The Data Scientist Will See You Now: 3 Steps to a Useful Analytics Checkup
Frustrated, the doctor then turns to the patient’s behavioral history. It doesn’t bode well: heavy smoker, no exercise, poor diet. The doctor then moves in to start checking vitals, but the patient refuses. With nothing concrete to diagnose or treat, the doctor makes her best guess about the underlying problems and issues the only suggestion she can. “Stop smoking, go for a walk, eat a few green things every day, and come back when you can tell me what’s wrong.”
Visibly irritated, the patient stands up abruptly, mumbles “Some doctor you are…”, and swiftly exits.
Our own experiences help us immediately see the impossibility of the doctor’s situation. Without anything specific to cure and no way to get additional information, she can only provide a few basic but still critical pointers and hope for the best.
Unfortunately, when it comes to data science and analytics, the real world sometimes looks more like our hypothetical patient then our goal-driven selves. Data scientists need to be truly “patient focused” to maximally leverage their skills in the context of business problems, but those seeking analytical insights would also do well to follow a few basic steps to lay the foundation for a helpful visit with the data scientist.
Step 1: Remember “Garbage in, garbage out”
This is old adage from computer science tells us that the quality of the input determines the quality of the output; the explosion of data and analytics makes it more applicable by the minute. If you want to live long and prosper, eat well and exercise. If you want to make better data-driven decisions, it pays to put in the basic processes that insure you have the timely, consistent, and accurate data you need before attempting to analyze anything.
Step 2: Clearly Define Your Business Problem
Just as our patient couldn’t identify his core ailment, those seeking analytics insights sometimes struggle to identify the specific problem they are trying to solve. Reading an article in the Wall Street Journal about the power of predictive analytics is one thing, but figuring out where and how one can actually leverage trustworthy analytics in the real world with real organizational constraints is another. A good data scientist should help you achieve greater collective focus and will likely highlight new opportunities not yet realized; a bad one hands you a model or some software you can’t ultimately use and declares victory.
Step 3: Ask “What will I do differently as a result of this information?”
The action can be as big as a massive segment reorganization or as small as a 20 minute follow-up meeting with five sales reps in the Charlotte office. The point is that SOMETHING meaningfully and usefully different needs to emerge, whether that “thing” is a major business decision or a subtle shift in perspective. Of course, to be cost effective, the amount of the data scientists’ work should not dwarf the downstream efforts of the business. Sometimes though, big business things can come from small analytics packages.
Like this post?
Get our FREE Turnover Mini Course!
You’ll get 5 insight-rich daily lessons delivered right to your inbox.
In this series you’ll discover:
- How to calculate this critical HR metric
- How turnover can actually be a GOOD thing for your organization
- How to develop your own LEADING INDICATORS
- Other insightful workforce metrics to use today
There’s a bunch more too. All free. All digestible. Right to your inbox.
Yes! Sign Me Up!
Comments or Questions?
Add your comments OR just send me an email: firstname.lastname@example.org
I would be happy to answer them!
photo credit: <a href=”http://www.flickr.com/photos/8623220@N02/2179909780″>Dr. Schreiber of San Augustine giving a typhoid innoculation at a rural school, San Augustine County, Texas (LOC)</a> via <a href=”http://photopin.com”>photopin</a> <a href=”https://www.flickr.com/commons/usage/”>(license)</a>
- © 2022 HR Analytics 101