Section 3.5. Making sense of MEAL data Raw data: information that has not yet been analysed. It needs to be organised in such a way that you are able to analyse and draw conclusions from it. This is what you will use to: • understand the progress you are making towards your outcomes, indicators and targets and whether you have achieved them, • demonstrate your achievements to key stakeholders and participants, • learn from the information and potentially adapt your work. As discussed in Module 2, different data types have distinct characteristics and require different analysis techniques. There is, however, an overall approach that can be taken for most data analysis (see Figure 9 below). Figure 9: Process to make sense of your data21 Review the data Check planned outputs and outcomes Decide questions/ method of analysis Do the analysis Interpret the results Once data has been collected, you can begin the process of drawing conclusions from it. Consider inviting key stakeholders and participants to contribute to the analysis, where appropriate. Having affected groups as part of this process could provide insights you would not otherwise have. The level of analysis you will be required to do will vary depending on the size and complexity of the data activity. The data analysis process is likely to be very straightforward and quick if you are analysing the results of one data source, such as a desk review to produce a pamphlet. It will be more comprehensive for larger data activities with multiple data types and sources such as required for a national inquiry. It is good practice to start by analysing data during a large data activity. Athough different data types have distinct characteristics and require different analysis techniques, for all types and sources a five-step data analysis process can be used. Step 1: Review the data While you may have reviewed the data throughout the collection process, it is useful to have a final review before beginning the analysis. Ask questions such as: • Has the data been recorded accurately? • Has enough data been collected to develop credible findings? • Is there data you do not need? • Are the data sources referenced? • Have the contexts/influences on each data source been noted, including the impact it could have on the analysis? 67