To monitor the success of your Open Data initiative, consider implementing metrics to your publications in order to evaluate its success. With these metrics, you can evaluate several indicators. The most useful evaluation activities are performance of the data, performance of the system, and collection and preparation performance.
Performance of the data
This evaluation includes checking the number of downloads and page views. These are not the same, but both indicate the popularity of the data set. It does not indicate the usefulness of the data set: one cannot conclude whether the data has been re-used based on the number of downloads.
Performance of the system
An important metric, especially when the data is available through an API. Here you want to evaluate whether the system can handle the requests, if there has been any downtime, and if there are performance consequences for other systems.
Collection and preparation performance
To evaluate user feedback, the usefulness of datasets is used. Usefulness is an indicator caused by the qualitative usefulness (is it helpful for a particular purpose?) and the practical usefulness (is the data described, clean, dense enough, etc.). The latter is an indicator you can influence, as this reflects the performance of the Open Data Lifecycle.
You should consider including metrics that will enable you to measure the success of the publication of data and your metadata. Think of the following metrics:
Free tools such as PIWIK are useful for these analyses. Some data portal software solutions come with built-in metrics.
This is an example of the French Open Data website where re-users give feedback on the datasets. It shows how many times it has been re-used and how many followers there are.