That holds true the other way. What does get measured, and how that is measured, is as much a political choice as what doesn’t. Metric design is political, also in the private sector. #ethicsbydesign
For the UNDP in Serbia, I made an overview of existing studies into the impact of open data. I’ve done something similar for the Flemish government a few years ago, so I had a good list of studies to start from. I updated that first list with more recent publications, resulting in a list of 45 studies from the past 10 years. The UNDP also asked me to suggest a measurement framework. Here’s a summary overview of some of the things I formulated in the report. I’ll start with 10 things that make measuring impact hard, and in a later post zoom in on what makes measuring impact doable.
While it is tempting to ask for a ‘killer app’ or ‘the next tech giant’ as proof of impact of open data, establishing the socio-economic impact of open data cannot depend on that. Both because answering such a question is only possible with long term hindsight which doesn’t help make decisions in the here and now, as well as because it would ignore the diversity of types of impacts of varying sizes known to be possible with open data. Judging by the available studies and cases there are several issues that make any easy answers to the question of open data impact impossible.
1 Dealing with variety and aggregating small increments
There are different varieties of impact, in all shapes and sizes. If an individual stakeholder, such as a citizen, does a very small thing based on open data, like making a different decision on some day, how do we express that value? Can it be expressed at all? E.g. in the Netherlands the open data based rain radar is used daily by most cyclists, to see if they can get to the rail way station dry, better wait ten minutes, or rather take the car. The impact of a decision to cycle can mean lower individual costs (no car usage), personal health benefits, economic benefits (lower traffic congestion) environmental benefits (lower emissions) etc., but is nearly impossible to quantify meaningfully in itself as a single act. Only where such decisions are stimulated, e.g. by providing open data that allows much smarter, multi-modal, route planning, aggregate effects may become visible, such as reduction of traffic congestion hours in a year, general health benefits of the population, reduction of traffic fatalities, which can be much better expressed in a monetary value to the economy.
2 Spotting new entrants, and tracking SME’s
The existing research shows that previously inactive stakeholders, and small to medium sized enterprises are better positioned to create benefits with open data. Smaller absolute improvements are of bigger value to them relatively, compared to e.g. larger corporations. Such large corporations usually overcome data access barriers with their size and capital. To them open data may even mean creating new competitive vulnerabilities at the lower end of their markets. (As a result larger corporations are more likely to say they have no problem with paying for data, as that protects market incumbents with the price of data as a barrier to entry.) This also means that establishing impacts requires simultaneously mapping new emerging stakeholders and aggregating that range of smaller impacts, which both can be hard to do (see point 1).
3 Network effects are costly to track
The research shows the presence of network effects, meaning that the impact of open data is not contained or even mostly specific to the first order of re-use of that data. Causal effects as well as second and higher order forms of re-use regularly occur and quickly become, certainly in aggregate, much higher than the value of the original form of re-use. For instance the European Space Agency (ESA) commissioned my company for a study into the impact of open satellite data for ice breakers in the Gulf of Bothnia. The direct impact for ice breakers is saving costs on helicopters and fuel, as the satellite data makes determining where the ice is thinnest much easier. But the aggregate value of the consequences of that is much higher: it creates a much higher predictability of ships and the (food)products they carry arriving in Finnish harbours, which means lower stocks are needed to ensure supply of these goods. This reverberates across the entire supply chain, saving costs in logistics and allowing lower retail prices across Finland. When mapping such higher order and network effects, every step further down the chain of causality shows that while the bandwidth of value created increases, at the same time the certainty that open data is the primary contributing factor decreases. Such studies also are time consuming and costly. It is often unlikely and unrealistic to expect data holders to go through such lengths to establish impact. The mentioned ESA example, is part of a series of over 20 such case studies ESA commissioned over the course of 5 years, at considerable cost for instance.
4 Comparison needs context
Without context, of a specific domain or a specific issue, it is hard to asses benefits, and compare their associated costs, which is often the underlying question concerning the impact of open data: does it weigh up against the costs of open data efforts? Even though in general open data efforts shouldn’t be costly, how does some type of open data benefit compare to the costs and benefits of other actions? Such comparisons can be made in a specific context (e.g. comparing the cost and benefit of open data for route planning with other measures to fight traffic congestion, such as increasing the number of lanes on a motor way, or increasing the availability of public transport).
5 Open data maturity determines impact and type of measurement possible
Because open data provisioning is a prerequisite for it having any impact, the availability of data and the maturity of open data efforts determine not only how much impact can be expected, but also determine what can be measured (mature impact might be measured as impact on e.g. traffic congestion hours in a year, but early impact might be measured in how the number of re-users of a data set is still steadily growing year over year)
6 Demand side maturity determines impact and type of measurement possible
Whether open data creates much impact is not only dependent on the availability of open data and the maturity of the supply-side, even if it is as mentioned a prerequisite. Impact, judging by the existing research, is certain to emerge, but the size and timing of such impact depends on a wide range of other factors on the demand-side as well, including things as the skills and capabilities of stakeholders, time to market, location and timing. An idea for open data re-use that may find no traction in France because the initiators can’t bring it to fruition, or because the potential French demand is too low, may well find its way to success in Bulgaria or Spain, because local circumstances and markets differ. In the Serbian national open data readiness assessment performed by me for the World Bank and the UNDP in 2015 this is reflected in the various dimensions assessed, that cover both supply and demand, as well as general aspects of Serbian infrastructure and society.
7 We don’t understand how infrastructure creates impact
The notion of broad open data provision as public infrastructure (such as the UK, Netherlands, Denmark and Belgium are already doing, and Switzerland is starting to do) further underlines the difficulty of establishing the general impact of open data on e.g. growth. The point that infrastructure (such as roads, telecoms, electricity) is important to growth is broadly acknowledged, with the corresponding acceptance of that within policy making. This acceptance of quantity and quality of infrastructure increasing human and physical capital however does not mean that it is clear how much what type of infrastructure contributes at what time to economic production and growth. Public capital is often used as a proxy to ascertain the impact of infrastructure on growth. Consensus is that there is a positive elasticity, meaning that an increase in public capital results in an increase in GDP, averaging at around 0.08, but varying across studies and types of infrastructure. Assuming such positive elasticity extends to open data provision as infrastructure (and we have very good reasons to do so), it will result in GDP growth, but without a clear view overall as to how much.
Most measurements concerning open data impact need to be understood as proxies. They are not measuring how open data is creating impact directly, but from measuring a certain movement it can be surmised that something is doing the moving. Where opening data can be assumed to be doing the moving, and where opening data was a deliberate effort to create such movement, impact can then be assessed. We may not be able to easily see it, but still it moves.
9 Motives often shape measurements
Apart from the difficulty of measuring impact and the effort involved in doing so, there is also the question of why such impact assessments are needed. Is an impact assessment needed to create support for ongoing open data efforts, or to make existing efforts sustainable? Is an impact measurement needed for comparison with specific costs for a specific data holder? Is it to be used for evaluation of open data policies in general? In other words, in whose perception should an impact measurement be meaningful?
The purpose of impact assessments for open data further determines and/or limits the way such assessments can be shaped.
10 Measurements get gamed, become targets
Finally, with any type of measurement, there needs to be awareness that those with a stake of interest into a measurement are likely to try and game the system. Especially so where measurements determine funding for further projects, or the continuation of an effort. This must lead to caution when determining indicators. Measurements easily become a target in themselves. For instance in the early days of national open data portals being launched worldwide, a simple metric often reported was the number of datasets a portal contained. This is an example of a ‘point’ measurement that can be easily gamed for instance by subdividing a dataset into several subsets. The first version of the national portal of a major EU member did precisely that and boasted several hundred thousand data sets at launch, which were mostly small subsets of a bigger whole. It briefly made for good headlines, but did not make for impact.
In a second part I will take a closer look at what these 10 points mean for designing a measurement framework to track open data impact.