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1. Apart from 'basic' principles, metadata principles are quite difficult. To get a good understanding of and this makes communication of them even harder. As it is extremely important to have organisational buy-in, the communication of the organisation metadata principles and associated model is something that needs some strong consideration.

2. Every-one has a view on what metadata they need - the list of metadata requirements / elements can be endless. Given the breadth of metadata - an incremental approach to the delivery of storage facilities is fundamental.

3. Establish a metadata framework upon which discussions can be based that best fits your organisation - we have agreed on MetaNet, supplemented with SDMX. As Statisticians we love frameworks so having one makes life a lot easier. You could argue that the framework is irrelevant but its the common language you aim to use.

4. There is a need to consider the audience of the metadata. The table about users covers some of this, but there is also the model where some basic metadata is supplied (e.g. Dublin Core) that will meet one need but this will then be further extended to satisfy another need and then extended even further to meet another need.

5. To make data re-use a reality there is a need to go back to 1st principles, i.e. what is the concept behind the data item. Surprisingly it might be difficult for some subject matter areas to identify these 1st principles easily, particularly if the collection has been in existence for some time.

6. Some metadata is better than no metadata - as long as it is of good quality. Our experience around classifications is that there are non-standard classifications used and providing a centralised environment to support these is much better than having an 'black market' running counter to the organisational approach. Once you have the centralised environment with standard & non-standard metadata you are in a much better position to clean-up the non-standard material.

7. Without significant governance it is very easy to start with a generic service concept and yet still deliver a silo solution. The ongoing upgrade of all generic services is needed to avoid this.

8. Expecting delivery of generic services from input / output specific projects leads to significant tensions, particularly in relation to added scope elements within fixed resource schedules. Delivery of business services at the same time as developing and delivering the underlying architecture services adds significant complexity to implementation. The approach with the development of the core infrastructure components within the special project was selected to overcome this problem.

9. The adoption and implementation of SOA as a Statistical Information Architecture requires a significant mind shift from data processing to enabling enterprise business processes through the delivery of enterprise services.

10. Skilled resources, familiar with SOA concepts and application are very difficult to recruit, and equally difficult to grow.

11. The move from 'silo systems' to a BmTS type model is a major challenge that should not be under-estimated.

12. Having an active Standards Governance Committee, made up of senior representatives from across the organisation (ours has the 3 DGSs on it), is a very useful thing to have in place. This forum provides an environment which standards can be discussed & agreed and the Committee can take on the role of the 'authority to answer to' if need be.

13. Well defined relationship between data and metadata is very important, the approach with direct connection between data element defined as statistical fact and metadata dimensions proved to be successful because we were able to test and utilize the concept before the (costly) development of metadata management systems.

14. Be prepared for survey-specific requirements: the BPM exercise is absolutely needed to define the common processes and identify potentially required survey-specific features.

15. Do not expect to get it 100% right the very first time.

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