Data Governance / Continuing Data Element Dialog

[HerschelC]

It is great that the government is seeking broad collaboration and discussion through Github as part of establishing data standards. However, establishing an initial standard, generally through paper and whiteboard exercises, is just a starting point. The real work and tough decisions come during implementation as "real" data is integrated and organizations work to these standards. While the two weeks of discussion time per element is great, other ideas will come up after the feedback period closes. (Or in my case, a comment to which I'd like to respond but couldn't.)

What is the plan for fostering a continuing dialog for future iterations of data standards and definitions that will accommodate the reality on the ground?

Broadly speaking, I'm asking about a government-wide data governance body for these elements. But without getting that grandiose, we do need a way to continue the conversation and a platform for everyone implementing to discuss data challenges so all can learn (beyond just federal government and system integrators - state/local implementations can learn too for when it is their turn).

I hope this conversation will continue.

[kaitlin]

Thanks @HerschelC. I agree, and right now I think the governance structure going forward is very TBD. It is one thing to create a standard, but another to implement it with real data. I created a new label for governance, just so we can keep track of these threads and ideas in the interim.

[CharlesHoffmanCPA]

I agree with @HerschelC with respect to governance, and I would add that it is just as important to define the relations between terms as it is to define terms. While computers are great tools, if they are not employed correctly, the result will be nonsense. To exchange information correctly, these four obstacles need to be overcome:

1. Business professional idiosyncrasies: The first obstacle is that different business professionals use different terminologies to refer to exactly the same thing.
2. Information technology idiosyncrasies: The second obstacle is that information technology professionals use different technology options , techniques , and formats to encode information and store exactly the same information.
3. Inconsistent domain understanding of and technology's limitations in expressing interconnections: A third obstacle is that information is not just a long list of facts, but rather these facts are logically interconnected and generally used within sets which can be dynamic and used one way by one business professional and some other way by another business professional or by the same business professional at some different point in time. These relations are many times more detailed and complex than the typical computer database can handle. Business professionals sometimes do not understand that certain relations exist.
4. Computers are dumb beasts: The forth obstacle is that computers don't understand themselves, the programs they run, or the information that they work with. Computers are dumb beasts. What computers do can sometimes seem magical. But in reality, computers are only as smart as the metadata they are given to work with, the programs that humans create, and the data that exists in databases that the computers work with.

Collaboration and coordination are critical in order to establish the proper control mechanisms. So while governance is essential, so is skillful execution.

[CharlesHoffmanCPA]

Elaborating on @HerschelC 's point about governance:

This paper explains in detail the taxonomy/ontology lifecycle:

http://www.researchgate.net/publication/260834360_Toward_Ontology_Evaluation_across_the_lifecycle

The executive summary of that paper states the following "Problem: Currently, there is no agreed on methodology for development of ontologies, and there is no consensus on how ontologies should be evaluated. Consequently, evaluation techniques and tools are not widely utilized in the development of ontologies. This can lead to ontologies of poor quality and is an obstacle to the successful deployment of ontologies as a technology.

There is a very good diagram where the stages of the lifecycle are detailed. This is a summary:

System design
Ontology design
Ontological analysis
Requirements definition
Operations/maintenance
Deployment
System development and integration
Ontology development and reuse