1. What Is Data Readiness?
Understanding if your current architecture can support advanced reporting or AI initiatives before procurement begins.
Takeaway: Audit before building.
Knowledge Base
Editorial research and practical planning concepts.
2. When Dashboards Fail
Why visually appealing dashboards are abandoned when underlying definitions are not trusted by the team.
Takeaway: Trust requires data lineage.
3. Why Spreadsheets Become Risky
The operational danger of relying on disconnected files maintained by single individuals without version control.
Takeaway: Centralise critical metrics.
4. How to Choose AI Use Cases
Mapping business problems to potential pilot projects based on data availability and acceptable risk levels.
Takeaway: Start with low-risk pilots.
5. What RAG Means in Business Language
Explaining Retrieval-Augmented Generation as a way to ground language models in your specific company documents.
Takeaway: Quality depends on your documents.
6. Understanding Reporting Cadence
Why real-time data is often unnecessary for strategic decisions, and how to match refresh rates to meeting cycles.
Takeaway: Daily data isn't always better.
7. Who Owns Business Data?
Establishing clear stewardship for datasets to ensure someone is accountable when metrics appear incorrect.
Takeaway: Unowned data decays.
8. Privacy Questions Before Automation
Reviewing how automated workflows might accidentally expose personal information to wider audiences.
Takeaway: Respect the Data Protection Act.
9. How to Map a Manual Process
The necessary step of writing down every manual click before attempting to automate a reporting workflow.
Takeaway: Document the friction first.
10. How to Prepare for a BI Project
Gathering audience requirements and locating primary source systems to ensure BI projects launch successfully.
Takeaway: Audience definition is key.
11. Understanding AI Limitations
Recognising hallucination risks and why generative tools require robust human verification loops.
Takeaway: Trust but verify.
12. Working With Non-Technical Teams
Translating complex data architecture requirements into practical business language for operations managers.
Takeaway: Use editorial communication.