The Yin-Yang of Understanding Data
About the role
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.
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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.
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Simplicity in Complexity: Binary Code and Yin, Yang (soulfields.wordpress.com)
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About Vellum Information
Vellum Information is a research and development platform focused around information visualization, web and data mining and analysis, healthcare and global health technology and data utilization.
We have a firm foot in open-source technology and public datasets, and believe that the best solutions are found in the root. It is our job to unearth these depths and make your answers accessible.
Computer science is really an art. They do not teach that in school, but the truth of it is that computers have a common language, and there are infinite dialects of it spoken around the world.
Current projects include:
Heath information technology and public health matters (and how the two meet).
Information visualization and beautiful data. This is a mantra for 2011: Make it simple, make it beautiful. Data can and should be straighforward and appealing. The era of static tables and charts is past.
Database development, web and application deployment and design - expert system design, case-based reasoning systems, knowledge management, and AI applications.
A keen interest in the Cloud and how it can be utilized to deploy an application across endless instances, which ties firmly into the rest of our projects.
Data/web mining and analysis, text mining, and content analysis. Semantic content and ontology creation.
Publishing several reports, as well as working on some academic presentations and publications. Look for us on Amazon and Google Books.
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The Yin-Yang of Understanding Data
About the role
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
About Vellum Information
Vellum Information is a research and development platform focused around information visualization, web and data mining and analysis, healthcare and global health technology and data utilization.
We have a firm foot in open-source technology and public datasets, and believe that the best solutions are found in the root. It is our job to unearth these depths and make your answers accessible.
Computer science is really an art. They do not teach that in school, but the truth of it is that computers have a common language, and there are infinite dialects of it spoken around the world.
Current projects include:
Heath information technology and public health matters (and how the two meet).
Information visualization and beautiful data. This is a mantra for 2011: Make it simple, make it beautiful. Data can and should be straighforward and appealing. The era of static tables and charts is past.
Database development, web and application deployment and design - expert system design, case-based reasoning systems, knowledge management, and AI applications.
A keen interest in the Cloud and how it can be utilized to deploy an application across endless instances, which ties firmly into the rest of our projects.
Data/web mining and analysis, text mining, and content analysis. Semantic content and ontology creation.
Publishing several reports, as well as working on some academic presentations and publications. Look for us on Amazon and Google Books.