The most important things to do with data are to SELECT, INTERPRET, PERSONALISE and PRESENT (SIPP).
1. Select - this does not mean just picking the most positive data and ignoring the rest! It means using data to back up the key messages and strengths of the school, as well as explaining honestly and practically any issues that you want to address.
Schools often overlook data that can support their key messages - for example I learned recently that one school has a excellent value added score for its less able students (and a pretty decent one for the more able!) but wasn't using it in marketing.
If you're not good with numbers, I suggest finding your Head of Maths or Maths Co-ordinator - they'll love to extract and explain the data to you!
2. Interpret - means explaining the data in ways that parents and other non-specialist audiences can understand. Teachers understand what an Average Standardised Residual of +0.3 in ALIS means (good news), but parents need to know that it means A-level results were statistically above those to be expected from the intake!
Equally, parents may wonder why your VA score has dropped from 100.1 to 99.9 - you need to explain that these numbers are statistically the same (a graph showing change over a longer time period helps here!)
3. Personalise - data means showing what the data actually means for individuals (usually students) at the school. If 100% of students move up the expected number of levels from KS1 to KS2 (well done again!), use a case study or examples of their changing work to show how much they have learned.
If you 've had a great A-level year, show how this has helped students achieve places in top universities.
4. Present - many people looking at schools will not be that used to viewing data and presenting key data in a simple format will help them focus on the key issues. Consider using infographics (click on infogram for details) to pick out key highlights online or in published documents.
At the same time, you need to be aware that you will have parents who will be very familiar with data analysis - so make sure you have raw data to back up any assertions and members of staff who can explain how your data has been presented.