not most) low-income countries are far from stellar (Andrews et al 2017).<sup>6</sup> For example, Jerven’s (2013) important work in Africa on the numerous challenges associated with maintaining the System of National Accounts – the longest-standing economic data collection task asked of all countries, from which their respective GDP is determined – portends the difficulties facing less high-profile metrics <mark>(see also Sandefur and Glassman 2015).</mark><sup>7</sup> Put differently: if many developing countries struggle to curate the single, longest-standing, universally-endorsed, most important measure asked of them, on what basis do we expect these countries to manage lesser, lower-stakes measures?

To be sure, building quantitative analytical skills in public agencies is highly desirable; for present purposes, our initial point is a slight variation on the old adage<sup>8</sup> that the quality of outcomes derived from quantitative data is only as good as the quality of the ‘raw material’ and the competence with which it is analyzed and interpreted. Fulfilling an otherwise noble ambition to build a professional public sector whose decisions are informed by ‘evidence’ requires a prior and companion effort to build the requisite skills and sensibilities. Put differently, precisely because effective data management is itself such a complex and difficult task, in contexts where agencies struggle to implement even basic policy measures at a satisfactory level (e.g., delivering mail, ensuring attendance at work) it is unlikely that, ceteris paribus, invoking such agencies to also take a more ‘data driven’ approach will elicit substantive improvement. More and better ‘data’ will not fix a problem if the absence of such data is not itself the key problem or the ‘binding constraint’; as such, the priority issue is discerning what _is_ in fact the key policy problem and its constituent elements. From this starting point, more and better data can be part of, but not a substitute for, strategies for enhancing the effectiveness of public sector agencies.

Even if both data management and broad institutional capability are functioning at high and complementary levels, there remains the structural necessity of interpreting what the data _means_ . Policy inference from even the best data and most rigorous methodology is never self-evident; it must always be undertaken in the light of theory. This might sound like an abstract academic concern, but it is especially important when seeking to draw lessons from, and/or make big decisions regarding the fate of, complex interventions. This is so because a defining characteristic of a complex problem is that it generates highly variable outcomes across time, space, and groups.

> 6 If such agencies/departments do in fact happen to perform especially strongly – in the spirit of the ‘positive deviance’ cases of government performance in Ghana provided in McDonnell (2020) – then it would be useful to understand how and why this has been attained. For present purposes, our point is that, perhaps paradoxically, we should not expect, ex ante, that agencies/departments in the business of collecting and curating data for guiding policy and performance to themselves be exemplary exponents of the deployment of that data to guide their _own_ performance – because doing so is a separate ontological task, requiring distinctive professional capabilities. Like the proverbial doctors, if data analysts cannot “heal thyselves” we should not expect other public agencies to be able to do so merely by infusing them with more and better data.

> 7 A special issue of _The Journal of Development Studies_ (Volume 51, Issue 2) was dedicated to this issue. For example, on the enduring challenges associated with agricultural data – another sector with a long history of data collection experience – see <mark>Carletto, Jolliffe, and Banerjee (2015).</mark>

> 8 Popularly known as GIGO: garbage in, garbage out.

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