Scores, sometimes, don't tell the entire story.
In 2015, Pakistan played Australia in the Cricket World Cup Quarterfinal.
On paper, the verdict was that Australia crushed the greenshirts by a whopping 6 wickets. Not only that, they chased down the target in super quick time (34 overs).
Now, what that scoreline doesn't reveal is that the game wasn't all that one-sided.
Wahab Riaz put on a brave, fiery spell of fast bowling that put everyone on the edge for a while, leaving seasoned players like Shane Watson flummoxed on multiple occasions.
Had a couple of catches not been dropped, the result would have been different.
However, while the score hides away these finer details, it remains a cold, hard fact.
After the game, the Pakistani captain cited the poor fielding as a major factor but accepted defeat saying we needed to improve.
That made sense. Wasting too much energy exonerating yourself is futile.
Most cricket enthusiasts in the future will in any case read the scoreline as a humiliating defeat.
Numbers can be insensitive that way.
It happens in product as well.
A while back at vFairs, I was on track to hit my lead gen targets for the month with 4 days left. It was going to be a cake walk.
I didn't realize that a long weekend was coming up for a couple of key markets during those final days.
Uh-oh.
As expected, leads nosedived and I missed my target by a few. I was gutted.
I initially thought of taking a super defensive stance. I didn't want to be blamed for this.
Then, I realized that the KPI sheet will still show this as a red cell, no matter what words I sprinkled on it. When this would be viewed in the future, "my defense" would be tucked away in a comment.
Instead, I decided to accept the loss.
And then refocused my energy to conlverting Insights to Foresights.
Insight =
Holidays & long weekends in Tier 1 markets invariably affect lead volumes.
Foresight = Plan for holidays upfront
The result was increased vigilance.
Rather than hand-waving the below-par month as "not my fault", we decided to learn from it and succeed in times to come.
And it worked. We became better at planning.
That's why retrospectives are a crucial part of the PM practice.
This also made me wonder how machine learning benefits from not having an ego.
It works on the premise that it will make mistakes in the start. But with greater volumes of data, it promises to get much better because of its feedback loop. It learns equally from failures and wins.
How can we be more like that?
Stop taking failures to heart.
Take them to the learning lab instead.
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