Granularity (photo by Emily, license: CC-BY-NC)
A client, after their previous goal of increasing the volume of open data provided, is now looking to improve data quality. One element in this is increasing the level of detail of the already published data. They asked for input on how one can approach and define granularity. I formulated some thoughts for them as input, which I am now posting here as well.
Data granularity in general is the level of detail a data set provides. This granularity can be thought of in two dimensions:
a) whether a combination of data elements in the set is presented in one field or split out into multiple fields: atomisation
b) the relative level of detail the data in a set represents: resolution
Improving this type of granularity can be done by looking at the structure of a data set itself. Are there fields within a data set that can be reliably separated into two or more fields? Common examples are separating first and last names, zipcodes and cities, streets and house numbers, organisations and departments, or keyword collections (tags, themes) into single keywords. This allows for more sophisticated queries on the data, as well as more ways it can potentially be related to or combined with other data sets.
For currently published data sets improving this type of granularity can be done by looking at the existing data structure directly, or by asking the provider of the data set if they have combined any fields into a single field when they created the dataset for publication.
This type of granularity increase changes the structure of the data but not the data itself. It improves the usability of the data, without improving the use value of the data. The data in terms of information content stays the same, but does become easier to work with.
Resolution can have multiple components such as: frequency of renewal, time frames represented, geographic resolution, or splitting categories into sub-categories or multilevel taxonomies. An example is how one can publish average daily temperature in a region. Let’s assume it is currently published monthly with one single value per day. Resolution of such a single value can be increased in multiple ways: publishing the average daily temperature daily, not monthly. Split up the average daily temperature for the region, into average daily temperature per sensor in that region (geographic resolution). Split up the average single sensor reading into hourly actual readings, or even more frequent. The highest resolution would be publishing real-time individual sensor readings continuously.
Improving resolution can only be done in collaboration with the holder of the actual source of the data. What level of improvement can be attained is determined by:
- The level of granularity and frequency at which the data is currently collected by the data holder
- The level of granularity or aggregation at which the data is used by the data holder for their public tasks
- The level of granularity or aggregation at which the data meets professional standards.
Item 1 provides an absolute limit to what can be done: what isn’t collected cannot be published. Usually however data is not used internally in the exact form it was collected either. In terms of access to information the practical limit to what can be published is usually the way that data is available internally for the data holder’s public tasks. Internal systems and IT choices are shaped accordingly usually. Generally data holders can reliably provide data at the level of Item 2, because that is what they work with themselves.
However, there are reasons why data sometimes cannot be publicly provided the same way it is available to the data holder internally. These can be reasons of privacy or common professional standards. For instance energy companies have data on energy usage per household, but in the Netherlands such data is aggregated to groups of at least 10 households before publication because of privacy concerns. National statistics agencies comply with international standards concerning how data is published for external use. Census data for instance will never be published in the way it was collected, but only at various levels of aggregation.
Discussions on the desired level of resolution need to be in collaboration with potential re-users of the data, not just the data holders. At what point does data become useful for different or novel types of usage? When is it meeting needs adequately?
Together with data holders and potential data re-users the balance needs to be struck between re-use value and considerations of e.g. privacy and professional standards.
This type of granularity increase changes the content of the data. It improves the usage value of the data as it allows new types of queries on the data, and enables more nuanced contextualisation in combination with other datasets.