Why Scrap and Rework isn’t good enough

Simon has thrown down a bit of a challenge…  can I show why Information Scrap and Rework isn’t good enough because it seems like a sensible starting point…

First off… let me provide a reference that should educate and delight (at least some of you) that explains what this Information Quality yoke is all about… THERE we go. The reference is a little old (2002) but for an update come to ICTEXPO on Friday.

Now… why isn’t Scrap and Rework good enough?

Who likes chocolate cake? Isn’t it a pain when your face gets covered in chocolate from mashing handfuls of cake into your gob? But you can wipe your face (usually in your sleeve) and carry on. That’s scrap and rework. A better solution is to wipe your face and take a smaller division of cake (a forkful). That is a change in the process based on an analysis of why you keep getting a chocolatey face, coupled with a scrap and rework task to set a baseline of cleanliness for your face that you will seek to maintain.

Simon is right – scrap and rework looks like a good place to start, and when you say “Data Quality” to most people that’s what they think, under the labels “data scrubbing”, “data cleansing” or similar. However, it doesn’t address the actual source of the poor information quality, much as wiping your face in your sleeve doesn’t stop your face getting covered in chocolate.

Therefore, once you clean your database, you will very quickly find it filling up with duff data again. Which eventually results in another round of scrap and rework to fix things again. Which then leads people to say that Information Quality management doesn’t work and costs lots of money. But scrap and rework isn’t information quality management. It is a process step to improving the quality of your information but it is just one step in many that range from culture change (from apathy to active interest) to process change to training etc.

Tom Redman is one of the co-founders of the IAIDQ. His metaphor is that databases are like lakes. No matter how many times you clean the lake, if you don’t address the sources of ‘pollution’ (root causes, cake-eating processes) then you will never achieve good quality.

To put it in professional terms that Simon (law-talking boyo that he is) might understand, scrap and rework is like apologising and offering some compensation everytime you punch a complete stranger in the face. A far better solution is to examine why it is you punch strangers in the face and stop doing it. Your apologies and offers of money to the injured fix the historical damage but do not prevent future occurences. And I doubt Simon would counsel any of his firm’s clients to continue punching strangers in the face.

Scrap and rework is costly. Scrap and rework on a repetitive institutionalised basis is futile, creating a sense of doing something about your Information quality without actually getting anywhere but burning a pile of cash to stand still. It is an important step in any information quality management programme. However, understanding your data capture processes and the root causes of your poor quality data and then acting to improve those processes to address those root causes are the components that contribute to a sustained improvement in quality.

Scrap and rework solves the problems of today at a short-term economic cost. However, it serves to bury the problems of tomorrow unless it takes place in tandem with process improvement to address root cause and the development of a ‘Quality culture’.

To tie this back to the Electoral Register, to rely on scrap and rework would mean that we would get a clean register this time around at a point in time. However, over time the register would degrade in quality again, in the same way as your face gets dirty again if you don’t change the way you eat your cake.

Now put that chocolate cake down and get a fork!