There is an old philosophical question: “If a tree fell in the forest but no one heard it, did it make a noise?” The basis of the question being that every time we’ve seen a tree fall in the past it has made a noise, but if no one heard it fall then maybe this one time it didn’t … but you couldn’t prove it either way.
Centrally important to certain areas of Data Management such as Data Governance, Master Data Management, and especially Data Quality is the absolute importance of metrics and measures. You can’t demonstrate that the quality of data improved unless you measure it. You can’t report the benefit of your program unless you measure it. And, showing improvement means that you need to measure both before and after to calculate the improvement.
Senior executives in organizations want to know what value a technology investment brings them. And the ways to show value are increased revenue, lowered cost, and reduced risk (which can include regulatory compliance). Without reporting financial benefit to management few organizations are willing to support ongoing improvement projects for multiple years. Also, it is important to report both what the financial benefit has been and what additional opportunities remain – management is very happy to declare success and terminate the program unless you are also reporting what remains to be done.
thoughtsondata 
April,
True you have to measure, but measurement is an entire discipline on it’s own. I’ve always been suspicious of improvement metrics like revenue, profit, etc., because no technology implementation can drive those things on their own there has to be a contribution from stakeholders as well, all of whom take credit for 100% of the gain (and no credit fror failure of course).
Measurement is fraught with risk as it can cause distortion and dysfunction if not modeled completely. Remember the Mars probe that crashed onto the planet after a screw up of inches and centimeters? I know this is the problem t hat MDM is supposed to address, but it’s a self-referential once when needling with measuring the thing you’re implementing.
-NR
Measurement is certainly a slippery slope. Even for sub-atomic particles the sheer act of observing may change the behavior of the observed. But how can we ever report that the quality of the data has improved if we are not measuring the quality of the data (whatever that means)?
Thanks for reading the blog post and taking the time to comment!
Happy Holidays! I hope to see you soon.
Boy do I hate hate that autocorrect feature. “once when needling?” I don’t even remember what I meant to type.
I’m not suggesting you don’t measure. Just measure well. Fifteen years ago I told a TDWI audience that an ROI calc for a data warehouse was pointless because realized benefits were a function o the implementers as well as the implements. Operational systems can hove a ROI, but informational systems, not so much. You can measure low level data quality improvements, but how do extrapolate that a financial benefit? I don’t know.
Yes it’s been a while. I didn’t submit anything or DAMA orIRM UK this year. Getting lazy I guess.
I am never going to type on an iPad again. I meant “implementers as well as the implementees.”