This is absolutely true and frequently overlooked – particularly when dealing with data that is amalgamated from multiple sources such as a data migration (http://dataqualitymatters.wordpress.com/2011/09/23/data-migrations-good-opportunity-to-improve-data-quality/) or a mdm project.

My post about data migrations and data quality received a lot of comment defending the position that data quality is irrelevant as it will impact on the budget and time lines.

the point is that it is necessary to understand how data will be reused and repurposed when planning a migration – and to plan and adapt at an early stage will in the long run save time and money

have a great weekend
Gary

There are in my eyes two things that will make new data quality movement necessary:
• Business objectives changes over time which will affect data quality benchmark and prioritizing.
• Technology evolves which will make thing previously not possible and option in the future. Sticking to old standards will eventually put you out of business.

(On the latter note if it were today I guess Nelson wouldn’t even have made it across the North Sea with his ships of the line before being driftwood).

I actually disagree with the problem of creating “a job for life”. Data quality *is* a job for life, just because it transitions into a BAU process does not mean that that focus should terminate. The baton passes from out-and-out DQ practitioners to BAU staff but the journey continues.

This is one of the key excuses for poor data quality as discussed in my post http://dataqualitymatters.wordpress.com/2011/08/02/top-5-excuses-for-bad-data/.

the same principle – start small and prove value is just as critical for data governance initiatives. It is highly optimistic to expect business to fork out budget for data governance without seeing any kind of return.