Steve Bennett over at Oz Analytics has just done a couple of good posts on data quality from the perspective of how to “sell” the issue of poor data quality to the business and make them realise it’s not just a technical problem, but can also cost them money. The relevant posts are here and here.
A flipside to his approach of quantifying the cost to the business is of that for us data monkeys, we should focus our thinking when faced with a data quality problem to consider if it’s actually worth solving. It may grate for us to have crappy data in our lovely warehouse, but if the cost of solving it exceeds the benefit realised – we may sometimes just have to let it be there.*
* Of course this thought makes me feel a little dirty, and I think I need to take a shower in some nice strongly typed data with enforced referential integrity