Data Quality Metaphors: Poor Data Quality Equals Staff Sick Days

Data Quality Metaphors

I’ve decided to kick-off a series on Data Quality Metaphors in the hope that you can use some of these messages to get your organisation motivated to tackle poor data quality.

For those who are members on Data Quality Pro (it’s free by the way) you may have read in the newsletter that I’ve been out of action for the past few days due to sickness. I’m almost fully fit again but in terms of producing content, supporting the community and generally doing “valuable stuff” then I’ve been running at about 30% output.

The point I made in the newsletter is that staff sick days are exactly the same as the waste created by poor data quality.

A Classic Example

I recently had to submit a tax return. This is normally a straightforward task but due to poor data quality management on my side I mislaid some important reference information. I wasted about half a day trying to track this down and complete the task.

So, one of the classic data quality dimensions, Accessibility, meant that I could not get my task done quickly and move on to higher value work.

Data Quality Metaphors and Storytelling

Poor data quality leads to countless Time Value Traps for everyone from the CEO to data entry staff. This waste is akin to staff sickness days and of course this is something that everyone in your organisation can relate to.

As Jim Harris on OCDQ Blog points out, metaphors are an excellent form of story telling and incredibly powerful tools in your data quality arsenal. If you can demonstrate that your staff are routinely delivering far less value than expected (as they do when poorly or absent) then it helps demonstrate in simple terms the benefits of swapping these non-value activities for more beneficial tasks.

What data quality metaphors have you used to convey the impact of poor data quality or data quality improvement?

Let’s see if we can collect a few in the comments below and perhaps start a new wiki which people can use to help grow awareness of data quality.