“Do you fall through out the day?”
“How many times?
“Depends, anywhere from 10 to 15 times a day.”
It was then I noticed the receptionist in radiology looked absolutely horrified. Which made me nervous, so I mumbled out, “Why do you ask?”
“We’re trying to determine if you need assistance walking to the X-ray.”
“Oh, I am working on handstands, depending on how much I practice, I fall.”
At that point everyone, patients and the desk staff all laughed.
Data: We use data to understand occurrences in the past and to predict behavior in the future. Let’s talk about data. The scientific method is to form a hypothesis and then prove or disprove it with data. How can we use data achieve to smart decisions and avoid bad ones? So let’s talk data.
I lead a study a couple of years ago about technology in the workplace. One hypothesis was, people who work from home got promoted less than those who worked in an office. The subteam working on this developed a list of reasons to support this. The at home worker is less visible; it is harder to network and be recognized by peers and higher ups; and there is a lack of mentoring that would normally occur in the office. The team was excited to be able to present this and started developing recommendation to change this. They strongly believed the at home worker was promoted less. Just one thing, the data did not support the hypothesis. There was no disparity between promotions based on the ability to telecommute or to be in an office. That should have been enough to end it, but we had gone from scientific study to belief. The subteam leader did not want to accept the fact, and at this point it was fact, that there was no difference between home versus office. Another two weeks and additional data continued to show the same results. This was the interesting thing. The belief so strong, the data was not accepted. This is a challenge in business. The data shows a trend, fact, etc, but there is still the difficulty to make those in power believe; to cause them to act.
As you collect and present data, think of two things. What action you want and what type of data or additional data you may need to get that action? Understand your context for data gathering, think about the whole picture and what other information you may need.
Recently, I received a report with recommendations. However, there was no data behind the recommendations which caused me to press the team. The aftermath was not pretty. While the recommendations sounded good, to implement them would have been costly AND ineffective. For example, there was a recommendation about assigning mentors to improve success rates. Sounds reasonable? For the entire population in question, mentors impaired performance according to the data. Data can stop us from making costly errors or implementing solutions that do not yield a desired effect. The team determined an action without data. When questioned, the “data” they used was anecdotal, based on feelings. Not to be dismissive, there is a difference in feelings and data that can be validated. For example, people sitting a stop light tend to over estimate wait time.
“Other” information takes us back to the first story. Data shows that falls by older adults are under reported. Hence, it makes sense, as a medical practice, to ask rather than depend on self reporting or a patient saying, “Hey, I have trouble with balance, I may need assistance so I don’t fall over on the machine alarming the staff.” Context, context, context. The staff quickly realized as long as the mammogram did not require me to do a handstand, the probability of me falling would be negligible and that I did not need help… well I did not need help walking to the machine.
Seth Godin blogged about a similar topic today. http://sethgodin.typepad.com/seths_blog/2015/05/how-to-win-an-argument-with-a-scientist.html The conclusion:
The easy way to tell the two varieties of argument apart is to ask, “what evidence would you need to see to change your mind about this?”