Hence, the problems have a nasty habit of perpetuating themselves. For sure, subordinates need to carry their weight and take responsibility. But notice how far all three of these issues, essentially the final responsibility for bringing these "unwelcome surprises" out in the open lies with management. What is the culture like in your company? My experience has been that managers may or may not be motivated to bring such issues out in the open, sometimes depending on the time horizon they consider for their own tenure.

7. Manage data for what it is: a strategic resource

Data is not merely a byproduct of business processes, but something that has value beyond its immediate processes. Finding new uses for existing data makes it more valuable, at no capital investment! Future changes to the way the data are to be used cannot be predicted, yet are guaranteed to happen! This proliferation of data usage needs to be anticipated, and calls for flexible data models. Good database design is resilient in the face of unanticipated changes. This means flexibility in hardware/infrastructure on the tangible side (avoid vendor or platform lock-in). On the intangible side, you want to avoid aggregating or any other data commitments that can not be reversed within the data scheme. It is fundamentally impossible to find a generic "right" way to aggregate inconsistencies in data. That is why flexibility calls for late commitments in the data model.

8. Higher quality data lead to far more flexibility for your corporate strategy

Fast access to accurate data not only gives a competitive advantage. What is even more important is the flexibility such companies enjoy in adjusting to changes in market conditions. So over time, as market changes will occur, the gap with the competition can grow even further. Also, changes in legislation or market regulation can be much more easily exploited and turned into an opportunity rather than 'suffered'.

9. Data quality improvement is a process, not an event

In many ways, one can draw parallels between Total Quality Management efforts, and the issues surrounding data quality. The Japanese use a word "Kaizen" that denotes both an incremental improvement method as well as a philosophy. What is crucial is that it's an on-going, never-ending effort to keep raising the bar. Data quality is never "perfect" as every new application of existing data is likely to bring up new issues. And the proliferation of data usage is not ending any time soon. So data quality issues are guaranteed to stay with us for a while.