✅ Green — Available and fit for purpose Use Green when: Outcome: Report builder can proceed with confidence. ⚠️ Yellow — Present, but not where/what we expected Use Yellow when: Outcome: Needs a small piece of investigation/remapping, but not a dead end. ❌ Red — Critical gap (blocking) Use Red when: Outcome: Requires Data Engineering … Read more
1. Treat the DWH as the system of record for the week Our existing rhythm is actually very strong: That already gives you something most organisations lack: a weekly data contract. Reports must key off that. 2. Reports should always trail the DWH by one controlled step With DEV → UAT → LIVE workspaces, you … Read more
This process describes how to take a new data source from landing → Bronze → DataMart view, and fully define its attribute lineage in myBMT. 1. Prepare and review the new data source 2. Catalogue and land the source 3. Plan the DataMart view(s) 4. Set up attribute lineage in myBMT For each new view … Read more
Purpose To provide a clear, sustainable leadership structure for the Data & Analytics function by separating accountability for: while maintaining shared ownership of standards and governance. This model is intended to: Context The Data & Analytics function currently spans: These responsibilities place competing demands on a single leadership role: To reduce strain and increase effectiveness, … Read more
Issued by: Data & Analytics TeamApplies to: All data integrations, pipelines, and views entering or leaving the Data WarehouseStatus: Approved DraftDate: [Insert Date]Version: 1.0 1. Purpose This standard defines the structure and principles for integrating data from source systems—including ERP (IFS), Dynamics 365, Power Apps, and SharePoint—into the enterprise Data Warehouse. It ensures that data … Read more
This note explains how to turn the current views (wide–from–tall) into a report‑friendly table using Power Query (recommended) or DAX. It covers two common layouts: It also shows how to extract a human‑readable Description from the JSON in VALUE_METADATA. Source schema (tall, meta-driven) Expected columns: Assumption: Each (OBJECT_SEQ, [META_TYPE], ATTRIBUTE) appears at most once for … Read more
This process explains how Data Engineering (DE) and Analytics Reporting (A) work together to turn a business need into a trusted, production-ready report. It’s designed for non-technical managers: no jargon, just what happens, who’s responsible, and what the outcomes are. Phase 0: Definition – Ground Zero PurposeLay the foundation before vision-shaping begins. Ensure clarity on … Read more
I thrive where challenge, novelty, and passion meet. I learn visually and by doing things myself. Engagement is key for me – if my brain isn’t bought in, it can’t focus on the task. Guide me with clarity of purpose, insight, novelty and challenge and I’ll deliver energy, creativity, and continuous improvement. 🧩 How I Work Best … Read more
I thrive at the intersection of curiosity, novelty, and real-world value, but I also perform best when work is structured, collaborative, and purposeful.Exploration and improvement energise me, yet clarity of expectations and harmony in the team keep me focused.Guide me with clarity of purpose, and I’ll deliver energy, creativity, and continuous improvement. 🧩 How I … Read more
Purpose This playbook guides the phased migration from existing DataMart views (202409) to CDM-standard views, including core, meta (dates, codes, values), and item tables. It supports the transition to IFS Cloud and ensures a consistent, scalable data model across all domains. MVP Scope Domains required for MVP: Phased Delivery Approach Phase 1: Live Data Seeding … Read more