I’m a type 1 diabetic. My life consists of data points: checking my blood sugar multiple times per day, counting carbohydrates, accounting for exercise and stress and lack of sleep, regular visits to the endocrinologist, basal and bolus and correction rates, the A1C (a three-month average of blood glucose readings) and other lab work. It’s a lot of data. Overwhelming at times.
The data is important, though. It tells a story. It alerts my doctor when something isn’t working, usually evidenced by a helter-skelter graph of blood glucose readings (It sometimes bears an uncanny resemblance to my Google Analytics.). It can tell me my basal, bolus, or correction rate needs to be adjusted. It can state I need to review how to count carbohydrates. It can do all those things, but it only can do it if I’m measuring, and if I’m measuring the right things.
The trick to measuring the right things is to focus on one area before addressing or adding another. For instance, if my doctor and I know that my blood sugars are bouncing between highs and lows, we gather additional data through a constant glucose monitoring system and food logs. That data will show whether my carb counting is off or whether my insulin rates need to be adjusted. We’ll make some changes based on that data – I’ll go back to carb counting class or adjustments will be made to whatever rates need it – and we’ll test again. We’ll keep testing until we achieve our desired outcome, which, in this case, is more tightly controlled blood sugars.
This isn’t a process reserved for diabetics; it’s to be used in organizations and businesses. To achieve desired outcomes – increased sales or people taking on a cause – data has to be measured because data tells a story. It shows where certain actions aren’t bringing the desired results, and it isolates areas for improvement. It says that the plot isn’t quite working, but with some tweaks and a lot more testing, it just might.
As for my story, it doesn’t actually end when I achieve more tightly controlled blood sugars. The testing continues because, as with businesses and organizations, a one-hit wonder doesn’t do the trick. Life interrupts. Data changes. What once worked now doesn’t. When those things happen, it’s back to testing and measuring. It’s back to remembering what the desired conclusion is and identifying and mapping the actions that will lead to it.
Image: John (CC BY NC 2.0)