Have you ever wondered why many of the NBA’s shooting greats hold the same frame after releasing the ball? The ball is already gone so holding the frame is not going to change the outcome. At the completion of a swing, the golf club is so far back that a golfer could almost kiss the club head. Darts players tend to freeze their hand in a pointing position as if to psycho kinetically guide the dart to the bull’s eye.
The follow-through is a leading indicator for achieving what we intend to achieve. Indeed, you may have observed amateurs and pros alike either celebrate or yell out a few choice words before the basketball reaches the hoop or the golf ball lands on the green or in the lagoon. This follow-through-mindset or way of achieving a goal can be applied to our engineering work as well.
1. AI for CFD: Intro (part 1)
2. Using Artificial Intelligence to detect COVID-19
3. Real vs Fake Tweet Detection using a BERT Transformer Model in few lines of code
4. Machine Learning System Design
We could bucket any work cycle into planning, executing and learning phases. In each of these phases, one can apply the follow-through-mindset to assess whether they have set themselves up well for the next stage.
The beginning of any cycle is typically as alive with activity as a sunny morning in a tropical rainforest. It typically conjures up images of the early phase of the Gartner hype cycle. Your goal as a planning team is to tamp down the peak of inflated expectations and avoid the valley of disillusionment.
So how might we achieve this? There are two simple questions that a team must answer:
- What are our goals and why do they matter? → Understand
- What are the best ways to achieve our goals? → Identify
The conclusion of a good planning period should leave the team with a well-grounded sense of optimism for what the near-future holds.
Follow-through-state:
- Clear goals
- A concise body of work needed to achieve goals
- Confidence in expected impact
Having a leading-metric for success is particularly critical in hardware sales because the conversion cycle is much longer than a “move fast” culture is comfortable with. So in order to make ship decisions faster and compound learnings, one needs to know with a good level of certainty that achieving a follow-through state, X, implies statistically significant increases in sales Y months later. In this phase, we must check and double-check our assumptions and execution to ensure that don’t get ensnared in the usual “gotchas” e.g. network effect contamination, test-control imbalances, slow exposure velocity etc
Follow-through-state:
- Live and valid experiments
- Defined and tracked leading metrics
When we evangelize our processes effectively, and repeat incessantly, the steps to the follow-through position become invisible as we execute them without conscious thought. They become muscle memory. This frees up our minds, to think about next best action. The basketball player might want to run in for the rebound or run back to defend. This decision is made before the ball approaches the rim. The best players and teams whether in sports or tech appear to continuously leverage this upward cycle, freeing up mental space to ponder the next best action.
Follow-through-state:
- Logical conclusions → A leads to B
- Valid deductions → Therefore C is likely to lead D
As you plan, execute and learn it might be worthwhile to ask yourself, if the team achieved good follow-through-states at each phase. If the answer is “no”, the time to raise the alarm is now as opposed to the end of the cycle.
Credit: BecomingHuman By: The AI LAB