The shortcomings of data being fit for some purpose of use compared to data that is aligned with the real world is a repeating topic on this blog latest in the post “Fitness for Use” is Dead.
Today I had a reminder of that when waiting for baggage at Copenhagen Airport.
There is an information screen telling when your baggage will start rolling in. What actually seems to happen is that a fixed time is assigned to every flight and then it starts counting down the minutes. Most baggage then starts rolling in (and this is showed on the screen) before zero minutes is reached. If it, as with my flight, happens that zero minutes is reached without delivery, the information screen shows that the baggage from this flight is delayed – but not how long.
So, the information provided is when you could expect your baggage probably according to some service level goal. OK, fit for that purpose. But in fact that doesn’t help you as a passenger a lot and doesn’t help at all when that goal isn’t reached.
End of rant.
Liliendahl on Data Quality 
Very good example, Henrik.
The key question is, ‘What is the real purpose of the information being displayed?’
Is it a) to accurately tell the waiting travellers at what time their bags actually will arrive or b) to serve as an internal performance measure against a service level goal?
Once we know the real Purpose of the information we can then decide on which real world event or value to use in order to make it fit for purpose.
Real World Alignment is only of value if it makes data and information Fit for Purpose.
Regards
John
Thanks for commenting John.
The problem I often see is that things become difficult when there are multiple uses for the same data.
My thesis is that there is a breakeven point when including more and more purposes where it will be less cumbersome to reflect the real world rather than trying to align all known purposes.
I don’t know much about airport baggage handling, but my guess is that (more) sensor data from the movement of the baggage will provide both better information for the passengers and more insight into how service level goals could be defined and reached better.
Very inspiring thoughts, Henrik. The more I research on the topic of Big Data, the more I find cases of enterprises using sensor data and/or external data to directly connect the real world with the digital world.
One example in the industry that you refer too, described in a Thomas Davenport article, is PASSUR who collect information about airplanes real time and predicts a much more accurate landing time than before, resulting in both better internal performance and improved customer satisfaction.
There are many more examples where the benefit is to collect the data at its source, even if the source is not internally “quality proved” (eg : getting the customer opt in to link its BtoB profile to aa enterprise B to B customer hub is a way to improve accuracy of data, since people tend to change their profile and mail as soon they change jobs ; and it triggers business opportunity, since you can react to this change as soon as it happens).
Thanks Jean Michel for adding in. I agree.
Sensor data is the one big part of big data which in many ways makes great opportunities with real world alignment.
Social data is the other big part of big data. Getting a link between traditional master data and the new big reference data being social network profiles has in my eyes a lot of opportunities not at least in the B2B world.
Reblogged this on drndark.