BMT’s Commitment to Excellence

BMT operates as a maritime-focused design and technical consulting firm dedicated to solving complex challenges in a rapidly changing environment. In a world where change is a constant, BMT embraces digital transformation with a customer-driven, digital-first approach to all aspects of our business, from our operating model to customer interactions.

The Role of Data Operations:

Data Operations are foundational to BMT’s strategy, ensuring access to reliable, high-quality data that supports informed decision-making, process optimisation, and the agility needed to respond to industry shifts. This Data Operations Strategy aligns with BMT’s broader objectives, setting a framework that transforms data into actionable insights, enhances customer experience, and sustains competitive advantage.

Strategic Alignment:

The Data Operations Strategy isn’t just a technical initiative but a strategic driver, connecting data operations to BMT’s goals. By creating a data-literate culture that uses data to shape decisions across the organisation, BMT is better positioned to anticipate customer needs and improve service quality.

BMT’s Vision for a Data-Literate Future

Our Digital Vision is a world where data and analytics empower everyone at BMT to make better decisions and solve complex challenges. By transforming data into actionable intelligence, the business can enhance decision-making, boost revenue and profitability, and optimise customer relationships.

Digital Strategic Objectives

Our Data Strategy focuses on five key objectives that underpin BMT’s digital transformation:

• Managing Customer Insights: Consolidating and interpreting customer data to better understand needs and behaviours.
• Tracking and Monitoring Project Data: Ensuring complete visibility of project progress, risks, and outcomes.
• Innovating and Transforming Products and Services: Leveraging data insights to develop customer-focused solutions.
• Securing Sensitive Data: Implementing robust security measures to protect critical information.
• Collecting and Actioning Customer Feedback: Embedding feedback loops to continuously improve our offerings.

By embedding these objectives into structured practices within our Data Operating Model, BMT enhances customer experience, streamlines operations, and drives innovation.

This strategy reaffirms BMT’s commitment to leveraging data as a core strategic asset for solving complex challenges, enhancing customer experiences, and achieving operational excellence.

Customer-Centricity

Data is essential for understanding and meeting customer needs. By consolidating and analysing customer data, BMT can proactively adapt services to deliver timely, personalised value.

Innovation

BMT’s data strategy fosters a culture of innovation, leveraging data for new product development, service improvements, and predictive insights, ensuring adaptability to market trends and customer needs.

Operational Efficiency

Centralising data and automating workflows reduces redundancies, enhances productivity, and supports faster decision-making, thus contributing to cost-effectiveness and operational excellence.

Resilience

A robust data operating model secures business continuity through strong data governance, centralised management, and secure access, positioning BMT for long-term sustainability.

Environmental Responsibility

Data operations use cloud-based infrastructure to minimise environmental impact, reduce reliance on physical servers, and employ efficient data acquisition and processing methods.

The main vehicle for the Data Operations Plan and implementation of BMT’s data strategy is the Data Operating Model (DOM). The purpose of the Data Operating Model is to create a centralised, secure, and optimised data framework that simplifies data access, improves quality, and supports agile decision-making. This model provides BMT with a consistent “single source of truth,” empowering strategic and operational decision-making across the organisation.

Purpose:

The purpose of the Data Operations Plan, realised through the DOM, is to:

Centralise Data Assets:

Consolidate disparate data sources into a unified structure, enabling BMT to access and leverage consistent, accurate data across business units.

Enhance Data Quality and Governance:

Establish robust data governance practices to maintain high data quality, ensuring all data is accurate, compliant, and aligned with organisational standards.

Support Agile and Informed Decision-Making:

Equip BMT’s workforce with the tools and data access needed for quick, data-driven decisions, enhancing responsiveness and adaptability to industry demands.

Strengthen Data Security and Compliance:

Uphold stringent data security, privacy, and compliance measures, safeguarding sensitive information and reinforcing stakeholder trust.

Scope

The scope of the Data Operations Plan encompasses the following critical areas, which together enable BMT to achieve data-driven transformation:

Data Governance:

Policies and frameworks to ensure data quality, privacy, and integrity, maintaining trust and compliance.

Data Architecture and Infrastructure:

A scalable, cloud-based, multi-layered medallion structure (Bronze, Silver, Gold, Platinum) supporting all data needs.

Data Integration and Interoperability:

Standardised data ingestion and integration processes to facilitate smooth, unified data access.

Analytics and Advanced Insights:

Foundational and advanced analytics capabilities, ensuring data is actionable.

User Accessibility and Data Literacy:

User-friendly access tools to enhance data literacy and democratise insights.

The Data Operating Environment at BMT serves as the operational foundation for data governance, accessibility, security, and delivery, aligned with our strategic goals. This environment brings together the technology, people, and processes needed to transform raw data into actionable insights, supporting the business with scalable, consistent data services.

Purpose of the Data Operating Environment

The purpose of the Data Operating Environment is to ensure BMT has a unified, reliable, and secure data infrastructure that enhances data accessibility and governance, reduces operational costs, and supports agile decision-making. This environment is designed to centralise data management, empower data-driven decision-making, and provide the tools necessary for consistent and compliant data practices.

Structure of the Data Operating Environment

The environment is structured into layered capabilities that build upon each other, creating a streamlined workflow from data ingestion to consumption. This layered model aligns with our medallion architecture:

• Bronze Layer: Raw data ingestion and storage, capturing data in its native form.
• Silver Layer: Cleansed and structured data, aligned with BMT’s common data model for easier analysis.
• Gold Layer: Enriched data marts, providing specific, pre-aggregated views tailored for different business functions.
• Platinum Layer: Advanced analytics and machine learning, facilitating predictive and prescriptive insights.

This structure simplifies data management, enabling faster, more consistent access to data across the organisation and reducing the need for siloed data handling.

Key Challenges Addressed by the Data Operating Environment

1. Tech-Centric Focus: The environment is not solely a technical implementation but a cross-functional strategy that enhances the BMT business model. By focusing on aligning technology with business goals, we ensure data solutions directly support customer experience, operational efficiency, and innovation.
2. Departmental Data Silos: Data silos are mitigated through centralised data access in the cloud-based environment, allowing departments to draw from a unified source. This centralisation reduces redundancies, ensures data consistency, and promotes collaboration across BMT.
3. Data Quality Assurance: To support high-quality data, the environment employs automated data validation and cleansing processes in the Silver layer. This ensures data used across the organisation meets consistent standards, reducing errors and enhancing reliability in reporting and analytics.
4. Complex Data Landscape: The environment accounts for BMT’s diverse data landscape, incorporating multiple data sources, ETL processes, and a common data model. This holistic view integrates all data sources within BMT, enabling a seamless data pipeline that aligns with operational and strategic needs.

Benefits of the Data Operating Environment

The Data Operating Environment offers BMT strategic advantages in several key areas:

• Enhanced Data Governance: Ensures data integrity, privacy, and compliance across all business units.
• Accelerated Speed to Insight: Reduces time to access and utilise data, enabling quicker and more reliable insights.
• Streamlined Data Accessibility: Empowers employees across departments with centralised, democratised data access.
• Improved Operational Efficiency: Reduces the burden of managing disparate data sources and manual processing.
• Reduced Environmental Impact: Cloud-based infrastructure and targeted data acquisition processes lower resource consumption and environmental footprint.

Contextual Challenges

BMT operates within a dynamic environment where reliable, accessible data is essential for informed decision-making. However, several common challenges currently hinder our data operations:

Data Centralisation and Access

• Challenge: BMT’s data is dispersed across multiple departments, systems, and sources, leading to inconsistencies and inefficiencies in data access. Employees often face difficulties locating and retrieving accurate data when needed.
• Impact: This decentralisation causes delays in decision-making, makes cross-departmental insights harder to achieve, and fosters a lack of data transparency across the organisation.

Data Quality and Consistency

• Challenge: Ensuring data quality is challenging due to varied data standards and the lack of a unified data governance framework. Issues like incomplete records, outdated information, and discrepancies in data entry across departments compromise data integrity.
• Impact: Poor data quality leads to unreliable insights, undermining confidence in data-driven decision-making and affecting customer-facing outputs and operational efficiency.

Departmental Data Silos

• Challenge: BMT’s departments often maintain separate data repositories, resulting in data silos. These silos obstruct the seamless flow of information and hinder collaborative insights, as data is only partially available across teams.
• Impact: Data silos create duplicate data efforts, increase operational costs, and inhibit a unified understanding of customer needs and project progress, ultimately limiting BMT’s agility and innovation.

Security and Compliance Pressures

• Challenge: With the growing regulatory landscape around data privacy and protection, ensuring data security across various systems and access points is increasingly complex. Compliance requires stringent governance but is difficult to enforce across disparate systems.
• Impact: Security vulnerabilities and non-compliance risks expose BMT to potential reputational damage and regulatory fines, compromising trust with stakeholders and customers.

Opportunities

The Data Operating Model (DOM) presents a strategic response to these challenges, transforming them into opportunities that align with BMT’s digital vision and business objectives:

Centralised Data Access for Unified Insights

• Opportunity: By centralising data into a single access point, the DOM facilitates consistent, transparent data access across departments. This centralisation provides a “single source of truth,” enabling departments to collaborate using unified insights, which improves cross-functional decision-making.
• Solution in the DOM: Through a cloud-based infrastructure and medallion architecture, the DOM consolidates data, reducing inefficiencies and enhancing the accuracy of insights.

Enhanced Data Quality and Governance

• Opportunity: Standardising data governance across BMT improves data quality and fosters a data-driven culture. Implementing consistent quality checks and governance protocols ensures that high-quality data is available across the organisation, building trust in data as a strategic asset.
• Solution in the DOM: The DOM’s data quality protocols in the Silver and Gold layers ensure data is consistently validated, cleansed, and aligned with BMT’s common data model. Data stewardship roles support ongoing quality checks, ensuring data integrity.

Breaking Down Data Silos for Greater Collaboration

• Opportunity: By dismantling data silos, the DOM enables smoother collaboration and empowers employees with a holistic view of business data. Unified data access allows for faster, more accurate decision-making, fostering an agile organisational culture that is responsive to new opportunities and customer demands.
• Solution in the DOM: The centralised data infrastructure integrates disparate data sources and ensures data is accessible to all authorised teams. This accessibility encourages collaboration and consistency in data handling, supporting both strategic and operational goals.

Strengthened Security and Compliance

• Opportunity: Implementing a unified security and compliance framework through the DOM mitigates risks and ensures BMT remains compliant with data privacy regulations. This security structure builds customer and stakeholder confidence, establishing BMT as a trusted data custodian.
• Solution in the DOM: The DOM enforces security protocols, including role-based access and regular audits, at each layer of the data lifecycle. These practices ensure compliance while maintaining flexible data access, aligning with BMT’s strategic commitment to security and trust.

Optimising Operational Efficiency and Reducing Costs

• Opportunity: By addressing data redundancy, standardising data access, and streamlining operations, the DOM supports BMT’s drive for operational efficiency. A centralised data approach reduces resource allocation to data management, allowing for a more strategic focus on innovation and value-driven activities.
• Solution in the DOM: Through automated workflows and cloud-based infrastructure, the DOM reduces the manual burden of data handling and lowers IT overheads. This efficiency frees resources, enabling teams to concentrate on enhancing customer experiences and driving innovation.

Purpose:

Managing organisational change is pivotal for BMT’s data-driven transformation. Engaging key stakeholders and fostering a data-centric culture ensures that the transition is effective, sustainable, and aligned with BMT’s operational and strategic goals.

Strategies:

Communications Strategy:

A multi-tiered communications plan will tailor updates to each audience. Executives will receive strategic insights and progress updates, while analysts and data practitioners will gain actionable guidance on integrating data into their workflows via KnowHow.

Stakeholder Engagement:

Department heads and key function leaders will champion data use across BMT, advocating for adoption and alignment with data governance practices. Regularly scheduled stakeholder forums will keep them engaged, informed, and aligned with the operational milestones.

Training and Onboarding:

A comprehensive onboarding program, supported by resources in KnowHow, will introduce new data processes. Team members will have access to training modules, best practices, and practical how-to guides that demonstrate effective use of myBMT as a tool for navigating the Data Warehouse.

Expected Outcomes:

Success metrics include high adoption rates, positive user satisfaction feedback, and increased engagement with both KnowHow resources and myBMT. Regular surveys and performance metrics from myBMT usage data will help measure these outcomes, demonstrating the value of the tools and the efficacy of change management efforts.

Purpose:

Performance metrics and KPIs provide a structured way to evaluate the impact of the Data Operating Model (DOM) on BMT’s strategic objectives. By defining clear indicators, BMT can track and demonstrate progress in data centralisation, data quality, user adoption, and operational efficiency, ensuring alignment with broader business goals.

Balanced Scorecard – Core KPIs:

Customer-Centric Metrics

• Data Accessibility for Key Teams: Track accessibility percentage for core datasets (target: initial 75%, with a gradual increase).
• User Engagement: Frequency of myBMT usage by customer-facing roles to assess data reach and value.

Operational Efficiency Metrics

• Data Quality Baseline: Quarterly assessments focusing on accuracy and completeness of centralised data.
• Data Processing Reliability: Track on-time data refresh completion rates (target: 85%+ to start) to support reliable, regular reporting.

Innovation and Growth Enablement

• New Data Sources Onboarded: Initial target of onboarding 2–3 new data sources per year, with growth tied to demand and evolving needs.
• Foundational Analytics Usage: Measure the number of users accessing base analytics in myBMT to monitor awareness and uptake of data-driven insights.

Environmental Responsibility and Efficiency

• Data Centre Usage Tracking: Begin with quarterly tracking of cloud-based data storage and processing energy use.
• Reduction in Redundant Data Processing: Quarterly reviews aimed at identifying and reducing unnecessary data processing activities.

Aspirational Future KPIs:

As BMT’s data maturity advances, the following aspirational KPIs will provide long-term targets:

• Advanced Analytics Adoption: Number of predictive models operationalised.
• Data Quality Compliance Target: Achieve 90% adherence to comprehensive data quality standards.
• Environmental Efficiency Impact: Target reduction in carbon footprint related to data infrastructure.

Purpose:

The purpose of this Risk Management Framework is to identify and address potential risks associated with the Data Operating Model (DOM). By proactively managing these risks, BMT can ensure the continuity, security, and compliance of its data operations, reinforcing stakeholder trust and the reliability of data-driven decision-making.

Key Risks and Mitigation Strategies:

Data Security and Privacy

• Risks: Risks include potential data breaches, unauthorised access, and accidental data exposure, which could lead to regulatory fines, reputational damage, and operational disruption.
• Mitigation: Implement regular security audits, robust encryption protocols, and role-based access controls (RBAC). Quarterly compliance reviews ensure that data policies align with evolving regulatory requirements.
• Response Plan: A structured incident response plan, including timely notifications, isolation of affected systems, and rapid mitigation, is in place to handle security incidents efficiently.

System Downtime and Resilience

• Risks: System downtimes, particularly affecting myBMT or data ingestion processes, could disrupt data access and delay key business functions.
• Mitigation: Establish resilience planning through redundant infrastructure, including failover systems and automated backup processes. Cloud-based scaling capabilities provide an additional buffer to maintain uptime during high demand.
• Response Plan: Downtime risk is addressed through a critical response plan, defining steps for immediate technical and communications response, stakeholder notification, and service restoration.

Compliance with Data Regulations

• Risks: Non-compliance with data protection laws (e.g., GDPR) or internal data policies could result in penalties and damage to stakeholder trust.
• Mitigation: BMT implements compliance protocols that monitor data handling and retention policies. This includes automatic deletion of outdated or non-compliant data and regular updates to align with the latest regulatory changes.
• Response Plan: Compliance risks are managed through a periodic compliance assessment. Non-compliant data practices trigger an immediate review and corrective action process led by compliance and data governance officers.

Contingency Planning for High-Risk Scenarios:

For high-risk scenarios, BMT’s contingency plan includes the following stages:

1. Identification: Rapid identification and categorisation of the incident by priority level.
2. Incident Response: Activation of predefined response teams with clear roles, from IT for technical containment to communications for external and internal notifications.
3. Resolution and Recovery: Swift problem-solving measures, including data restoration, system backups, or security patching as needed.
4. Post-Incident Review: Detailed analysis of incident causes and effectiveness of response, leading to refined mitigation strategies and updated protocols.

Data Protection Impact Assessments

DPIAs are a core component of BMT’s privacy risk management strategy. For any new data processing or changes involving sensitive or high-risk data, DPIAs will be conducted to evaluate privacy risks, design mitigating measures, and ensure compliance with regulatory standards. This proactive approach to data privacy protects individuals’ rights, enhances trust, and aligns with BMT’s commitment to responsible data management.

This structured approach to risk management reinforces BMT’s commitment to secure, compliant, and resilient data operations, supporting both operational stability and long-term organisational trust.

Purpose:

Develop data literacy across BMT to empower effective data usage, fostering a workforce capable of leveraging data confidently and accurately.

Framework:

• Data Wrangler Academy (Future Aspiration): Establish a comprehensive academy to train BMT staff on all levels of data competency, from foundational knowledge to advanced analytics. This academy will formalise BMT’s commitment to data maturity, providing specialised courses and certifications aligned with the competency framework.
• Data Literacy Levels: Define literacy levels by role, supporting colleagues in progressing from Awareness to Working levels, and eventually to Practitioner and Expert as their roles demand.
• Core Skills: Focus on essential competencies, including data handling, interpretation, visualisation, and analytics basics, expanding over time with specific modules for predictive and prescriptive analytics.
• Training Modules:
  • Introductory Training: Familiarises new users with the fundamentals of the Data Operating Model and essential data principles.
  • Advanced Analytics: Equips data-savvy users with advanced techniques for predictive and prescriptive analytics, fostering data-driven problem-solving.
  • Compliance and Governance: Designed for data stewards and users handling sensitive data, focusing on policy adherence and secure data handling.

Measurement:

Track training completion rates, participant feedback, and progress within the competency framework, setting up pathways for certification within the Data Wrangler Academy to formalise skills development across the organisation.

Purpose:

Outlines how BMT will leverage emerging trends to drive ongoing innovation.

Future Trends:

• Advanced Analytics and Machine Learning: Plan to expand on the Platinum layer’s predictive and prescriptive capabilities.
• Data Automation and Self-Service Analytics: Enable more departments to access and use data with minimal technical intervention.
• Data Mesh and Decentralised Data Ownership: As BMT scales, consider a federated model to support regional analytics autonomy.

Innovation Roadmap:

• 2024: Data operating model stabilisation and data literacy improvements.
• 2025: Expansion into advanced analytics and regional scalability.
• 2026: Full implementation of machine learning applications and integration with real-time analytics.