One of the cleverest things said ever is in my eyes Parkinson ’s Law that states: “Work expands so as to fill the time available for its completion”.
There is even a variant for data that says: “Data expands to fill the space available for storage”. This is why we have big data today.
Another similar law that seems to be true is Murphy’s Law saying: “Anything that can go wrong will go wrong”. The sharper version of that is Finagle’s Law that warns: “Anything that can go wrong, will—at the worst possible moment”.
When I started working with data quality the most common trigger for data quality improvement initiatives were after a perfect storm encompassing these laws like saying: “The quality of data will decrease until everything goes wrong at the worst possible moment”.
Fortunately more and more organizations are becoming proactive about data quality these days. In doing that I recommend reversing Finagle, Murphy and Parkinson by doing this:
- Prevent data quality at the best possible moment being at data capture as told in the post instant Data Quality
- Maintain optimal data quality whenever you can as examined in the post Last Time Right
- Be more agile with data quality initiatives as reported in the post The Statue of Liberty versus The Little Mermaid.
Liliendahl on Data Quality 
Work is a gas. It expands to fill the available space. 😉
Don’t be pessimistic Henrik, please! you are the most quality sensitive and gentle man ı ever seen in my life. Think positive, don’t call bad thinks to your brain. Do you know for exp. a doctor who works about hearth attacks long time, he deads by heart attack. If you think long time one concept too much this karma finds you. God loves you in my opinion. Only same exams gives to us. Comes and goes during process maybe looks your acts. I think so.
I wish healthy happy long life to you, God bless you. Feel value of life. Every time every day is a gift.
(P.S orry my english not vert well 🙂
Old friend Ayşegül Yüksel From Turkey Library Site
π
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This is very true, Henrik! Many organizations have been reactive and only creating data quality initiatives when problems arise. However, I think there have been enough public disasters (across every vertical) to demonstrate the costly effects of dirty data. It’s good to see the shift toward a preventative approach!
Thanks Dennis, Aysegul and Kathy for joining in.