I very nearly fell in to the same trap when I chose this example. After all, for those of us that work with data every day dirty or inaccurate address data causes numerous problems – ranging from returned mail, to the inability to collect on bad debts, to, as in the example used “being sued”. So it seems apparent that the poor address data quality is a business problem.
In fact, the business drivers are not about address quality. They are “Operational Efficiency – the cost of returned mail”, “Revenue Enhancement – the value of increaaed debt collection” and “Compliance – the saving of not beuing sued.”
Each of these projects may require that address data quality, amongst other issues, is addressed in order to meet the business goal. So good data governance would ensure that a solution to resolve address data quality should look beyond a single requirement and be reusable to address all of these needs.
Poor address data quality, in this case, is inhibiting the business from achieving three goals and the proposed solution may well be a process to improve and maintain adequate address data quality. But let’s not confuse this with the business reasons that this data quality objective is required.