nishingly unlikely for anyone to score a “10” for their effectiveness on data reporting. When it comes to data effectiveness generally, we see a wide range of views – due either to differences between councils’ arrangements, or differences between employees’ perceptions of what “good” means. Chart 2 - Average scores for section 1 of survey Seems to be a sliding scale: Most 1: On Case Management System & stat return (CiN/CP/CLA) 2: On CMS (maybe) but not stat return or vice versa (EH) 3: Not on CMS or stat return (partner data, secondary internal source) Least Similar to EH findings. Where data is incorporated into systems/returns/benchmarking, it works better. But is this because items are “statutory”, or are items “statutory” because they’re useful? Chart 3 – Importance vs effectiveness of understanding outcomes 0 1 2 3 4 5 6 7 8 9 10 Average Score Given (0-10) Measure Area Difference between average scores of importance and effectiveness From largest to smallest How Important How Effective This shows how important we think it is to measure outcomes compared to how good we are at measuring them. In every category, we rate the importance higher than our current effectiveness. We’re closest in those items which are either universal (education outcomes) or highly targeted and regulated (permanence, identifying need). We struggle most with mental health and child/parent views – arguably the most important measures of whether we helped. A question remains around whether traditional data analysis is the right vehicle for understanding these outcomes. Chart 4 – Current effectiveness (self-appraisals) The bar chart shows the average score; the scatters show individual respondents’ scores. Ignoring some outliers, we see broadest divergence in Early Help (the least regulated dat