Friday, September 15, 2023

Data: Rule #1

The first rule of data:
  • Don't ask for data if you don't know what you are going to do with it
Or, said another way (same rule)
  • Don't ask for data which you can not use or act upon
 And, your reaction might be: Of course!

But, alas, in too many PMOs there are too many incidents of reports, data accumulation, measurements, etc which are the progeny of PMO doctrine. But the reality is: There actually is no plan for what to do with all this stuff that comes in. 

Sometimes, a data collection is just curiosity; sometimes it's just blind compliance with a data regulation; sometimes it's just to have a justification for an analyst job. 

But sometimes, there is a "feeling" that if such data is not coming in and available that somehow you're failing as a manager. Afterall, one view of management is to measure, evaluate, and act. If you're not doing the first step, how can you be managing effectively? Ergo: measure everything! Somehow, the good 'stuff' will then rise to the top. (I submit "hope" and "somehow" are not actually good planning tools)

The test:
 If someone says they need data, the first questions are: 
  • What are you going to do with the data?
  • How does the data add value to what is to be done
  • Is the data quality consistent with the intended use or application, and 
  • Is there a plan to effectuate that value-add (in other words, can you put the data into action)?
And how much data?
Does the data inquisitor have a notion of data limits: What is enough, but not too much, to be statistically significant (*), informative for management decision making, and sufficient to establish control limits?

And information?
Well, the usual definition is that information is data, perhaps multiple data, integrated with context, and interpreted for the current situation.

So, the rule can be extended: If there are not means to process data into information, is the data necessary to be collected?

Bottom line: To state the obvious: always test a request for data collection for value-add before spending resources!

(*) Statistical significance: The observed behavior in the data is unlikely to be just a random outcome; the data is predictability attributed to something specific which can be described statistically.

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