Guest post from Michael Cañares
Michael Cañares is the Word Wide Web Foundation’s regional research manager for Asia. Based in the Philippines, he leads the Open Data Lab in Jakarta where he manages the design, implementation, and monitoring of action research projects that harness the power of open data to achieve better social outcomes. Prior to joining the foundation, he was a managing consultant of Step Up Consulting in the Philippines.
The launch of the International Open Data Charter in October this year rallied the cause for accessible, useful, and timely data to support, among other things, key social, economic, and political outcomes. This came right at a critical time when country governments endorsed the Sustainable Development Goals, which recognize that monitoring progress in 16 development goals requires timely, quality, reliable, and accessible disaggregated data.
Amidst these, there is a persistent realization that data quality, especially in the developing world, is a problem. The UN document on the SDGs, for example, highlighted the need to invest in initiatives that would strengthen national data systems. The 2015 International Open Data Conference report recognizes that substantial reforms are needed to guarantee data quality, calling for strategic interventions in ensuring the continuous supply of high quality data sets that stakeholders can use to achieve impact.
Capacity to supply and use data is at the heart of these processes. It is acknowledged that open data is only a foundation for impact from which different uses and results can emerge – to improve governance outcomes, to fight corruption, to stimulate better economic performance, and achieve broader social outcomes, among others. But the quality of uses as well as results, hinges on several factors. It is not just about open data.
The results of the Open Data in Developing Countries research highlighted one metaphor to explain how open data can lead to tangible results. In what is referred to as the “domino effect”, it was argued that there are many different pieces that need to be lined up before open data can result in outcomes and impact. Good data quality and metadata, functioning of intermediaries as CSO, researchers, businesses, and media, receptiveness of decision-makers to data-driven policy-making or program development – these are some of those necessary pieces that need to be in place before open data can generate its envisioned results. To put this in place, there is a need to build capacity of suppliers, intermediaries, and users of data.
Capacity building is necessary for government bureaucrats, civil servants, and rank-and-file employees so that they are convinced that open data can generate results and are able to disclose priority high-value datasets. Capacity building is critical so that intermediaries are able to access, analyze, use, and disseminate open data. Capacity building is important on the part of citizens, so that they can exercise beneficial usage. Briefly, as indicated in the IODC 2015 report, there is an overwhelming need to “build capacity to produce and use open data effectively”.
Nevertheless, it is important to highlight, that several of the interventions intended to increase skills related to open data are training, not capacity building. The latter is a wider concept that involves not only training people but ensuring that policy, technology, institutions, and resources are available to undertake a particular task. The conduct of training on open data is important, but it will only yield results when people are trained in an environment where policies of disclosure are in place, when technology is available and accessible to allow timely provision and use of data, when there are actual opportunities for conversations between governments and citizens, among other things. Thus, off-the-shelf, canned, short-term, standard modules can work only to a certain extent, if not accompanied with interventions that would create an enabling environment for open data to be shared, used, and reused.
In the next International Open Data Conference in Madrid, Spain in 2016, it would be useful to look back at what has happened to the committed actions in Canada this year. It would also be useful to see if in the area of capacity building, we get our approaches and methods right, and whether we are able to move ourselves an inch closer to the vision of not leaving anyone behind in terms of open data. It would be good to have a sense of what is the status of the goal of ensuring that suppliers and users of data are capacitated, measuring the results using a common yardstick that the open data community develops and owns.