Nobody likes bad data, and presumably we're all working tirelessly to minimize it. After all, bad data has a price.

Some obvious "costs" of bad data include poor deliverability of email/snail mail, inability to target market/message, inability to analyze data effectively, and potential reputational embarrassment.

But there are also many indirect costs for bad data. One example of this is querying and report-writing. When data is bad, writing queries or reports that are accurate and usable can become very time-consuming, as staff (or a paid contractor) has to make extra efforts to filter out bad data. Every time a report has to be refined to address bad data is an increase of direct or indirect costs to the organization.

Another example is loss of trust in the data itself, by staff and, potentially, by the board and members. The more bad data that consumers of the data see, the less likely they are to trust data they see in the future. And of course, this can lead to the cycle of doom.

The cost of bad data is high, especially when you consider indirect costs. Are you doing everything you can to keep your data clean?

(Hat tip to Mike Frye for this idea.)