Create, Read, Update, Delete (CRUD) – the four fundamentals of the data management lifecycle, mundane and embedded deep within everyday processes and systems, yet they underpin every sophisticated data-driven approach to transformation – evidence-based decision-making, insight and predictive analytics, and AI automation.

Data as a strategic resource relies on getting the basics right. These are:

● Create: To drive value and support improvement, an effective data architecture relies on the completeness and quality of the data attributes supported. To capture what is needed, think back from the problem at hand – to tackle damp and mould or repeat homelessness, say what do we need to know?

● Read: Data is only valuable if accessible at a critical moment – safety actions or safeguarding interventions may depend on it. The real agency of data occurs when it is shared or joined-up to support a whole-system approach.

● Update: Data must be current to retain its value – an intervention flag on a vulnerable person's record that is never lowered has no value. Getting processes right is critical but this is only achievable if the underpinning data is right too. Accountability for data quality should be prioritised alongside performance management and customer experience.

● Delete: Data protection compliance is a given, but how much redundant, obsolete or trivial (ROT) data do you hold, supporting worthless but resourced processes and ultimately sitting on servers that warm our planet.

Organisations seldom exploit the data capabilities of the software platforms they have invested in, with data-driven capabilities often left in the box. A healthcheck or data audit is a worthwhile investment, and a high-level understanding of your data architecture will enable you to unlock valuable inter-relationships.

As a service leader contemplating transformation, step back and ask: What are the most valuable data attributes we hold and what are the gaps? Do we have a clear and compliant governance plan for our data? And how can we best harness our data to maximise impact?

Ignore data fundamentals at your peril, GIGO – ‘garbage in garbage out' remains the universal truth of the data lifecycle!

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