# **Interactions between data features**

As a general rule, the more present a feature is, the greater value the data bring to development. But there are important caveats and nuances to this broad assertion, each linked to the point that the effect of one feature on the value of data is not independent of changes in the other data features. More specifically, there are three important ways in which value is determined from the interaction of features: (1) the feature least present is often the constraining factor on the value that can be derived (i.e., increasing other features may not increase value if the least present feature is unchanged), (2) at times there are positive spillovers between features, and (3) at times there are negative spillovers between features.

_(1) The importance of the features least present._ Often the derived value will be determined by the category of features (adequate coverage, high quality, easy to use, and safe to use) that is least present. Such a situation is comparable to Kremer’s O-Ring theory of economic development, where one malfunction in the value chain in production can dramatically reduce a product’s value (Kremer 1993). More colloquially, this is sometimes expressed as a chain being only as good as its weakest link. If nearly all features are present, but the data are not produced with adequate coverage, they may not be able to serve their intended policy objective at all, dramatically reducing their value. When data are not of high quality, they are much less valuable and could even misguide policy decisions. When the data are not easy to use, they might not be used at all. When data are not safe to use, they may end up causing harm due to data breaches, surveillance, or exclusion.

_(2) Positive spillovers between features._ The features that are present in a data set may foster the development or advancement of other features. For example, if data are made available and understandable to users, the use and demand for the data will likely increase. This, in turn, can have trickle-down effects on the supply of data. For example, government ministries’ reliance on and demand for high-quality data has been associated with a higher capacity of national statistical offices in Latin America (Lotta et al. 2020). In the same region, demand for and interest in accurate and high-quality statistics in civil society, academia, the media, and the private sector have been found to lead to better funding, autonomy, and capacity in national statistical systems (Lotta et al. 2020). In Figure 1, this is reflected by the double-ended arrow between supply and demand. The argument also goes the other way; the absence of a feature might cause the absence of another feature. For example, when data quality is weak, and especially when methodological foundations are vague, it becomes easier to manipulate statistics in one direction or another (Jerven, 2019).

_(3) Negative spillovers between features._ On other occasions, the presence of one feature has a negative impact on other features. For data producers, this can create trade-offs and conflicts between features. For example, data producers may need to find a balance between increasing completeness and the costs of doing so. They may also have to balance increased timeliness with having less time to assure the quality of the data and foster understandability of the data. And perhaps most importantly, the potential harm from data not being safe to use increases when certain other features are in place. For example, when data are granular, interoperable, and have wide coverage, they can be misused more easily and more effectively for illicit ends, magnifying the risk of confidentially violations. In essence, because these features make the data very useful and usable, they multiply the ways in which the data could be used to cause harm.

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