ry stretched, there is apprehension about providing new measures. • LAs want requests for new data items to be sufficiently supported with appropriate resourcing. • Core functions of all data teams are similar (monthly/quarterly reporting, stat returns etc.) • Additional functions variable – deeper analysis and understanding. • Where other departments are involved (IT, other reporting teams) it can make work more complex – examples include having to ask for new reports to be developed. • There are skills gaps within teams, particularly as we move away from spreadsheets to dedicated BI software. Different structures mean analysts are not always empowered to make changes • LA data teams are spending so long on returns that there is little time left for the people who know the data best to draw interesting conclusions that could benefit understanding. • Distinct tasks (e.g. optimising a report) may be prioritised at the expense of internal relations (e.g. ensuring analysts can communicate with social workers). • Positive examples of internal collaboration are often the exception rather than the rule. • Where data teams and social workers do come together, it allows for deeper understanding of the system. We asked participants at our workshops: If you could pick one thing to stop collecting, what would it be? (multiple occurrences in brackets) • CiN Primary need codes are felt to be outdated. (2) • CLA Reviews – not reported back to us. (2) • CLA placement changes – the ‘other’ category is overused. • Adoption dataset – hard to produce. • Workforce – hard to produce. (3) • Foster care return. • Assessments started. • Distance placed from home. We asked participants at our workshops: If you could pick one thing to start collecting, what would it be? (multiple occurrences in 