Because there are SO many forms of data, it can be hard to compare various sources in a data marketplace or to prioritize spending on your own direct research. We've assembled a list of various dimensions of data that can be assigned numeric values so that you can create a scorecard or rubric by which to compare data sources.
There are many contexts from which you can assess a dataset's fitness for purpose. Be explicit about which you use so that you evaluate consistently. Examples of lenses that you might use:
When evaluating data sets, it's helpful to prioritize attributes. It can be time-intensive or difficult to assess the potential analysis cost of data, so it's often helpful to start from a dataset's alignment with the time frequency, geography, etc. before attempting to compute more advanced criteria. Evaluating data sets for unknown future uses is very challenging, so it may be helpful to engage in design thinking and/or practical futurism exercises to focus your evaluation.