are priced at £700/day, aligned with market rates from the 2024/2025 salary guide (Analytics Engineer: £500-£750/day, Senior Data Engineer: £500-£750/day). This rate reflects senior-level expertise required to deliver at pace in this short sprint without extensive onboarding. The higher rate ensures we can attract candidates who can hit the ground running immediately. The Senior Tech Product Manager (FTE) is a permanent position and is not included in the one-off delivery costs above.

Ongoing Cost: Senior Tech Product Manager (FTE)

The Senior Tech Product Manager is a permanent Full-Time FTE position that covers both Project Lantern delivery and ongoing BAU sustainment. This role is not a one-off project cost but represents an ongoing operational expense.

Role Details:

  • Position: Senior Tech Product Manager (Full-Time FTE)
  • Start Date: 26-Jan-25
  • Duration: Permanent position (ongoing)
  • Scope: Dual responsibility covering both Project Lantern delivery and BAU sustainment
  • Reporting: Reports to Senior BI Manager
  • Cost Type: Ongoing operational cost (not included in one-off delivery costs)

Note: The salary and benefits for this permanent FTE position should be budgeted as an ongoing operational expense separate from the one-off Project Lantern delivery costs. This role will continue as a permanent Senior Tech Product Manager post-Lantern, ensuring continuity of delivery management beyond the project completion.

Project Overview

Project Lantern is a strategic initiative to deliver a comprehensive self-serve reporting and analytics capability on the Omni Analytics platform for iD Mobile. The project aims to empower each business function with on-demand access to critical business insights, replacing current manual reporting processes and enabling data-driven decision making at scale.

Strategic Mission: To establish a world-class self-serve analytics capability that enables business functions to answer their own questions on-demand, reducing dependency on centralised reporting teams and accelerating time-to-insight. The platform will be adaptable to evolving question types and concepts, ensuring long-term value as business needs change.

Key Objectives

📊 Self-Serve Capability

Enable each function to independently access and analyse data through Omni Analytics without requiring technical support, with the flexibility to answer questions of relevant types as they evolve.

🤖 AI Enablement

Leverage AI capabilities within Omni to provide natural language querying and automated analysis that adapts to changing question patterns.

📈 Function Coverage

Deliver comprehensive reporting capabilities across initial function areas (Sales, CRM, Customer CMC Data, Customer Feedback, Base Reporting, Customer Workflow, and Financials), with the platform designed to accommodate additional functions as needed.

📚 Adaptable Data Models

Comprehensive self-service data models with topics, clear descriptions, full measures, and complete metadata, designed to be flexible and adaptable to evolving question types and business needs.

Function Areas in Scope

Project Lantern will deliver self-serve reporting capabilities for initial function areas, with each function receiving engineered datasets and the ability to answer questions of relevant types on-demand. The approach focuses on understanding question types and concepts rather than building static answers to specific questions, ensuring the platform can adapt as business questions evolve.

Note: The function areas listed above are the initial scope for Project Lantern delivery. This list is not exhaustive, and additional function areas may be added as the platform evolves and business needs expand. The platform architecture is designed to be extensible, allowing new function areas to be onboarded efficiently.

Function-Specific Question Development

For each function area, we will work with stakeholders to identify and understand the types of questions they ask as concepts, exploring what it would take for such questions to be answered on demand. This process is not about building specific answers to fixed questions, but rather about creating an adaptable platform that can answer questions of these types as they evolve over time.

Key Principle: Example questions are likely to be different each period of time, so we need an adaptable platform rather than putting effort into answering specific questions. The focus is on building flexible data models and self-serve capabilities that can handle question types and concepts, not individual question instances.

This process involves:

This approach ensures that Project Lantern delivers a flexible, adaptable platform that can answer questions of relevant types on demand, rather than building static solutions for specific questions that may change over time. The platform's adaptability ensures long-term value as business questions evolve.

AI Enablement Plan for Omni

Project Lantern will leverage Omni Analytics' AI capabilities to transform how business users interact with data. This plan outlines the comprehensive approach to AI enablement across the platform.

AI Capabilities to Enable

Natural Language Querying
Enable users to ask questions in plain English and receive instant answers from the data.
  • Implement conversational AI interface
  • Update semantic model with appropriate AI context for Omni agent to understand (documentation/governance, not model training)
  • Provide query suggestions and autocomplete
AI-Generated Commentary
Enable AI to automatically generate accurate, contextual commentary on reports and dashboards to help users understand insights.
  • Develop best practices for AI commentary generation
  • Configure AI context for accurate report interpretation
  • Train AI on business terminology and reporting standards
  • Ensure commentary aligns with business context and requirements
Automated Report Generation
Automatically generate reports and dashboards based on user requirements and data patterns.
  • Template-based report generation
  • Custom report builder with AI assistance
  • Automated data visualisation
  • Smart layout optimisation

Implementation Phases

Phase 1: Foundation (Months 1-2)

Establish Omni platform infrastructure, basic AI capabilities, and initial dataset engineering for priority functions.

Phase 2: Core AI Features (Months 3-4)

Implement natural language querying, develop AI context for accurate question answering, and establish best practices for AI-generated commentary on reports.

Phase 3: Advanced Capabilities (Months 5-6)

Optimise AI context, refine natural language querying, and deploy automated commentary generation across all function areas.

Interim Organisation Structure for Self-Serve Enablement

The interim organisational structure builds upon the existing current Data & AI Function structure to support Project Lantern delivery. This structure includes a Senior Tech Product Manager (Full-Time FTE) who manages both Lantern delivery and BAU sustainment, working with central change teams on demand sizing and roadmap adherence, plus Omni specialists, training resources, and additional capacity to ensure successful self-serve enablement.

Key Principle: All new Project Lantern roles report to FTE positions. The Senior Tech Product Manager (Full-Time FTE) manages both Project Lantern delivery and BAU sustainment, working with central teams to size demands, maintain an agreed roadmap, and ensure adherence to delivery commitments.

Interim Organisational Chart for Project Lantern

Based on current Data & AI Function structure with Project Lantern additions.

graph TD; %% Current Organisational Structure (Level 1) A["Senior BI Manager
Ben White"] --> D["Senior Analytics Engineer
Anil Asher"] A --> E["Data Scientist
Anastasia Veselova"] A --> G["Data Lead
Megha Latha"] A --> LANTERN["Senior Tech Product
Manager
(Full-Time FTE)"] %% Existing Reports to Senior Analytics Engineer D --> H["Analytics Engineer
James Nagel"] %% Reports to Data Lead G --> I["Report Developer
Ramya Paduchuri"] G --> J["Report Developer
Sai Teja Gadiyaram"] G --> L["Azure Data Engineer
Chaitanya Annavarapu"] G --> M["Azure Data Engineer
Subham Kumar"] %% Project Lantern Additions - All report to FTE roles %% Reports to Senior Tech Product Manager LANTERN --> SUPPORT["Self-Serve Support
(Contract)"] LANTERN --> OMNI2["Omni Specialist
(Contract)"] LANTERN --> OMNI3["Omni Specialist
(Contract)"] %% Reports to Senior Analytics Engineer (FTE) D --> LANTERN_AE1["Senior Analytics Engineer
(Lantern Focus - FTC)"] D --> LANTERN_AE2["Senior Analytics Engineer
(Lantern Focus - FTC)"] D --> LANTERN_DE1["Senior Data Engineer
(Lantern Focus - FTC)
CCaaS Chat Data"] %% Custom styling classDef manager fill:#10A45E,stroke:#0E6E40,stroke-width:3px,color:#fff,font-weight:bold classDef fteRole fill:#1ADE82,stroke:#10A45E,stroke-width:2px,color:#232323,font-weight:600 classDef infosysRole fill:#F3F208,stroke:#0E6E40,stroke-width:2px,color:#232323,font-weight:600 classDef vacantRole fill:#FF6B6B,stroke:#CC0000,stroke-width:2px,color:#fff,font-weight:600 classDef lanternRole fill:#FFA500,stroke:#FF8C00,stroke-width:2px,color:#232323,font-weight:600 classDef contractRole fill:#9370DB,stroke:#7B68EE,stroke-width:2px,color:#fff,font-weight:600 %% Apply classes class A manager class D,E,H,LANTERN fteRole class G,I,J,L,M infosysRole class OMNI2,OMNI3,SUPPORT,LANTERN_AE1,LANTERN_AE2,LANTERN_DE1 contractRole
Senior BI Manager
FTE Roles
Infosys Roles
Contract/Consultant Roles

Hiring Needs

Project Lantern requires additional capacity and specialised skills to ensure successful delivery. This section outlines the hiring requirements for both permanent and contract positions.

Hiring Summary

1
Senior Tech Product Manager (Full-Time FTE)
2
Omni Specialists (Contract)
1
CI/CD Training Specialist (Contract)
1
Support Specialist (Contract)
2
Senior Analytics Engineers (FTC)
1
Senior Data Engineer (FTC)

Senior Tech Product Manager (Full-Time FTE)

Senior Tech Product Manager (Full-Time FTE) - 1 Position
Type: Permanent Full-Time FTE | Start: Month 1
Scope: Permanent Full-Time FTE role responsible for managing both Project Lantern delivery and BAU sustainment. Works with the data team and central change team to size demands, maintain an agreed roadmap, and ensure adherence to delivery commitments while coordinating resources for successful self-serve enablement on Omni Analytics platform.
  • Manage and prioritise Project Lantern delivery and milestones while ensuring BAU activities are sustained
  • Work with central change team and data team to size demands, maintain agreed roadmap, and ensure adherence to delivery commitments
  • Coordinate contract resources and Omni specialists for Lantern delivery
  • Facilitate communication between technical teams and business stakeholders
  • Manage demand, requirements, and prioritisation from function areas
  • Track and report on project delivery progress and metrics for both Lantern and BAU
  • Strong skills in calling out issues, pushing teams when needed, and ensuring roadmap adherence
  • Ensure smooth transition to permanent Data & AI Function structure (continues as permanent Senior Tech Product Manager post-Lantern)

Interim Role Descriptions

Omni Specialist (Contract) x2
Technical specialists with deep expertise in Omni Analytics platform, responsible for training Senior Analytics Engineers and Report Developers through joint working on data models, as well as implementing AI capabilities including natural language querying and AI-generated commentary. Models will be owned by Senior Analytics Engineers and Report Developers once Omni specialists leave, ensuring knowledge transfer and sustainable capability.
  • Train Senior Analytics Engineers and Report Developers through joint working on data models
  • Work collaboratively with Senior Analytics Engineers on tables, relationships, and fields
  • Guide Report Developers in using tables and relationships, documenting how data works together in metric views/Omni topics
  • Support Report Developers in creating measures and dimensions from existing tables and fields
  • Develop comprehensive AI context to ensure accurate answers to user questions
  • Configure and optimise natural language querying capabilities
  • Write and document best practices for AI to generate accurate commentary on reports
  • Train AI context on business terminology and domain-specific knowledge
  • Enable automated commentary generation
  • Ensure data models are properly structured for self-serve consumption with comprehensive documentation
  • Transfer ownership of models to Senior Analytics Engineers and Report Developers
  • Configure platform settings and permissions for self-serve access
  • Optimise data models and query performance
Self-Serve Support (Contract)
Support specialist working with stakeholders to train and assess their capability to self-serve, providing user assistance, troubleshooting, and guidance to business users adopting self-serve capabilities on Omni.
  • Work with stakeholders to train and assess their capability to self-serve
  • Provide user support and troubleshooting
  • Answer questions and provide guidance
  • Assess stakeholder self-serve capability and identify training needs
  • Document common issues and solutions
  • Gather user feedback for improvements
  • Escalate technical issues to appropriate teams
  • Contribute to knowledge base and FAQs

Contract Positions (6-Month Duration)

Omni Specialist (Contract) - 2 Positions
Duration: ~3 months (70 working days) | Start: Month 1
Focus: Training Senior Analytics Engineers and Report Developers through joint working on data models, plus implementing AI capabilities including natural language querying and AI-generated commentary
  • Expert-level Omni Analytics platform knowledge including AI capabilities
  • Train Senior Analytics Engineers and Report Developers through joint working on data models
  • Develop training curriculum and materials for team members
  • Deliver hands-on training sessions and workshops
  • Work collaboratively with Senior Analytics Engineers on tables, relationships, and fields
  • Guide Report Developers in using tables/relationships and documenting data in metric views/Omni topics
  • Support Report Developers in creating measures and dimensions from existing tables and fields
  • Develop comprehensive AI context to ensure accurate answers to user questions
  • Configure and optimise natural language querying capabilities
  • Write and document best practices for AI to generate accurate commentary on reports
  • Train AI context on business terminology and domain-specific knowledge
  • Enable automated commentary generation
  • Ensure knowledge transfer so models are owned by Senior Analytics Engineers and Report Developers
  • Strong experience creating self-service data models with proper topic definitions
  • Understanding of self-serve analytics best practices
  • Ability to work across multiple function areas
Senior Analytics Engineer (Lantern Focus - FTC) - 2 Positions
Type: Fixed Term Contract (FTC) | Start: Month 1
Level: Senior (must hit the ground running in short sprint delivery)
Potential: Long-term role opportunity post-Lantern
  • Senior-level expertise required to deliver at pace without extensive onboarding
  • Work jointly with Omni Specialists and Report Developers on complex data models
  • Lead dataset engineering: creating tables, relationships, and fields with minimal guidance
  • Create and optimise new relationships and fields within data models
  • Design and implement data assets for self-serve consumption with best practices
  • Take ownership of data models from Omni Specialists and drive forward independently
  • Rapidly learn Omni Analytics platform and contribute immediately to delivery
  • Expert-level data transformation and modelling skills with modern data platforms
  • Proven track record delivering complex analytics projects under tight timelines
  • Document data lineage, transformations, and best practices for team knowledge
  • Mentor and guide other team members on data modelling approaches
Senior Data Engineer (Lantern Focus - FTC) - 1 Position
Type: Fixed Term Contract (FTC) | Start: Month 1
Level: Senior (must hit the ground running in short sprint delivery)
Focus: Introduce missing contact centre data, specifically chat data from the CCaaS (Contact Centre as a Service) platform, connecting to the data source and bringing it into Databricks for use in self-serve reporting.
  • Senior-level expertise required to deliver at pace without extensive onboarding
  • Connect to CCaaS platform to extract chat data and other contact centre data not yet in the data platform
  • Efficiently bring contact centre data into Databricks from the CCaaS platform with minimal supervision
  • Design and build robust, scalable data pipelines for CCaaS chat data integration
  • Implement enterprise-grade ETL/ELT processes for contact centre data sources
  • Establish data quality frameworks and validation processes for CCaaS data
  • Document CCaaS data source connections, transformations, and architectural decisions
  • Collaborate effectively with Senior Analytics Engineers to ensure contact centre data is available for modelling
  • Proven experience with Databricks, Azure Data Factory, CCaaS platforms, and modern data engineering tools
  • Ability to troubleshoot complex data integration challenges independently
  • Understanding of contact centre operations and chat data structures
CI/CD & Repository Training Specialist (Contract) - 1 Position
Duration: 20-30 days | Start: After FTC resources are onboarded (Month 1-2)
  • Expert-level knowledge of Git repositories and version control best practices
  • Deep experience implementing CI/CD pipelines for data platforms
  • Strong expertise in Databricks CI/CD setup and configuration
  • Experience with Omni Analytics CI/CD workflows and deployment
  • Proven ability to train technical teams on engineering practices
  • Understanding of data engineering best practices and maintainability
  • Ability to establish and document CI/CD standards and processes
Self-Serve Support Specialist (Contract) - 1 Position
Duration: ~2 months (40 working days maximum) | Start: Month 2
Focus: Intensive support period to train stakeholders and assess their self-serve capability, with condensed timeline to deliver the same outcomes more efficiently
  • Work with stakeholders to train and assess their capability to self-serve
  • Strong Omni Analytics user support experience
  • Excellent communication and problem-solving skills
  • Assess stakeholder self-serve capability and identify training needs
  • Ability to create documentation and knowledge base content
  • Experience supporting business users in analytics tools
  • Understanding of self-serve analytics adoption challenges
  • Deliver focused, intensive training sessions to maximise impact within shorter timeframe

Training Requirements

Comprehensive training is essential for Project Lantern success. This section outlines the training strategy for both existing team members and new hires to build Omni Analytics expertise.

Training Strategy

3
Omni Specialists Required
6
Months Training Period
100%
Team Coverage Target

Training Programmes

Existing Team Training
Comprehensive training programme for current Data & AI Function team members to build Omni Analytics expertise.
  • Report Developers: Omni report development, data modelling, and certification processes (4 weeks intensive)
  • Senior Analytics Engineers: Omni dataset engineering, data transformation, and platform integration (4-6 weeks - accelerated due to senior level)
  • Senior Data Engineer: Omni data pipeline integration, ETL/ELT processes, and data quality, focused on CCaaS chat data integration (3-4 weeks - accelerated due to senior level)
  • AI Capabilities: Omni AI capabilities, model integration, and natural language processing (integrated into Omni Specialist training)
  • Engineering Lead: Platform architecture, governance, and advanced features (ongoing)
New Hire Training
Structured onboarding and training programme for new team members joining during Project Lantern delivery.
  • Onboarding: 2-week intensive Omni fundamentals course
  • Role-Specific: 4-8 weeks role-specific Omni training based on position
  • Mentorship: Pairing with experienced Omni specialists
  • Certification: Omni Analytics certification within 3 months
  • Continuous Learning: Ongoing training and upskilling opportunities
Omni Specialist Training Delivery
Training delivery approach using contract Omni specialists to build internal capability.
  • Omni Specialists: Develop curriculum, deliver structured training sessions, and provide hands-on mentoring and technical guidance
  • Workshops: Weekly workshops on specific Omni features and capabilities
  • Project-Based Learning: Real-world projects to reinforce learning
  • Knowledge Transfer: Document best practices and create internal knowledge base
CI/CD & Repository Training
Critical capability training to ensure Project Lantern is delivered with proper engineering practices, version control, and maintainability.
  • Repository Management: Training on using Git repositories for all project work
  • CI/CD Implementation: Setting up and implementing continuous integration and deployment pipelines
  • Databricks CI/CD: Configuration and setup of CI/CD practices on Databricks platform
  • Omni CI/CD: Implementation of CI/CD workflows for Omni Analytics development
  • Best Practices: Code review processes, branching strategies, and deployment workflows
  • Team-Wide Adoption: Ensure all team members are trained and following engineering best practices

Training Timeline

Month 1-2: Foundation Training

Omni platform fundamentals, basic report development, data modelling, and CI/CD/repository training for all team members. Establish engineering practices from the start.

Month 3-4: Advanced Features

Advanced reporting, AI capabilities, natural language querying, predictive analytics training, and advanced CI/CD workflows for Databricks and Omni.

Month 5-6: Specialisation & Certification

Role-specific advanced training, Omni certification, knowledge transfer completion, and refinement of CI/CD processes.

Implementation Roadmap

The Project Lantern implementation roadmap outlines the key milestones, deliverables, and activities over the 6-month delivery period.

6-Month Delivery Timeline

Loading roadmap data...
Month 1: Foundation & Setup
  • Onboard contract resources (2 Omni Specialists, 2 Senior Analytics Engineers, 1 Senior Data Engineer)
  • Onboard CI/CD Training Specialist (after FTC resources are onboarded)
  • Establish Project Lantern governance and reporting
  • Begin stakeholder engagement and requirements gathering
  • Set up Omni platform infrastructure and access
  • Establish repository structure and CI/CD pipelines for Databricks and Omni
  • Start foundation training for existing team (including CI/CD and repository practices)
  • Begin joint work on dataset engineering and model development for priority functions (CRM, Base Reporting, Sales) - Senior Analytics Engineers work on tables/relationships/fields, Report Developers work on using tables, documenting in metric views/Omni topics, creating measures and dimensions
Month 2: Core Development
  • Complete joint work on CRM, Base Reporting, and Sales datasets and models (Senior Analytics Engineers: tables/relationships/fields; Report Developers: metric views/Omni topics, measures/dimensions)
  • Begin joint work on Customer CMC Data and Customer Feedback datasets and models
  • Implement basic AI capabilities (natural language querying)
  • Onboard Self-Serve Support Specialist
  • Continue team training (advanced features) - Omni Specialists training Senior Analytics Engineers and Report Developers through joint working
Month 3: Expansion & AI Enablement
  • Complete joint work on remaining function datasets (Customer Workflow, Financials) - Senior Analytics Engineers and Report Developers working with Omni Specialists
  • Deploy advanced AI features (AI-generated commentary)
  • Begin user acceptance testing with function stakeholders
  • Advanced training and specialisation for team - knowledge transfer from Omni Specialists to Senior Analytics Engineers and Report Developers
Month 4: Refinement & Training
  • Refine reports and dashboards based on user feedback
  • Optimise AI context and natural language querying capabilities
  • Refine AI commentary best practices based on user feedback
  • Complete team training and certification
  • Begin knowledge transfer from contract specialists
  • Develop long-term support model
Month 5: User Adoption & Transition
  • Roll out self-serve capability to all functions
  • Provide user training and support
  • Monitor adoption and usage metrics
  • Complete knowledge transfer documentation
  • Plan transition to permanent structure
  • Begin hiring for permanent roles (if required)
Month 6: Stabilisation & Handover
  • Stabilise platform and resolve any issues
  • Complete transition to permanent team structure
  • Finalise documentation and knowledge base
  • Conduct project retrospective and lessons learned
  • Handover to permanent Data & AI Function structure
  • Project Lantern closure and reporting

Key Deliverables

📊 Datasets

Engineered datasets for all 7 function areas, optimised for self-serve consumption on Omni.

📈 Reports & Dashboards

Certified reports and dashboards for each function, replacing current reporting solutions.

🤖 AI Capabilities

Fully enabled AI features including natural language querying and AI-generated commentary with comprehensive context.

📚 Documentation

Comprehensive data model documentation (topics, descriptions, measures), training materials, user guides, and technical documentation.

👥 Trained Team

Fully trained and certified team capable of maintaining and extending Omni capabilities.

🔧 CI/CD & Engineering Practices

Established repository structure, CI/CD pipelines for Databricks and Omni, and engineering best practices across the team.

🏗️ Organisational Structure

Defined long-term organisational structure aligned with self-serve enablement objectives.

Success Metrics

Key success metrics to measure Project Lantern delivery and adoption.

7+
Function Areas Enabled (Minimum)
80%+
User Adoption Target
500+
Total Omni Views (Target)

Contract Resources Required

To deliver Project Lantern by end of June while continuing all current deliverables and agreed prioritised projects, the following contract resources are required:

Contract Positions Summary

2 Omni Specialists

Duration: ~3 months (70 days) | Start: Month 1

Expert-level Omni Analytics platform knowledge including AI capabilities, training Senior Analytics Engineers and Report Developers through joint working on data models, plus implementing natural language querying and AI-generated commentary.

1 Senior Data Engineer (FTC)

Duration: 6 months | Start: Month 1 | Rate: £700/day

Senior-level expertise to connect to CCaaS platform and introduce missing contact centre data (chat data) into Databricks. Must hit the ground running in short sprint delivery.

1 CI/CD Training Specialist

Duration: 20-30 days | Start: After FTC resources are onboarded (Month 1-2)

Training on repository management and CI/CD implementation for Databricks and Omni to enable the whole team.

1 Self-Serve Support Specialist

Duration: ~2 months (40 days maximum) | Start: Month 2

Intensive user support and guidance for business users adopting self-serve capabilities, delivered in a condensed timeframe.

Total Contract Resources: 7 contract positions (4 Contract specialists + 3 FTC positions)

  • Contract Positions: 2 Omni Specialists (70 days each, includes AI capabilities), 1 CI/CD Training Specialist (20-30 days, starts after FTC resources onboarded), 1 Self-Serve Support Specialist (40 days maximum) (4 total)
  • Fixed Term Contracts (FTC): 2 Senior Analytics Engineers (£700/day), 1 Senior Data Engineer (£700/day, focused on CCaaS chat data) (3 total)

Permanent Position: 1 Full-Time FTE - Senior Tech Product Manager (covers both Lantern delivery and BAU sustainment, working with the data team and central change team on demand sizing and roadmap adherence). This is an ongoing operational cost, not included in one-off delivery costs.

Fixed Term Contracts: 2 Senior Analytics Engineers (FTC, £700/day) and 1 Senior Data Engineer (FTC, £700/day, focused on CCaaS chat data integration) - senior-level expertise required to deliver at pace, potential for long-term roles post-Lantern

Long-Term Team Structure

As specified in the brief, Project Lantern will concurrently define the long-term structure of the Data & AI Function team given the new Omni Analytics tooling. This process will run in parallel with project delivery to ensure the permanent structure is ready for transition at project completion.

Structure Definition Process

Months 1-2: Assessment & Planning

Assess current team capabilities, identify skill gaps, and begin defining the optimal structure for ongoing Omni Analytics support and development.

Months 3-4: Structure Design

Design the long-term organisational structure, role definitions, and career pathways aligned with Omni Analytics capabilities and self-serve enablement objectives.

Months 5-6: Finalisation & Transition Planning

Finalise the long-term structure, secure approvals, and develop detailed transition plan from interim to permanent structure.

Key Considerations for Long-Term Structure

The final long-term structure will be documented and presented for approval during Month 5, ensuring a smooth transition from the interim structure at project completion.

Core Assumptions and Risks

Project Lantern is built on several core assumptions regarding the existing data platform infrastructure and frameworks. These assumptions are critical to the project's scope, timeline, and resource requirements.

Key Assumptions

1. Existing Data Framework Continuity

Project Lantern assumes that the current data cleaning and conforming frameworks remain unchanged and supported throughout the delivery period and beyond. The project relies on:

  • Cleaned Framework: The existing custom framework for cleaning new data into the platform will remain operational and supported
  • Conformed Framework: The existing custom framework for conforming data for use in reporting and semantic models will remain operational and supported
  • Senior Analytics Engineer Support: Continued availability and support from the Senior Analytics Engineer who maintains the conformed layer logic

Group Commitment Required: This assumption requires a group-level commitment to support and maintain the existing data framework infrastructure throughout and beyond the Project Lantern delivery timeline.

Risks and Dependencies

Risk 1: Framework Migration or Deprecation

If the group strategy moves to migrate data cleaning or conforming processes to a new framework (e.g., Lakeflow, dbt), significant additional work would be required:

  • Rebuild of data pipelines to support self-serve capabilities in the new framework
  • Refactoring of all downstream dependencies built over the existing frameworks
  • Migration of complex data integration logic that currently exists in the conformed layer
  • Impact on operational processes and integrations built by other teams (e.g., Adobe, Infosys)

Mitigation: Requires group-level decision and commitment to maintain existing frameworks, or allocation of additional resources and timeline extension if migration is required.

Risk 2: Data Integration Complexity

The existing data platform relies on complex integration logic due to:

  • No direct integration keys between core source systems (e.g., CMP, Netezza, Adobe)
  • Complex join logic requiring deep understanding of multiple platform data structures
  • Limited documentation from source system suppliers
  • Highly customised integration logic currently maintained in the conformed layer

Impact: New team members cannot easily replicate this work without extensive knowledge transfer and documentation. This complexity is currently managed by the Senior Analytics Engineer and exists within the conformed framework.

Risk 3: Framework Dependency on Key Personnel

The conformed framework is highly dependent on the Senior Analytics Engineer for ongoing support and maintenance. This creates:

  • Knowledge concentration risk in a single role
  • Limited ability for other team members to maintain or extend conformed logic
  • Potential bottleneck for self-serve capability expansion

Long-term Consideration: While the framework serves current needs well, it was not designed for long-term scalability. A future migration to supported frameworks (Lakeflow, dbt) would address this dependency risk but requires upfront investment.

Resource Implications

Should any of these assumptions change during or after Project Lantern delivery, the following would be required:

  • Additional Contract Resources: Data engineers and analytics engineers with framework migration expertise
  • Extended Timeline: Additional months for framework migration, pipeline rebuild, and testing
  • Coordination with Group Strategy: Alignment with group-level data framework decisions and timelines
  • Impact on Other Teams: Coordination with teams that have built processes over the existing frameworks (Infosys, Adobe integrations, operational processes)

These resource implications are not included in the current Project Lantern scope and budget, as they are contingent on group-level decisions regarding data framework strategy.

Recommendations

To mitigate these risks and ensure Project Lantern success:

  • Group Commitment: Secure explicit commitment from group data strategy leadership to maintain existing frameworks throughout Project Lantern delivery and initial operations period
  • Decision Framework: Establish a clear decision point and process for evaluating framework migration requirements, with adequate lead time for planning
  • Knowledge Transfer: Accelerate documentation and knowledge transfer of conformed layer logic to reduce dependency risk
  • Alternative Planning: Develop a contingency plan for framework migration if group strategy changes, including resource requirements and timeline implications

Next Steps

To proceed with Project Lantern implementation: