One example of how to accomplish this, which has been taken up by some countries, is to rely on a protocol developed by Apple and Google for their contact tracing apps. With this protocol, phones in close proximity of each other will use low energy Bluetooth technology to exchange a temporary key code, which changes every 15 minutes. This information is stored on each phone, rather than in a centralized database. When a user reports a positive test result, the temporary key codes are used to notify other users of their potential exposure. (Zastrow, 2020). This solution serves the intended purpose of notifying users that may have been exposed to the virus, while being relatively privacy-preserving. It cannot be used to enforce quarantine, track, or identify individuals, limiting the scope for surveillance and other inappropriate uses (Privacy International 2020).

In contrast, some countries have taken a more expansive approach to contact tracing, in which more sensitive information was collected and used and, in some instances, shared with law enforcement agencies. In Israel, mobile phone location data was used by the domestic security agency to identify individuals exposed to the virus and by the police to enforce quarantine, a practice which was subsequently challenged as unconstitutional in the Supreme Court (Amit et al. 2020; Bradford et al. 2020). India’s contact tracing app stores location data alongside a set of demographic information, including age, gender, phone number, and travel history, on a centralized server. It was initially mandatory for air and rail travel as well as for government employees to return to work, raising fears this would effectively exclude those without a smartphone from these activities (Arun 2020; Bradford et al. 2020; Privacy International 2020).

# **4. Conclusion**

Around the world, governments are producing more and more data, yet it is highly doubtful that the full potential value of the data is being reaped. In this paper, we have presented a theoretical and empirical argument for why the returns to existing government data might not be maximized. We show that for data to yield returns for governments they must live up to certain conditions, these being that they are of adequate spatial and temporal coverage (complete, frequent, and timely), of high quality (accurate, comparable, and granular), easy to use (accessible, understandable, and interoperable) and safe to use (impartial, confidential, and appropriate). Through case studies covering a wide range of countries and topics, we illustrate how these 12 features matter in practice for generating value from data.

Too often governments are concerned with producing a particular data set, with less of an eye to how those data will generate value. It is imperative for governments not only to invest in more data, but also the right data, and working towards the safe use of data. Often the value derived from data is determined by the features least present, emphasizing the need for a comprehensive analysis of which of the 12 conditions elicited in this paper may be lacking. This can ensure that the whole chain from data production to data use is in place and the data can come closer to realizing its maximum value for development.

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