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:
- 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.
- 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.
- 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.