New technologies now make it possible to build the services to answer these questions automatically. Much of the data you would need to answer these questions is generated by public bodies. However, often the data required is not yet available in a form which is easy to use. This book is about how to unlock the potential of official and other information to enable new services, to improve the lives of citizens and to make government and society work better.
The notion of open data and specifically open government data – information, public or otherwise, which anyone is free to access and re-use for any purpose – has been around for some years. In 2009 open data started to become visible in the mainstream, with various governments (such as the USA, UK, Canada and New Zealand) announcing new initiatives towards opening up their public information.
There are many areas where we can expect open data to be of value, and where examples of how it has been used already exist. There are also many different groups of people and organisations who can benefit from the availability of open data, including government itself. At the same time it is impossible to predict precisely how and where value will be created in the future. The nature of innovation is that developments often comes from unlikely places.
It is already possible to point to a large number of areas where open government data is creating value. Some of these areas include:
- Transparency and democratic control
- Participation
- Self-empowerment
- Improved or new private products and services
- Innovation
- Improved efficiency of government services
- Improved effectiveness of government services
- Impact measurement of policies
- New knowledge from combined data sources and patterns in large data volumes
What is Open?
Open data is data that can be freely used, reused and redistributed by anyone – subject only, at most, to the requirement to attribute and sharealike.
The full Open Definition gives precise details as to what this means. To summarize the most important:
- Availability and Access: the data must be available as a whole and at no more than a reasonable reproduction cost, preferably by downloading over the internet. The data must also be available in a convenient and modifiable form.
- Reuse and Redistribution: the data must be provided under terms that permit reuse and redistribution including the intermixing with other datasets.
- Universal Participation: everyone must be able to use, reuse and redistribute – there should be no discrimination against fields of endeavour or against persons or groups. For example, ‘non-commercial’ restrictions that would prevent ‘commercial’ use, or restrictions of use for certain purposes (e.g. only in education), are not allowed.
If you’re wondering why it is so important to be clear about what open means and why this definition is used, there’s a simple answer: interoperability.
Interoperability denotes the ability of diverse systems and organizations to work together (inter-operate). In this case, it is the ability to interoperate – or intermix – different datasets.
Interoperability is important because it allows for different components to work together. This ability to componentize and to ‘plug together’ components is essential to building large, complex systems. Without interoperability this becomes near impossible — as evidenced in the most famous myth of the Tower of Babel where the (in)ability to communicate (to interoperate) resulted in the complete breakdown of the tower-building effort.
We face a similar situation with regard to data. The core of a “commons” of data (or code) is that one piece of “open” material contained therein can be freely intermixed with other “open” material. This interoperability is absolutely key to realizing the main practical benefits of “openness”: the dramatically enhanced ability to combine different datasets together and thereby to develop more and better products and services (these benefits are discussed in more detail in the section on ‘why’ open data).
Providing a clear definition of openness ensures that when you get two open datasets from two different sources, you will be able to combine them together, and it ensures that we avoid our own ‘tower of babel’: lots of datasets but little or no ability to combine them together into the larger systems where the real value lies.
