Data Management

Have you ever wondered why it takes months to produce a new management report or corporate dashboard? These frustrating delays, endless debates over data definitions, and inconsistent metrics often point to a hidden issue: poorly managed reference data.

Organisations spend heavily on data lakes, warehouses and cloud migrations and still struggle to get business insight out. A semantic layer — metadata, taxonomy, ontology and knowledge graph — is what closes the gap.

Metcalfe’s Law holds that a network’s value is proportional to the square of its connected nodes. Applied to enterprise data, it explains why every new connection between systems compounds the value of everything already connected.

Chaining six facts together in a relational model can mean an eleven-table join. Graph databases traverse nodes and links instead, sidestepping the mapping tables that make enterprise data so slow to query.

Most organisations know they hold valuable data. Far fewer can say what exists, where it lives, or whether it can be trusted. Twelve practical steps for CIOs and CTOs who want to close that gap.

“No one ever got fired for buying IBM.” But enterprise software still rooted in 1980s architecture is the source of the poor-quality data that stalls AI programmes, and CIOs now face a do-or-die decision.