The Yin-Yang of Understanding Data

There are several issues with data. One is that it’s viewed with suspicion. Conversely, it is also held to sacrosanct integrity. I’d almost refer to this as the yin-yang of data understanding.

When I come to findings or conclusions with healthcare data, people often refuse to give the data due credence. Largely this stems from political roots or an ingrained sense of self-knowledge (wherein the data assessor believes their own anecdotal evidence over the data itself). This is the yin portion of data perception.

Within the other schema of understanding data is an over reliance on the data analysis to validate or make decisions. I had a supervisor who was a subsidiary information officer. One of his favorite quotes was ‘what gets measured, gets managed’. I’m sure this actually comes from some corner of the business world, but don’t know the particular source of origin.

Another rather bright fellow always made sure to explain these concepts with a caveat: if you mis-measure, you’ll mismanage. For example, in hospitals a key metric is room utilization and efficiency. Not all departments or surgeons are as efficient, and finding a key performance level for their work was crucial to retaining top physicians and ensuring their compensation was fair. Laying down a blanket 50% metric would have been grossly unfair to a vast majority of doctors, while still eliciting protest from the bottom two quartiles. Clearly, there needs to be a better way to manage efficiency and performance at all levels. One key complaint I’ve heard is that companies lose their crucial employees by not realizing what they contributed – another classic example of mis-measurement. The work wasn’t accounted for, but still was being done.

In the these latter cases, the classic decision-making model was supplemented by data, but the distinct possibility of faulty or misguided data analysis made wrong decisions not just likely, but almost certain. The human element of error was compounded by the data.

 

Advertisements
This entry was posted in Careers And Work, Data Science, Information Technology, Resource-a-rama and tagged , , , , , , , . Bookmark the permalink.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s