Thursday 29 June 2023

Why didn't I achieve my goal?

“No one can hit their target with their eyes closed.”

- Paulo Coelho

At the beginning of the month, I set myself a goal. 

The goal was to finish a certain number of Pomodoros during the month. The details of how many, doesn’t really matter. For a long time now, I’ve measured the number of Pomodoros I complete in a day, a week, a month, etc. 

I set off with good intentions and a quiet confidence that I'll achieve my target. 

As I near the end of the month, it becomes obvious that things are not going well. By the end of the month, I’d completely failed to achieve it. In fact, I’ve achieved less than half the number I set as a goal.

So what? It happens. But does it have to? 

All data is good data, and perhaps this is just the stepping-stone I need. I attempt to figure out what went wrong. It’s a lot more fun to be successful than unsuccessful, at whatever it is you is trying to accomplish. 

Was the goal even achievable? Stretch goals are good, impossible goals are less motivating. 

Had I kept it in front of me during the month? Well, yes and no. I’d measured my Pomodoros every day, but if the information had been compelling enough, I would have reacted much earlier. By the time I’d realised I wasn’t completing enough Pomodoros, it was too late - I couldn’t get back on track.

What had gone wrong with my reporting? Why didn’t my data persuade me to act sooner?

The answer lies in the type of analytics I was looking at. I was reporting on how many Pomodoros I had done in a day, and week, or a month. I could compare to previous days, or weeks, months, and even years. I could look at trends and make comparisons. 

But none of my data told me WHY these things were happening. Why could I achieve more on one day than other?

The report I was using was descriptive, that is it reported faithfully what had happened. It might have had trends and comparisons, but it was still just descriptive.

What I need is diagnostic reporting. I need some insight into why some days are better than others. What factors make it more likely that my day will go well, and what are the danger signs that things are going off the rails. Diagnostic analytics reports on the factors that affect the outcome.

How do we know whether our analytics are descriptive, or diagnostic?

Descriptive analytics aggregates and compares data to understand trends and relationships. Achieving 15 Pomodoros in a day is descriptive. As is achieving 4 Pomodoros in a day (it happens).

Diagnostic analytics uses additional data to understand why it happened. An urgent deadline, and no appointments might be the explanation for completing 15 Pomodoros in a day. Back-to-back meetings and starting work late might explain 4 or less Pomodoros in a day.

Descriptive and diagnostic analytics can be used together or separately, and it’s worth knowing the difference between them.