Seth Godin doesn't care about SEO savvy headlines and you shouldn't either. Many of my journalist colleagues disagree and I get it. We are only as clickable as our cleverest headline written to bring more and more eyeballs to the table. I am no Seth Godin but I also prefer headlines that mean something to me not the Google algorithms that bring short term interest. A stray piece of conversation or a snippet from a podcast that catches my attention will also do just nicely.
Look down the list at the last few posts--Go Fund Yourself, A Tabula Rasa in Data Literacy, and Generic but Ambitious--what the hell am I talking about? I don't know. Maybe grab a coffee or tea and take a look. Seth teaches us not to be the thing for everyone--just some people.
If you prefer provocative headlines, I didn't make this for you.
Even if you don't think of yourself as a marketer--you are a marketer. Maybe you are the product or maybe your currency is knowledge or expertise but make no mistake we are all selling something.
Seth teaches us how to be effective. If you are indeed bringing value to the table you need to find your tribe, stand in front and say--here. I made this for you. This particular blog captured my attention. Not because I need something shiny to look at like a buzz worthy headline but because I trust I will learn something.
And when does it get boring? is well worth the quick read. I think Seth and I share short attention spans. In fact, I agree with his method--read along until you get the joke. So why not keep it succinct and to the point?
Almost no one who takes an intro to economics course becomes an economist. One reason might be that within a few days of starting the class, it becomes abstract, formula-based and dull.--Seth Godin
I think I surprise many when they find out I am a data analyst. I have always had a wide scope of general interests and this serves me well. Believe it or not, there aren't that many of us that can work the entire end to end strategic data process. We need to know the language, the challenges, and the opportunities. Not everyone needs to be niche driven or highly specific. If you are reading this as one of my data friends or colleagues, here is the perfect podcast for you, Should I Become More Technical or Business Focused in Data Science Career.
I began this post in response to a question I field pretty regularly. How much statistics do I need to become a data scientist/analyst/professional? I can't answer that question. Textbooks cram information into your head for an exam. She/he/they who passes the most exams wins and can be perhaps a statistician, mathematician, computer scientist. You need to ask yourself what the long road looks like for you. Where do you want to work? What do you want to be doing?
I have completed numerous classes in statistics but I am not a statistician. Weirdly I am also a member of the American Statistical Association. I need to be. I need to keep learning and applying rigor and comprehension to technical topics.
Once you get out in the real world and discover your unique flair and contribution, the trick is to remain curious. To use the tools that give "us a chance to understand and to figure things out."
Because a testing regime is in place, particularly now when so many other tropes in the education-industrial complex are disrupted, the textbook authors and administrators work together to skip the ‘fluff’ and go straight to the stuff that’s easy to test.--Seth Godin
For example, when I think about data modeling and trying figure out the shape of my data, I think about linear, sinusoidal, or quadratic equations. These questions jump from the pages of math books into practical applications when you have variables to consider and relationships to determine.
Am I the only one considering positive first derivatives when looking at the COVID-19 curves? All the curves were getting bigger but we needed to consider the rates. They can stay the same, increase slowly, or represent what actually was occurring--the rate was increasing quite rapidly. All applications of the math concepts we were forced to memorize to a test in the absence of how they are applied in the real world.
When my boys were small occasionally one or both of them would complain about being bored. My response rarely wavered. I would tell them, "That doesn't sound like being bored, that sounds like a lack of imagination."
And off we would go to have an adventure.
Speaking of adventures...
May 15th we are having a free lunch and learn about demographic data. Register at link Getting comfortable with demographic data.
In my mind, I always imagined a resource like this one, EDITED, for healthcare analytics. You don't have to squint too hard to imagine a stack of patient files instead of a rack of clothing in the image above. The biggest data challenge might be that healthcare isn't just one thing right? We have to rely on latent factor models to better represent the complexity of what we mean when measuring any metrics related to healthcare.
A blouse is a blouse, and I suppose retail represents a commodity industry while healthcare is quite something else. Let's see if there aren't any insights we can model or at least pay attention and appreciate.
Think of a data point included in a visualization not unlike the "tragic sweater" described by Meryl Streep in the Devil Wears Prada. There are downstream decisions made based on data purity and quality although perhaps not in the same way as a cerulean blue pigment but you get my drift...
We need to do better with accessing the right data. I have been there. You locate a great data source that seems to have the variables you need to say something meaningful about a specific topic or insight. But there is one more step needed before the self congratulations and fist bumping.
Are you confirming a thought or a belief? I was directed to some work by Byron Katie about cognitive restructuring and naturally, I thought of data insights.
It goes something like this...are these insights true? Can you absolutely know they are true? Is there data out there that might challenge this reaction?
A thought is harmless until we believe it. It's not our thoughts, but our attachment to our thoughts, that causes suffering.--Byron Katie
The lovely Mona Chalabi hand draws all of her charts and graphics to remind us of the human element.
Our biases and frailties are all evident in the data questions we formulate, the sources we access, and even the insights we generate.
We need to look at the broader context of the low-hanging fruit and see what lurks beyond. Imagine if there was a "data" repository similar to the broad market snapshots of retail clothing.
Maybe monthly trend reports are needed to move the conversation in healthcare toward a more robust understanding of the open source data.
I am definitely thinking along those lines as I reach a broader audience interested in wrapping a story around their data. Maybe the devil actually wears...data.
I will try and share some of the free discussions as I travel around the country talking about data literacy in healthcare specifically.
You can register for a free webinar on March 29th at 1:00 PM EST here...The 5 second rule and your data: how dirty is it?
Browse the archive...
Thank you for making a donution!
In a world of "evidence-based" medicine I am a bigger fan of practice-based evidence.
Remember the quote by Upton Sinclair...
“It is difficult to get a man to understand something, when his salary depends upon his not understanding it!”
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