“When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind ...” —William Thompson (Lord Kelvin), 1824–1907.

90 % of all human data has been created in the last two years: from big to micro data, quantitative to qualitative data – data is everywhere and none of us can escape it. The sentiment expressed by Lord Kelvin – that you understand a phenomenon better when you have data about it – seems comforting in times like these. And who knows, perhaps it even led the drive for gathering ever more data in the first place.

Data, like all other things, are subject to life cycles: collected data are analysed and often used to create indicators. These in turn can be combined to help understand complex issues. Different interpretations then generate needs for new data and the process goes on. In the policy domain, one particular use of data is to prepare benchmarks and targets that become central to "evidence-based" policy development. In its purest form, this process leads to policies being informed by rigorously established objective evidence, rather than by beliefs or popular ideas.

While in the business world, benchmarks and targets have long been used to measure performance and stimulate production, in terms of European policy development they are a relatively new phenomenon. Now, however, the use of benchmarks and targets seems on the rise. Just consider the number of so-called "scoreboards", or “scorecards” that are being developed and that are based on cross-country monitoring and benchmarks: you can now find a single market scoreboard, a Digital agenda scoreboard; a Research and innovation scoreboard, and even a European MOOCs scoreboard, to list but a few.

In education, the inclusion of two headline targets in the Europe 2020 strategy (higher education attainment should reach 40% and Early School Leaving should be reduced to 10% by 2020) has recently also innovated perceptions on data in the field. Why? Targets like these show the increasing importance of education to Europe as a whole. They also show the futility of believing that education policy is still a purely national affair: can we imagine a European country setting education policy without considering developments and performance at least among its neighbours? Not really.

Comparing data on education systems is, however, extremely challenging because education is so deeply ingrained in national culture and tradition. That’s why the organisation of education systems is so very different from one country to another. How, then, to avoid comparing apples with pears? That question has been a central issue in a recently published Eurydice report paving the way for the first European scoreboard in the field of education on learner mobility.

The Mobility Scoreboard concept focuses on key factors – information and guidance, language preparation, portability of financial support and recognition of learning – that are felt to have a significant influence on young people's motivation and ability to study or train abroad. While easy to identify, these factors are difficult to define, measure and compare. For example, which is the better system for financing mobility abroad: country X that provides financial support to few students, but allows those students to take it anywhere; or country Y that gives generous student aid, but has tight restrictions on where it can be used?

Despite the difficulties in developing reliable indicators, the first results of the report are compelling. The most important finding is that no European country performs strongly across all indicators. This means that there is plenty of work for everyone in creating better conditions for mobility. But how will we be able to follow the story? In 2015, the mobility scoreboard will be updated, thus showing whether or not countries are making progress.

Whether you like it or not, European scoreboards are one of many tools that have sprung from the data explosion and seem set to stay – for a while at least. And because they are based on objective evidence, they have the potential to provide a sound basis for policy development. So, surely a good way to use data?

David Crosier and Andrea Pulh