Data Curation

When it comes to managing large sets of content and data, it is important to think like a reptile — articulate that jaw to swallow more than you can chew.

Large datasets need to be managed with prioritisation to make sure the impactful 20% of content and data that generates 80% engagement is over-optimised while the remaining 80% is triaged to be 'reliable'.

'Reliable' can mean having sanity and validity checks and logic to programmatically drop bad data.

Doing more with less by prioritising management of important subsets of data while doing the minimum quality checks on the remainder can be a successful strategy in content and data curation.