I was asked to provide a short talk at the start of the Future Food Hackathon that kicked off in Wageningen, NL today, linked to the Global Open Data on Agriculture and Nutrition workshop taking place over the next few days.
Below are the speaker notes I jotted down for the talk.
On open data and impact
I want to start with an admission. I’m a sceptic about open data.
In the last five years we’ve seen literally millions of datasets placed online as part of a broad open data movement – with grand promises made about the way this will revolutionise politics, governance and economies.
But, when you look for impact, with the exception of a few specific domains such as transport, the broad society wide impact of that open data is hard to find. Hundreds of hack-days have showcased what could be possible with data, but few have delivered truly transformative innovations that have made it to scale.
And many of the innovations that result often seem to focus #FirstWorldProblems – if not purely ‘empowering the already empowered’, then at least not really engaging with social issues in ways that are set to tip the balance in favour of those with least advantage.
I’m sceptical, but I’m not pessimistic. In fact, understood as part of a critique of the closed way we’ve been doing aid, policy making, production and development – open data is an incredibly exciting idea.
However, far to much open data thinking has stopped at the critique, without moving on to propose something new and substantive. It offers a negation (data which is not proprietary; not in PDF; not kept from public view), without talking enough about how new open datasets should be constructed. Because opening data is not just about taking a dataset from inside the government or company and putting it online, in practice it involves the creation of new datasets: selecting and standardising fields and deciding how to model data. This ultimately involves the construction of new systems of data.
And this links to a second blind spot of current open data thinking: the emphasis on the dataset, to the exclusion of the social relationships around it.
Datasets do not stand alone. The are produced by someone, or some group, for some purpose. They get meaning from their relationship to other data, and from the uses to which they are put. As Lisa Gitelman and colleagues have put it in ‘Raw Data is an Oxymoron’, datasets have histories, and we need to understand these to reshape their futures.
Matthew Smith and colleagues at the IDRC have spent a number of years exploring the idea of openness in development. They distinguish between openness defined in ‘universal legal and technical terms’, and openness as a practice – and argue that we need to put open practices at the centre of our theory of openness. These practices are, to some extent, enabled by the formalities of creative common licenses, or open data formats, but they are something more, and draw upon the cultures of peer-to-peer production and open source, not just the legal and technical devices.
Ultimately, then, I’m optimistic about the potential of open data if we can to think about the work of projects like GODAN not just as a case of gaining permission to work with a few datasets, but as about building new open and collaborative infrastructures, through which we can use data to communicate, collaborate and reshape our world.
I’m also hopeful about the potential of colliding cultures from open source and open data, with current cultures in the agriculture and nutrition communities. Can we bring these into a dialogue that builds shared understanding of how to solve problems, and lets us rethink both openness, and agriculture, to be more effective, inclusive and just?
Five observations on hacking with open data
Ok: so let me pause. I recognise that the last few minutes might have been a bit abstract and theoretical for 9am on a Monday morning. Let me try then and offer then five somewhat more practical thoughts about approaching an open data hackathon:
1. Hacking is learning.
A common experience of the hackathon is frustration at the data not just being ready to use. Yet the process of struggling with data is a process of learning about the world it represents – and sometimes one of the most important outcomes of a hack is the induction of a new community of people, from different backgrounds, into shared understanding of some data and domain.
One of the most fascinating things about the open government data processes I’ve been tracking in the UK has been the way in which it has supported civic learning amongst technology communities – coming to understand more how the state works by coming to understand its data.
So – at an interdisciplinary hack like this, there is the opportunity to see peculiarities of the data as opportunities to understand the process and politics of the agriculture and nutrition field, and to be better equipped to propose new approaches that don’t try to make perfect data out of problematic situations – but that try and engage with the real challenges and problems of the field.
2. Hacking is political.
I’ve had the pleasure over the last few years of working an number of times with the team at the iHub in Nairobi, and of following the development of [Kenya’s open data initiative]. In their study of an ‘incubator’ project to encourage developers to use Kenyan open government data, Leo Mutuku and her team made an interesting discovery.
Some developers did not understand their apps as products to be taken to scale – but instead saw them as rhetorical acts. A demonstration to government of how ICTs could be used, and a call on government to rethinking its own ICTs, rather than an attempt by outside developers to replace those ICTs for government.
Norfolk based developer, Rupert Reddington, once referred to this as ‘digital pamphleteering’ in which the application is a provocation in a debate – rather than primarily, or at all, a tool for everyday use.
Think about how you present a openness-oriented provocation to the status quo when you pitch your ideas and creations.
3. You are building infrastructure.
Apps created with open data are just one part of the change process. Even a transport app that lets people know when the next bus is only has an impact if it becomes part of people’s everyday practice, and they rely on it in ways that change their behaviour.
Infrastructure is something which fades into the background: when it becomes established and works well, we don’t see it. It is only when it is disrupted that it becomes notable (as I learned trying to cross the channel yesterday – when the Channel Tunnel became a very visible piece of infrastructure exactly because it was blocked and not working).
One of the questions I’m increasingly asking in my research work, is how we can build ‘inclusive infrastructures’, and what steps we need to take to ensure that the data infrastructures we have are tipped in favour of the least advantaged rather than the most powerful. Sometimes the best innovations are ones that complement and extend an existing infrastructure, bringing hitherto unheard voices into the debate, or surfacing hitherto unseen assumptions.
Sustainability is also important to infrastructure. What you create today may just be a prototype – but if you are proposing it as part of a new infrastructure of action – consider if you can how it might be made sustainable. Would building for sustainability change the concept or idea?
4. Look at the whole value chain.
There is a tendency in hackthons to focus on the ‘end user’ – building consumer oriented apps and platforms. Often that approach makes sense: disintermediation can make many systems work better. But it’s not always the way to make the most difference.
When I worked with CABI and the Institute for Development Studies in 2013 to host a ‘Research to Impact’ hackathon at the iHub in Nairobi, we brought together people involved in improving the quality of agriculture and the lives of smallholder farmers. After a lot of discussion, it became clear that between ‘research’ and the ‘farm’ were all sorts of important intermediaries, from seed-sellers, to agricultural extension workers. Instead of building direct-to-farmer information systems, teams explored the kinds of tools that could help an agriculture extension worker deliver better support, or that could help a seed-seller to improve their product range.
Apps with 10s or 100s of back-office users may be much more powerful than apps with 1000s of ‘end users’.
When the two Open Data in Developing Countries project research partners in Kenya launched their research in the middle of last year, an interesting argument broke out between advocates of ‘disintermediation’, and ‘empowering intermediaries’. One the one hand, intermediaries contextualise information, and may be trusted: helping communities adopt information as actionable insights, when they may not understand or trust the information direct from source. On the other hand, intermediaries are often seen as a problem: middle-men using their position for self-interest, and limiting the freedoms of those they are the intermediary to.
Open approaches can offer an important ‘pressure valve’ in these contexts: focussing on creating platforms for intermediary, but not restricting information to intermediaries only.
5. Evolution can be as powerful as revolution.
The UN Secretary General has led the call for a ‘data revolution for development’, with the Independent Expert Group he appointed proposing a major updated in practices of data use and practice.
This revolution narratives often implies that organisations needs to shift direction; completely transforming data practices; throwing out existing report-writing and paper-based approaches in place of new ‘digital by default’ technology-driven processes. But what happens if we think differently and start from the existing strengths of organisations:
- What is going well when it comes to data in the international potato trade?
- Who are the organisations with promising practice in localising climate-change relevant information for farmers?
- What have been the stories of progress in tracking food-borne disease?
How can we extend these successes? What innovations have made their first iteration, but are just waiting for the next?
One of the big challenges of ‘data revolution’ is the organisational change curve it demands, and the complex relationship between data supply and demand. Often the data available right now is not great. For example, if you are currently running a crop monitoring project with documents and meetings, but a new open dataset becomes available that is relevant to your work, starting a ‘data revolution’ tomorrow will involve lots of time working with bad data and finding new ways to work around the peculiarities of the new system: the investment this year to do the same work you were doing with ‘inefficient’ analogue approaches last year might be double, as you scale the learning curve.
Of course, in year 3 or 4, the more efficient way of working may start to pay off: but often projects never get there. And because use of the new open dataset dropped away in year 2, when early adopters realised they could not afford to transform their practices to work with it, government publishers get discouraged, and by year 3 and 4 the data might not be there.
An evolution approach works out how to change practices year-by-year: iterating and negotiating the place of data in the future of food.
(See Open Data in Developing Countries – Insights from Phase I for more on this point)
Ok. Still a bit abstract for 9.15am on a Monday morning: but I hope the general point is clear.
Ultimately, the most important thing about the creations at a hackathon is their ‘theory of change’: how does the time spent hacking show the way towards real change? I’m certainly very optimistic that when it comes to the pitch back tomorrow, the ideas and energy in this room will offer some key pointers for us all.