Six Sigma
If you talk to Japanese manufacturing companies (say Toyota or Toshiba) and you talk data quality, then soon you'll talk 'Six Sigma'. Six sigma is a term derived from the statistical term describing the standard deviation (sigma) and therefore describes the likelyhood to make an error. If you reach Six Sigma, you reach a quality where you hardly make any mistakes. This is particularly important in manufacturing.
When talking Six Sigma I think it is mostly striking to see that the quality process is everywhere; it is engrained in the organisation -from workers up to sr managers - quality is everywhere. And that is - I think - the main message of Six Sigma: You can only reach high levels of quality (and therefore also information quality) if this is engrained in everything we do.
Some vendors of data quality tools start to use the term Six Sigma in their toolkit and that makes me sceptical, since it is not the tool that does the job, but the complete culture change. So if you see managers buying a Six Sigma tool and not do anything else - than don't trust them!
Labels: culture change, quality
Making Controls Work
Data architects need to balance constantly between what we want in terms of easy to use functionality and building in some level of control. Users do not want to be bothered with storing data in the right place, or adding all sorts of meta data, but data managers know that without this it is not easy to re-use the data elsewhere, or later during the life cycle of the data.
So one of the holy grails for data architects is to find that right balance between easy functionality and the controls. My view is that we need to focus with controls on the 'must-have' and further see how much we can already populate through some smart algorithms. At the end of the day we all know that if things are automated they will happen (like the a search index), and when you rely on the user it is more likely that meta data stays incomplete and inconsistent, unless there is a lot of control. The right balance is even easier to find if some of the user interaction can be supported by work done by automated processes - e.g. if the meta data can be pre-populated, and users only need to click OK (and take out the glaring mistakes), then this process may work.
Another option is to make the data quality more visible; more transparent retrieval mechanisms (like e.g. Spotfire or BusinessObjects) are great enablers for this. Suddenly users see that data is missing and may even grasp the effect of this.
Labels: behaviour, quality, retrieval
