Data analysis

Choose a suitable method of analysis

The chosen method of analysis depends on the type of data and the hypothesis. For quantitative data, such as questionnaire results or log data, statistical testing can be applied. You may also have qualitative data, for which the aim is to identify patterns or themes.

More often, you will conduct a mixed-methods analysis, combining quantitative and qualitative data. When combining methods, ensure that it is clear how the different types of data complement or add nuance to one another.

Context and range

Data only gains meaning when it is placed in a practical context. Therefore, when interpreting the data, also take into account knowledge obtained earlier, such as the results from the review of existing literature, your description of the work process, and the different stakeholders involved.

Exact figures are often lacking or there may be uncertainties in the data. In such cases, it is advisable to work with ranges. For example, by calculating minimum and maximum time savings, or by defining lower and upper limits for costs and benefits. In this way, uncertainty is made explicit without compromising the usability of the results.

Presenting outcomes

Ensure that the outcomes of the analysis are presented in a way that allows them to be readily used for developing the cost–benefit matrix. Also identify any potential follow-up questions or knowledge gaps that may require additional data collection or analysis.