Open data is data that anyone can access, use and share.
Open data is the result of several processes. The most obvious one is the release process that results in data being made available for reuse and sharing.
But there are other processes that may take place before that open data is made available: collecting and curating a dataset; running it through quality checks; or ensuring that data has been properly anonymised.
There are also processes that happen after data has been published. Providing support to users, for example. Or dealing with error reports or service issues with an API or portal.
Some processes are also continuous. Engaging with re-users is something that is best done on an ongoing basis. Re-users can help you decide which datasets to release and when. They can also give you feedback on ways to improve how your data is published. Or how it can be connected and enriched against other sources.
Collectively these processes define the practice of open data.
The practice of open data covers much more than the technical details of helping someone else access your data. It covers a whole range of organisational activities.
Releasing open data can be really easy. But developing your open data practice can take time. It can involve other changes in your organisation, such as creating a more open approach to data sharing. Or getting better at data governance and management.
The extent to which you develop an open data practice depends on how important open data is to your organisation. Is it part of your core strategy or just something you’re doing on a more limited basis?
The breadth and depth of the practice of open data is surprising to many people. The learning process is best experienced. Going through the process of opening a dataset, however small, provides useful insight that can help identify where further learning is needed.
On aspect of the practice of open data involves understanding what data can be open, what can be shared and what must stay closed. Moving data along the data spectrum can unlock more value. But not all data can be open.
An open data practitioner works to make sure that data is at the right point on the data spectrum.
An open data practitioner will understand the practice of open data and be able to use those skills to create value for their organisation.
Often I find that when people write about “the state of open data” what they’re actually writing about is the practice of open data within a specific community. For example, the practice of open data in research, or the practice of open government data in the US, or the UK.
Different communities are developing their open data practices at different rates. It’s useful to compare practices so we can distil out the useful, reusable elements. But we must acknowledge that these differences exist. That open data can fulfil a different role and offer a different value proposition in different communities. However there will obviously be common elements to those practices; the common processes that we all follow.
The open data maturity model is an attempt to describe the practice of open data. The framework identifies a range of activities and processes that are relevant to the practice of open data. It’s based on years of experience across a range of different projects. And it’s been used by both public and private sector organisations.
The model is designed to help organisations assess and improve their open data practice. It provides a tool-kit to help you think about the different aspects of open data practice. By using a common framework we can benchmark our practices against those in other organisations. Not as a way to generate leader-boards, but as a way to identify opportunities for sharing our experiences to help each other develop.
If you take and find it useful, then let me know. And if you don’t find it useful, then let me know too. Hearing what works and what doesn’t is how I develop my own open data practice.