Fractal - Kaizen Methodology- Part 1

So, What is Kaizen? Kaizen is a japanese concept which literally means change to become "zen".

Zen is the absolute state where no further improvement is possible and kaizen philosophy is all about making incremental changes to achieve that perfection.

While some of us might not be too keen to become a zen, I am sure we all would love to have better processes in our work place than what exists and find a way that isnt too tedious and actually works. The answer.. could be Kaizen. In contrast to Business Process Re-engineering model (the western approach usually), Kaizen prophesises change from the bottom up. Like everything else, I am sure there is a middle ground. Start with a firm vision, objective and business model and from there, go with incremental, constant and continuous changes!

Look at the following rather simplistic representation of a Hybrid Kaizen Model. The Initial directive follows the Business Process (Re)engineering methodology and Once the Initial A&R is complete Kaizen takes over from there.

So what are the Improvement cycles mentioned? We will see in the next part!

Data Quality Assurance

No doubt we all desire quality data to work with, particularly when making strategic decisions. But how to go about assuring the data is indeed fit for consumption may be a non trivial pursuit in itself.

First, as with most corporate initiatives is the all important budget consideration. Assuming there is a rather compelling business case for it, i.e. we have executive buy-in, the question of who's going to do it needs to be answered - can this be accomplished internally or must we also rely on outside consultants with their broader experience base?

Next comes the daunting task of establishing scope - should we cover all corporate data or only pieces deemed mission critical? Even when starting small makes sense, there remains the decision of where to address data quality - downstream in target applications, within upstream data sources themselves or in a mix of the two?

Despite the challenges there is an approach to improving data quality that is both practical and frugal, viz., risk-based resolution. This prudent approach recognizes that some data quality problems are more probable than others and deploy limited resources to solve high risk ones instead of trying to mitigate each and every data issue.

Such a risk-based approach works because it focuses effort on where it makes the biggest difference - is there anything more practical than confronting problems likely to cause the most significant losses? - and by doing so, achieve a cost-effectiveness that is more palatable to the finance folks - hey, IT can be frugal after all!