• Purpose: Cultivate a culture where data-driven decision-making is embedded into daily operations, empowering every level of the organisation to leverage data for proactive insights and strategic advantage.
• Scope: This BHAG covers initiatives that promote data accessibility, encourage data literacy, and ensure data platforms provide seamless support across departments.
• High-Level Intent: Move from isolated data insights to an integrated, organisation-wide data ecosystem where all teams actively use data to inform decisions, supported by user-centric tools and training.

Start-up:

• Purpose: Launch initiatives to build awareness of data’s role in decision-making across BMT.
• Goal: Establish a core data team and communicate the vision for a data-driven culture.

Foundation:

• Purpose: Lay down the infrastructure and data governance practices to support data-driven decisions.
• Goal: Roll out basic training and introduce data literacy programs across departments.

Reporting:

• Purpose: Enable easy access to operational and strategic reports that support data-informed decisions.
• Goal: Establish automated reporting with key metrics visible to all relevant teams through myBMT.

Predictive:

• Purpose: Integrate predictive insights into decision-making processes.
• Goal: Equip teams with tools and insights for scenario planning and forecasting to anticipate customer and operational needs.

Embed:

• Purpose: Cultivate a self-sustaining, data-informed mindset.

• Purpose: Create a centralised, highly accessible, and user-friendly data platform that provides accurate and consistent data across BMT, reducing silos and ensuring data reliability.
• Scope: This BHAG involves consolidating data products into a unified data model, improving accessibility via user interfaces (e.g., myBMT), and establishing sustainability in data practices.
• High-Level Intent: Provide a dependable, accessible “single source of truth” that enables all departments to access the data they need, fostering trust in data quality and consistency.

Start-up:

• Purpose: Begin by identifying critical data sources and implementing basic access controls.
• Goal: Consolidate data into a centralised repository.

Foundation:

• Purpose: Establish data governance and data cataloguing to define and document data assets.
• Goal: Roll out myBMT for initial access to unified data sources and set basic data quality standards.

Reporting:

• Purpose: Provide standardised, user-friendly reports based on the centralised data.
• Goal: Create accessible dashboards and reports, ensuring single-source, consistent reporting for operational efficiency.

Predictive:

• Purpose: Add predictive analytics layers to data products, improving the timeliness and relevance of insights.
• Goal: Develop models that generate predictive insights for key departments, enabling forward-looking decision-making.

Embed:

• Purpose: Fully integrate sustainable data products as the “go-to” information source.
• Goal: Achieve 95%+ adoption of data products across departments, reinforcing a shared, trusted source of truth.

• Purpose: Foster a culture of data competency by establishing a clear pathway for data literacy and skill development, culminating in an advanced data training program known as the Wrangler Academy.
• Scope: This includes foundational to advanced data training programs for all employees, aligned with job-specific needs and progressing to specialist tracks.
• High-Level Intent: Equip BMT staff with the skills to understand, use, and interpret data confidently, ultimately creating a workforce that embraces data as a strategic asset.

Start-up:

• Purpose: Raise awareness of data literacy’s importance and introduce basic data education.
• Goal: Launch initial workshops on data fundamentals for all employees.

Foundation:

• Purpose: Formalise the data literacy pathway with structured learning modules.
• Goal: Create tiered literacy levels and core skills for each role (e.g., Awareness, Working, Practitioner).

Reporting:

• Purpose: Equip employees to interpret and use standard reports effectively.
• Goal: Provide training on data interpretation and reporting tools through foundational Wrangler Academy modules.

Predictive:

• Purpose: Advance data skills, focusing on analytics and predictive insights for data-literate users.
• Goal: Develop and deliver courses on predictive analytics, fostering self-service and empowered analysis among users.

Embed:

• Purpose: Fully operationalise the Wrangler Academy, making data literacy a continuous, evolving practice.
• Goal: Graduate 90%+ of employees to a minimum of “Working” level literacy, with specialised paths for advanced users.

• Purpose: Develop and operationalise predictive analytics capabilities to provide forward-looking insights, supporting proactive decision-making in areas such as customer management, project tracking, and operational efficiency.
• Scope: This BHAG focuses on the development of predictive models, supported by infrastructure enhancements, to deliver real-time, actionable insights.
• High-Level Intent: Move from reactive to proactive decision-making by implementing predictive analytics, which helps anticipate challenges, optimise resource allocation, and meet strategic objectives more effectively.

Start-up:

• Purpose: Identify core areas where predictive analytics could drive value.
• Goal: Begin prototyping simple predictive models in selected areas (e.g., customer trends, project timelines).

Foundation:

• Purpose: Establish a standard framework and platform for developing predictive insights.
• Goal: Deploy initial predictive models with stakeholder input to test viability and impact.

Reporting:

• Purpose: Enable actionable reporting that includes early predictive metrics.
• Goal: Roll out reports that incorporate predictive elements to relevant departments, such as trend forecasting.

Predictive:

• Purpose: Integrate predictive insights routinely across key business functions.
• Goal: Expand predictive models to cover multiple operational areas, refining accuracy and actionable outcomes.

Embed:

• Purpose: Make predictive insights a fundamental component of strategic planning.
• Goal: Ensure all major departments use predictive insights as a routine part of decision-making.

• Purpose: Build a robust governance framework to maintain data quality, ensure regulatory compliance, and protect sensitive information, setting the standard for data management.
• Scope: This BHAG encompasses data governance practices, quality control processes, compliance with regulations, and security protocols.
• High-Level Intent: Achieve excellence in data governance by ensuring BMT’s data is trustworthy, compliant, secure, and accessible only to authorised users.

Start-up:

• Purpose: Set foundational data governance standards and appoint data stewards.
• Goal: Establish data handling and access protocols, including initial data quality checks.

Foundation:

• Purpose: Formalise governance policies and quality standards across departments.
• Goal: Develop a Data Governance Framework and introduce quality assessments in critical areas.

Reporting:

• Purpose: Ensure consistent, high-quality data supports reliable reporting.
• Goal: Embed regular quality checks and provide reporting dashboards on data quality metrics.

Predictive:

• Purpose: Extend governance to cover advanced analytics and machine learning models.
• Goal: Implement governance and monitoring processes for predictive models, including data lineage and usage tracking.

Embed:

• Purpose: Achieve industry-recognised excellence in data quality and governance.
• Goal: Continuously maintain a high standard in data quality metrics and compliance, with certification where applicable.

Data Engineering is a crucial enabler for BMT’s data transformation goals. The following principles define the scope and commitments of Data Engineering to support our broader strategic aims:

Conscious Design

Promote forward thinking around usability and interoperability of the data and user-centric design principles of sustainable data products

• Usability: Making sure data is accessible and understandable for end users, reducing friction in accessing and utilising data effectively.
• Interoperability: Ensuring that data can flow seamlessly across different systems and platforms, promoting collaboration and data integration without technical barriers.
• User-Centric Design: Focusing on the needs and workflows of end users, tailoring data access and structures to align with how they work, ultimately boosting productivity and satisfaction.
• Sustainability: Emphasising long-term usability and efficiency, where data products are designed not only to serve current needs but also to adapt and remain relevant as requirements evolve.

Purpose-Driven Platform:

The modern data platform is a versatile tool that supports a broad spectrum of use cases, from BI/MI reporting and self-service analytics to real-time data streaming and advanced AI capabilities. However, each use case requires focused planning, specific resources, and structured data workflows. The data platform itself is not the final solution but a foundation for delivering value through aligned people, processes, and technology.

Data Engineering Commitments:

• Implement Robust Data Pipelines: Establish consistent data flows that connect operational systems with BI/MI systems, ensuring that data is timely, accurate, and accessible.
• Maintain Documentation and Transparency: Create and update detailed source-to-target mappings and data lineage documentation to support transparency, data quality, and knowledge transfer.
• Automate and Scale: Where possible, re-engineer manual workflows to achieve repeatable, scalable data processes, supporting growth without compromising data integrity.
• Optimise ETL Performance: Write and maintain ETL scripts that perform efficiently, ensuring timely data delivery without excessive resource consumption.
• Support BI Reusability: Design BI and analytics outputs that can be leveraged across use cases, reducing redundancy and empowering users to make data-informed decisions.

Managing Expectations:

1. Clear Use Case Prioritisation: Each use case will be evaluated and prioritised based on alignment with strategic objectives, data readiness, and resource availability. Not all use cases can be supported simultaneously, and clear prioritisation is essential for effective delivery.
2. Iterative Delivery: Data products and solutions will be built and refined iteratively. Early delivery will focus on foundational needs, while more complex capabilities like advanced AI and real-time analytics will follow once core processes are stable and validated.
3. Capacity for Evolution: As new use cases and data demands emerge, the data engineering team will assess and adapt the data platform. However, changes will be managed through a structured process that balances innovation with stability.

Value Realisation Beyond the Data Warehouse:

The data platform by itself doesn’t create value—it enables it. Realising the full value requires an intersection of process, people, and technology aligned to the data model. Organisational structures, from data governance to data literacy programs, must complement data engineering to achieve the operational and strategic potential envisioned.