In the fast-moving, tech-heavy world of the data revolution, statistical capacity building has a reputation as a slow-paced, unwieldy, and somewhat archaic endeavor that could somehow be bypassed through investments in smart devices and big data analytics. We found that this reputation is misguided and unjustified.

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• Statistical capacity building is institution building.
• Technological fixes cannot be useful without the right institution and proper skills, the core of building statistical capacity.
• More coordinated and long-term support for countries is needed in strengthening administrative data systems and building data capacity beyond national statistical offices.

I am a fan of the Economist's "Daily Chart" section. In one chart, with appealing visualization, the magazine provides a "window" onto the world around us. The other day, six compelling graphs were taking the pulse of the "world's wellbeing" with global data on new cases of Malaria and HIV, maternal mortality and poverty. As development evaluators and practitioners, we consume data daily, marveling or deploring trends. Yet, we hardly think about what it takes to produce and share data.

Let's think for a minute about the magnitude of the effort. For any data point on one of these charts, countries need to collect and share their data following internationally-agreed standards. This is not a trivial endeavor. It requires among other things, having a coordinated network of data producers following strict procedures to record, process and share administrative information (in health centers, schools, townhalls, etc.) and to collect household and enterprise survey data.

More fundamentally, underneath the production of each data point lies a set of technical, administrative, legal, and, governance challenges. To put it simply, statistical capacity building is institution building.

In our evaluation we assessed the World Bank's results in supporting its client countries in this endeavor over the past decade.

The case of Tanzania is quite enlightening. Back in 2007, the National Bureau of Statistics (NBS) had limited autonomy, technical ability and scarce human resources. Consequently, there were delays in data collection, limited quality control, and ultimately data gaps. Since then, a concerted effort between the government, the World Bank (with no fewer than six data interventions) and its partners have yielded significant progress. Today, surveys are more timely, cover a wider range of topics, and adhere to quality standards. The NBS is even making headways in using geospatial data. Tanzania has moved up 15 points on the scrutinized Statistical Capacity Indicator, positioning the country well above the regional average.

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Similar patterns emerged from other cases such as Rwanda and Ghana: reforms that paired improvements in the institutional and legal environment for data production with investments in the training of staff, equipment of offices and improvement of IT systems helped improve data reliability and timeliness, in turn making countries more willing to share their data. This required long-term support from the Bank to build trust, and alignment with other development partners, including through pooled funding mechanisms.

Despite steady change in some countries, progress remains slow and uneven, many countries are still data deprived, continue to have weak data systems, especially at the sub-national level. In Tanzania, capacity remains weaker in the other parts of the national statistical systems, in ministries and at the local level, which have a key role to play in the production and use of routine administrative and results data.

In the fast-moving, tech-heavy world of the data revolution, statistical capacity building has a reputation as a slow-paced, unwieldy, and somewhat archaic endeavor that could somehow be bypassed through investments in smart devices and big data analytics. We found that this reputation is misguided and unjustified. Technological "fixes" cannot be useful without the right institution and proper skills--the core of building statistical capacity. Undoubtedly the World Bank initiatives have had high transaction costs, and have been slow to show results. However, this is somewhat characteristic of this type of intervention, which seeks change at the system level.

Country clients need continued support from the World Bank that is more coordinated and long-term, especially in strengthening their administrative data systems and building data capacity beyond national statistical offices. This is key, not for my daily enjoyment of the Economist's "Daily chart" but because data production, sharing and use is fundamental to achieving the SDGs.

Read the Evaluation - Data for Development: An Evaluation of World Bank Support for Data and Statistical Capacity