Funny how very different organisations can have very similar challenges. One of the things I wanted to find out from going to the IRM UK Enterprise Data Conference Europe is how other organisations went about agreeing data standards for their own organisation.
I got there in one: the very first talk was about exactly that topic. Becky Russell from the Environment Agency, with help from Nigel Turner of Global Data Strategy ltd, presented on their approach to the collaborative development of data standards.
The problem they addressed was the following: they had lots of separate teams working on different aspects of the same processes with many different systems. Because they worked on different aspects, they had different definitions for the same things, which made reporting on them difficult, costly and error prone. For example, one ‘thing’ or core data entity for the EA is ‘catchment area’. The team unearthed 16 definitions for those, some subtly, some very different. Some definitions had the same labels, others not so much. Sounds familiar?
The core of the solution the EA team developed is to design a process for agreeing common definitions of major data entities such ‘catchment area’. They also develop a logical data model for those entities as well, where such a thing is needed. A data model specifies what attributes or dimensions an entity has, without quite specifying how a particular system needs to store it.
That solves the reporting problem, but the process has challenges of its own. To deal with them, they stuck to a couple of principles:
- Be driven by a business problem, don’t just standardise something for the sake of it
- Be business led. They know their domain best.
- Have space for local as well as global standards. There can be good reasons for local exceptions to widely agreed definitions or data models.
- Re-use external standards where you can.
- Have supporting technologies in place.
- Align the standards development to data governance structures.
- Introduce the standard in new technology alone, because changing legacy systems is costly.
The latter was tricky, because it means that the benefits of standardisation can take quite a while to materialise. Data warehouses can help, in that respect.
This approach did work well, and allows the team to iteratively and pragmatically create more order in the data landscape of the organisation, without having to spend huge resources upfront. As the effort progresses, the standardisation process can become more formal where it needs to be.