Prior to the Beyond Data Event we’ve asked KPN, partner of the event, to their opinion about open data. What are the main challenges we are facing? What can go wrong? And which collaborations are effective?
“Recent and future developments (will) make it possible to ‘open’ all data and to have it available at any time, to combine it, and to have it contribute immediate (split-second) value. The challenges? Which issues ideally need which data? Where do you begin, and why? Which data is required and why?”
What are the main challenges cities are facing with open data the coming years?
Cities will always have a huge volume of data available. Because of developments in recent years, there will be an exponential growth in that volume, the data’s diversity and the desire to have it accessible. The insights that may arise through the combining and integrating of this data, can deliver high added value in policy-making, for day‑to-day management and for interaction with all stakeholders. In data terms, ‘open’ really means ‘accessible and available to everyone, to be used’. However, data exists in a variety of forms: on paper in the archives, digitally in older automation systems, and in the current digital standards. Recent and future developments (will) make it possible to ‘open’ all data and to have it available at any time, to combine it, and to have it contribute immediate (split-second) value. The challenges? Which issues ideally need which data? Where do you begin, and why? Which data is required and why? etc, etc. Among other things, the evolution of technology is driving the aforementioned trends and supports them, but beware of the technological swamp. The greatest challenge is drawing up the transition strategy to benefit from the current developments and to take logical steps towards ‘the new world’, starting from the defined added value involving (social) issues.
How far do you go with Open Data?
In principle, there must be a willingness not to impose any boundaries. However, having said this: it is certainly absolutely necessary to act within the ethical norms and defined rules of play.
What can go wrong with Open Data Projects?
There are plenty of examples of projects where making data ‘open’ has become a technical project and an objective in itself. Vast volumes of data are opened ‘technically’, and data sets are made available and accessible ‘online’. The costs incurred in achieving this are enormous, not to mention maintenance and management (for example to keep the data up-to-date).
There is an expectation that hordes of interested parties will then get to work to create value and to utilise the data, in whatever form. Experience shows that nothing could be further from the truth. What can go wrong?
Thinking that it ends with making several data sets available and that the magic will do its own work. Data sets need to be described, must be of high quality and relevant, and must be updated continuously, ideally in real-time. Open Data is not about the technology, but about facilitating change and thus about people.
Too much experimental thinking. Yes, it all starts with experimenting, but the objective and basic principle is to produce something in practice. The real world has new laws. Is the data then still of sufficient quality?
Concentrating only on open data. Closed sources could become available per application, and are still vitally important.
Who is the boss of the data?
The individual who can be described and identified with the data, in whatever way. He or she must be able to grant permission at the level of an application. What is the data used for? Only giving an organisation permission to utilise data is impossible, because without the application you cannot estimate and determine the effect of this agreement.
When the data is unable to state anything about an entity (individual or organisation), one frequently refers to open data. Consider, for instance, a traffic sign, a bench or a tree.
Which collaborations are effective?
Creating solutions consists of three phases. Generating ideas, validating them in practice, and actually utilising the idea in practice. Each phase has its own specialists with various backgrounds who must work closely together and give each other space.
Generating ideas is assigned to optimists with a focus on the process who can produce a working application rapidly. Start-ups and hackathons can make important contributions here. The owners of resources with which to experiment must facilitate this, for example with technology like sensors (IoT Academy) / open data sets in a lab.
Validation is testing the business (Value) case. Not to be forgotten in this phase is the Value model. Research agencies, but also commercial bodies, can play a valuable role here.
For utilisation, one particularly needs to consider the larger corporates to roll out the solution sustainably and to be able to scale up in a relatively short period, where the person who devised the idea retains control over the functional solution.
How do you finance the projects?
Start with the city’s issues. Do not see open data as a shotgun wedding, but as the engine of innovation and new working methods. What challenges do I face, and what are the associated efforts (including costs)? What issues can be resolved by technology, and do I already see examples of this in other cities or other industries? Then start a lab and research, and start generating ideas. Think like an entrepreneur. If one in ten ideas appear to work in the validation phase, then the investment has been recouped. Also work together with other cities, and trans-sectorally. We do not have to keep reinventing the wheel again in each city. But note: if scaling-up is among one of the objectives, during the development phase you must take into account that everything has to be able to be managed and maintained efficiently and effectively, and sufficient attention must be devoted to security and assurance.