It’s important to move from the raw data collected to an understanding of what that means for your programme. Then you can make appropriate changes to improve programme delivery and therefore, outcomes. Your first step is to go back to why you collected the data in the first place.
Example
If your model has a long and expensive volunteer training programme, then volunteers need to stay with you for at least a year to make the programme viable. So, you want to know how many volunteers stay for at least a year.
The raw data you collect is number of volunteers who start the year and the number at the end of the year. This data needs to go through a number of processes in order to be applied. This is shown in the diagram below:
Adapted from: NPC. Data with Destiny: How to turn your charity’s data into meaningful action. August 2017
If you find that 28 of the 34 volunteers who started at the beginning of the year have dropped out then you would want to review your recruitment plan. Are you following the implementation plan?
- If not, then make changes to ensure that you are doing what you think is necessary
- If you are, then the plan itself needs to be changed as it is currently not achieving the outcome you want.
When planning your measurements and data collection
Consider:
- Does your team have the capacity and skills to translate the raw data into knowledge? If not how will you find those skills? This will include the capacity to analyse quantitative data (numbers) and qualitative data (words).
- Who will come together to review the knowledge and turn it into wisdom that can be applied? How often will the knowledge be reviewed?
- How will you review the way you collect and analyse data to make sure it is good quality and useful?
For more information
Please download the Manual Guide: Reviewing and improving your programme below.
Manual Guide Reviewing and improving your programme
Added 09/03/2020Please sign in to download this file.