Are there parallels between clean eating and individual performance, and data and business performance? I’ve applied data principles to clean eating and found some great similarities. And my simple lifestyle tracker continues to be spot on.
Data and Business Performance
While reading up on data science last week, I came across an article titled “Why Data Driven Decision Making is Your Path the Business Success.” It referenced a study with the MIT Center for Digital Business. MIT Sloan School of Management professors Andrew McAfee and Erik Brynjolfsson found that among the companies surveyed, the ones that were mostly data-driven had 4% higher productivity and 6% higher profits than the average.
I realize this is not new news, as the fastest growing jobs today are in data science and machine learning (according to the LinkedIn 2017 U.S. Emerging Jobs Report). So how does this relate to nutrition, eating, and our overall health?
Like most of you, I want to increase my performance at the office and at home, and deliver better results. Some of you may take classes or on-line training or take on new assignments. For me, I’m increasing my overall health so I have more energy, can think clearer and enjoy what I do; all purported benefits of clean eating.
I love frameworks and models, which is one reason I’m wanting to validate the lifestyle tracker. And for me, a good model must be simple and enhance understanding. One model that meets these criteria is the basic systems approach connecting inputs, processes, and outputs. This model can be used to help us understand what happens:
- in a factory (timber is cut to become lumber),
- in an automobile (fuel is combined with oxygen and a spark to combust and create the energy needed to propel),
- and in data-based decision making (data is captured, analyzed, and insights provided).
How many times have we reviewed a report only to suspect bad data? By some accounts, 40% of company data is thought to be inaccurate, leading to poor business decisions (or decisions based on gut feel rather than data) and missed opportunities. In a perfect world, we’d have perfectly clean data, going in to perfect algorithms all leading to insightful business information.
A useful rule to follow when assessing the impact of your data quality is the 1-10-100 rule, which was first proposed by George Labovitz and Yu Sang Chang in the early ’90s. In the 1-10-100 rule, there are three phases, each of which explains the cost of maintaining data quality.
In the first phase, the “prevention” stage, $1 represents the amount it costs to verify accurate data at the point of capture. This is the simplest and least expensive way of gathering data and ensuring its accuracy and validity. However, as we move into the second phase, the “correction” phase, we see that the initial $1 rises exponentially to $10. This $10 represents the increased cost that an incorrect data will have on your business down the line. And as we head into phase 3, the “failure” phase, we see that the amount increases tenfold to $100. This $100 represents the amount companies will pay for doing nothing about their poor data.
A Healthy Comparison
How can these models apply to our health? We consume food, our bodies digest and create energy. This is a basic systems approach. And thinking of food as conceptually similar to data-based decisions, the cleaner the food, the better we can use it to create energy our bodies need to operate.
What about the 1-10-100 model? It suggests it will be less expensive to start with clean food in the prevention phase. By eating “dirty” foods, foods our body doesn’t process well, we will incur additional expenses in the correction phase. In this case, we can consider the $66 billion weight-loss industry to the $25 billion fitness industry as the investment we are making to fix the issues from the prevention phase.
How would we characterize the failure phase? From a health perspective, we might consider the cost of treating preventable “lifestyle” diseases in the US, which in 2011 was nearly a TRILLION dollars. The latter cost doesn’t consider productivity or performance losses either.
So while not a perfect fit, these models help us put our current health situation into simple, understandable concepts.
Where Will You Invest?
We try to get to the gym. We go on diets only to regain the weight. The result is nearly 35% of Americans being obese. We work over 40 hours/week. We all need to eat. I recommend taking the path that will provide the better result; focus on eating clean food as a long term solution to better health and performance.
Lifestyle Tracker Update
What a great week! Nutrition-wise I ate 19 out of 21 meals comprised solely of clean ingredients (TimeChop type meals) which gave me the opportunity to lose weight. When combined with 4 more exercise points than beer points, the model forecasted a weight loss trend, and I actually lost 1/2 pound. I’m pretty excited that my simple model is 5 for 5 in predicting my weight direction based primarily on clean eating. And I didn’t count calories or put severe restrictions on going out and having fun.
2018 Lifestyle Tracker
|Week Ending||Whole Food Nutrition (out of 21 possible)||Weekly Exercise||Weekly "Beer"||Formula Result||Actual Result|
|1/6/2018||19||5||6||Weight Loss||Lost 1 lb.|
|1/13/2018||20||5||4||Weight Loss||Lost 1 lb.|
|1/20/2018||19||6||5||Weight Loss||Lost 1 lb.|
|1/27/2018||17||3||5||Weight Gain||Gained 1/2 lb.|
|2/3/2018||19||6||2||Weight Loss||Lost 1/2 lb.|
|2/10/1018||20||4||9||Weight Gain||No scale for weighing|
|2/17/1018||19||4||2||Weight Loss||Potential Loss (assumes gain in prior week)|