August brings into sharp focus and a furious boil everything I've been listening to in the late spring and summer--Henry Rollins
This summer, faced with my youngest spending 3 weeks participating in an academic program at University of Chicago, Steve (husband) and I decided to take advantage of a long weekend in the lovely blue ridge mountains.
The pace of summers vacillate between frenetic and this new normal--kids with new found independence and opportunities beyond the familiar. Fewer ferry rides to the beach and more sojourns with friends--pausing only to grab a set of keys and hoping for a little cash (just in case).
I have observed an increased efficiency in my work as I evolved from a hired gun to simply write what I was asked to now being responsible for the data that informs the narrative.
Don't get me wrong. I always did my due diligence with research and probing evidence-based medical articles but let me be frank. I only looked where the client was pointing.
Maya Angelou is attributed to saying something like, "When you know better, you do better." I do a fair share of pro bono work teaching the basics of survey design and how to clean data upstream from the fancy dashboard visualizations that everyone is clamoring for. And I spend a non-trivial amount of time learning how to think about analysis.
When you have been working in a field like medical writing or healthcare consulting you realize the secret sauce is scalability. Do you want to be a data mechanic constantly repairing and fixing poorly designed questions or do you crave higher level collaborations? I have noticed many colleagues falling in with the status quo and giving their clients what they have asked for. Poorly designed or articulated outcomes questions from multiple choice surveys or a data question only interested in probing or interrogating a single source of data.
You can dance around to the music provided to you like a little monkey--or become curious. Is there a better way? Am I measuring what I think I am measuring or just grabbing low hanging bananas?
Although my toolbox has expanded over the years to include Python, SQL, and R--I use them with intense focus and deliberation. When you evolve skills into exploring large data sets you innately comprehend why certain types of questions are higher value than others.
For example, I sat through a Tableau session on how to analyze Likert data--data gathered from those ubiquitous uni- (one attribute) or multi (contrasting opposites)-polar scales. You are asked how likely you might be to exhibit a certain behavior.
No. No we can't. But if we use probabilities like the ones we can generate from asking respondents to rank responses--we now know how they prioritized their behavior or sentiment.
And ranking or rating questions can be more straightforward to analyze.
Do you have questions regarding cleaning data and what tools are available?
Join us on August 14th for a Healthcare Tableau User Group meeting. You can register below...