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. Advertisement 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. Related articles Simplicity in Complexity: Binary Code and Yin, Yang (soulfields.wordpress.com) Advertisement Share on X (Opens in new window) X Share on LinkedIn (Opens in new window) LinkedIn Share on Facebook (Opens in new window) Facebook Email a link to a friend (Opens in new window) Email Print (Opens in new window) Print Share on Reddit (Opens in new window) Reddit Share on Tumblr (Opens in new window) Tumblr Like Loading... Related

The role involves analyzing healthcare data to draw conclusions while addressing issues of data perception and integrity. It requires balancing the reliance on data analysis with the understanding of human factors that can lead to misinterpretation.

Candidates should have experience in data analysis, particularly in healthcare settings, and an understanding of performance management metrics. The ability to navigate the complexities of data perception and decision-making is essential.

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• Data Analysis
• Healthcare Data
• Performance Management
• Decision Making
• Efficiency Metrics
• Data Integrity
• Data Assessment
• Human Element
• Mis-measurement
• Political Roots
• Anecdotal Evidence
• Key Performance Indicators
• Compensation Fairness
• Data Validation
• Management
• Data Perception

Expérience minimale

2+ ans

Postuler avant le

12 août 2026