outcomes, such as macroeconomic data, the desired frequency may be monthly or quarterly. In each case, the desired frequency of data capture depends ultimately on the temporal dynamics of the outcome of interest, and this frequency may change as other conditions change (e.g. ideal frequency of economic data is most likely high during volatile economic times and might be low during stagnant periods).

- _Timely_ : Timely data means that the time from data collection to data release is short, or that the time between an event of interest occurring and data collection on the event is short. This is particularly relevant for emergencies where policy response does not have the luxury to wait for data to arrive, such as environmental disasters, health crises, conflicts and more. The timeliness of data depends on the mode of data collection. Some machine-generated data can be made available nearly instantaneously, while other data, such as survey data, often takes place over a longer period of time and needs careful analysis and cleaning before it can be released, leaving a time lag of several months. A related name for this feature is that the data need to be punctual.

# **High quality**

- _Accurate_ : For data to be valuable, they need to measure their concept of interest with minimal error. This means that data collection and processing should use methods and tools that are designed to capture the signal of some phenomena while filtering out noise or measurement error that can bias conclusions and evidence-based policies.

- _Comparable_ : For data to be used for cross-country analysis or for tracking patterns over time, they need to be comparable across space and time. This means, for example, that the data should conform to certain standards and data collection instruments should be relatively stable over time. Examples of conforming to standards include the near universal acceptance of governments in using the System of National Accounts for measuring the size of their economies, and similarly the widely accepted standards for measuring geolocation and units of time. There are fewer clear examples of how to maintain comparability over time, but one common view is to avoid making unnecessary changes to the data collection processes which may induce a break in comparability of the data series. A caveat to this view is that data collection instruments may require change to maintain comparability if the behavior of the enumerated entity changes significantly (e.g., list of consumption items may need to change if new items are introduced into society)<sup>2</sup> or if international standards of data collection are updated over time, in part to facilitate the adoption methods to increase accuracy.

- _Granular_ : Often for data to maximize their value they need to be granular, which, loosely defined, means that they can be broken down by subgroups, such as geographical areas, time, gender, disability status, and more. This requires these subgroups to be captured appropriately in the data. The level of detail available with granular data means that deidentifying subjects becomes instrumental to protect the privacy of the subjects.

> 2 Gibson et al. (2017) offer an example where a stable, unchanged consumption questionnaire during a period of rapidly changing consumption patterns in Vietnam led to concerns about comparability of the measures of household well-being over time.

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