The importance of good quality data to good quality management
In a recent article on QualityDigest.com, author David Schwinn writes about the downfall of General Motors and the key failings in the organisation which have lead to its ultimate demise. As a former GM-er himself, he has an interesting perspective on the root causes.
Ultimately, it seems to boil down to a failure to keep learning and to use the information at the disposal of management to better understand what was actually happening in the business. Schwinn shares the anecdote of his time in Ford when Japanese manufacturers were making inroads in the US auto industry because of their higher quality. Schwinn was in Ford’s Corporate Quality Office, and it was clear that Ford had access to the data they needed to figure out the quality issue. But it wasn’t being looked at because their biggest competitor (GM) wasn’t looking at it. When they eventually began to look at it they found they had to play catchup.
By that time their new competitors had effectively re-written the rules of the market and changed the basis for competition.
Schwinn shares with us a model for learning (for people and organisations):
Listening and leading are really two basic steps of learning. Learning, in a more complete way, is:
- Asking the right questions
- Gathering the expected and unexpected data
- Responding to the analysis
- Analysing the data
I’ve underlined the word “data” in this model for a simple reason. You can’t get from asking questions to responding to an analysis without having information to work from. If that information is missing, incomplete, inconsistent, full of duplicate values, or just plain inaccurate then your analysis will flawed and your response may not be correct or appropriate.
What are the questions your organisation has to answer? Do you have the information to answer those questions? How sure are you of the quality of that information? Will the quality of your information affect the quality of your management?