First, lets look at the issue of “Same 100”. It is easy to state – we always go to the same 100 people to solve our problems. Actually we probably go to the same 5-7 people to solve our problems, whether they are personal or professional. There’s nothing wrong with that, it is what we evolved to do. However the current size and scope of the problems we as organizations are trying to solve demands going beyond the Same 5, or the Same 100. We have to figure out how to build “Same 10,000” organizations.
Your next stop on this journey is an amazing essay on Same 100, Next Problem thinking by one of my favorite people, Ralph Welborn. Jump to the middle of that essay, and you’ll find Ralph talking about codified knowledge. Codified knowledge is really what a data standard is – the collective wisdom of a group on how best to represent data for its transmission, sharing and archiving.
The Same 100 problem with data standards is the limits imposed by always going to the Same 100 people within the standards organization. In the XBRL world, we’ve experienced this time and again, especially in the slow pace of standards development due to always going to the same overworked volunteers in multiple working groups.
Social media seem like the obvious answer to this problem. After all, we each have way more friends, likes, followers, etc. than we could keep track of ourselves. The system is amplifying our ability to maintain a network far larger than a Rolodex.
I would love to say that the problem will be solved by the magic words, “Fork me on GitHub!” It won’t, not until we figure out how to build standards the way we know how to build software.
Forking a standard will let people work on the problems that concern them. Great, but meta-level issues are going to arise when you start to merge forks into the main “distribution”. Some of the same issues also show up in software, standards development will have to learn and expand upon those solutions.
If it works, Same 10,000 standards development will release a backlog of in-development standards as a first wave of change. The next and ongoing influence will be continued innovation in standards – standards that track changes in the universe of discourse in a more timely, flexible way.
Currently, standards get stuck at a stage of development characterized by success in the marketplace and/or exhaustion of those interested. The world moves on, friction and tension begins to build as a result of the disconnect between map and territory.
Personally, I see a problem with even Same 10,000 thinking. It is the Yahoo vs. Google, John Henry vs. the machine problem. Relying on people to discover on their own the problems they can contribute to, and the problems that need their help, will be too slow. The network itself has to become an active participant in assisting in tracking the slowly changing discourse, the new metadata, and bring these changes to the attention of those interested in problems. At best, the network should be able to recommend problems to solve to individuals, just as easily as it recommends friends and music today.