Data is important. No one is disputing that. The most effective responses to development challenges are the ones that rely on quality data which is regularly collected and verified. Good data informs good programmes which reflect the needs and priorities of the beneficiaries which have been targeted. However, in every context, collecting good data takes time, money and people. It goes without saying that the more developed a country is, with better established resources and access to personnel with the necessary qualifications to collect data, the more data is collected, and the quality of the data is better. Without foraying too far into the quantitative aspects of development, one could make an assumption that good data can lead to better development outcomes.
Such is the theory behind the ‘data revolution’ which underscores the Sustainable Development Goals, to be adopted later this year. The problem is the expectations – there are a LOT of them – which are being put on poorer and least developed countries to follow-through on the data commitments that donor countries are increasingly prioritizing.
A recent article on Scidev.net ‘Pressure Builds on Poor Nations to Collect Quality Data’ (19 June 2015, http://www.scidev.net/global/data/news/poor-nations-gather-quality-data-sdg-odi.html?utm_medium=email&utm_source=SciDevNewsletter&utm_campaign=international%20SciDev.Net%20update%3A%2022%20June%202015) discussed one of the main expectations being placed on poor and least developed countries: spend more to collect quality data and ensure data banks are open. It doesn’t sound like rocket science, but to countries constrained in human and financial resources, it just may feel like it in practice.
First, data collection isn’t simply counting. There has to be rhyme and reason – why is the data being collected and what will it be used for? How will it be collected? Approaches used in one part of a country may not be practical or effective in others (think capital cities and remote island communities in archipelago countries; think urban living versus multi-family households). Second, who will collect the data? Are government staff in the position to take time out of their regular tasks to either collect the data or oversee a team that will? Is there capacity for data collection? (Here again, one might think: counting, it’s not rocket science. But how you count is as important as what you count – survey results can be invalidated if the surveys themselves were not consistently implemented across all communities. It takes training and extensive experience to get it right) Third, data collection tools such as surveys are expensive. Governments that can barely afford to pay civil servant salaries are not going to prioritize what little remaining budget they have on collecting data – they are going to prioritize fixing roads, building ports, rehabilitating power plants or investing in generators – investments that can lead to economic development in the short term. Investments that, over time, can increase their tax base which will lead to increasing income for the government… and one day in the future maybe some of that extra income can be devoted to improving data collection processes and the overall quality of data.
So, in the meantime, it is not practical to expect a ‘data revolution’ to take place in poor and least developed countries. Yes, it is important and critical to improved development outcomes, but perhaps the onus is on developed countries and other donors to take a long term (20-25 year) approach to financing and providing technical support for data collection. In fairness, while good data and the ‘data revolution’ are a grand idea, in reality they are a prerogative of the rich, with the financial, technical and human resources at their disposal. We really cannot expect it to be a priority of the governments which are being put under a microscope. Unless, of course, someone else wants to fund it and implement it on their behalf.