The unsexy work that makes everything else possible — and the discipline that AI makes non-negotiable.
Every BI project eventually hits the same wall: two teams with two different numbers for the same metric, each convinced theirs is right. That’s a governance failure, not a data failure.
I’ve built a data dictionary, a certified data program, a reporting repository, and a full Tableau audit across an organisation with seven internal stakeholder groups. None of it was glamorous. All of it was necessary.
And then AI arrived. The moment you start feeding LLMs operational data, every governance gap becomes a liability. This pillar covers how to build the foundation — before you need it.
How to build a data dictionary that actually gets used — tied to your reporting layer, owned by the people who generate the data, and maintained through a governance cycle rather than a one-time project.
Master data management for ops teams — deduplication, entity resolution, and the reporting standards that prevent seven teams from producing seven different answers to the same question.
The audit process for an existing Tableau environment — how to identify what’s being used, what’s stale, and what’s actively misleading. Plus the governance cadence that prevents the graveyard from growing back.
Across four regional centres covering Video, Access, and Intrusion product lines, the absence of standardised reporting meant each centre defined their own metrics. FCR, MTTR, and CSAT were all calculated differently depending on who you asked and which report they pulled.
The ITIL implementation established a governance layer that standardised how every key metric was defined, measured, and reported across all four centres — creating a single reporting view that regional leadership and global management could use without reconciling competing numbers.