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6 Decision trees can produce the best new talent

Decision trees can produce the best new talent

project-management

Decision tree ideas are easy to apply when we are dealing with project management. Determine the probabilities of failure and success and apply them to financial forecasts. Choose the decision that offers the highest expected return.

So how do we apply this concept when considering human talent? Here are a few tips.

No Considering Human Talent Tips
1 Human talent
2 Initial investment
3 Financial results
4 Probability
5 Dynamic environment
6 Education

1. Human talent

It is not possible to determine the complete inner motivation of every newly recruited talent. Talent can be nurtured on the first interview with a range of self-help books and YouTube tutorials. Only time can tell the true value that talent brings to the plate.

Unfortunately, the importance of building decision trees is compounded by how quickly they can be used to develop, train, and assess talent. This is because talents may not last long enough to fully develop.

2. Initial investment

Decision trees always start with different initial investments. This is to point out potential losses if the decisions made are wrong. It is the punishment for bad decisions. More importantly, each initial investment underscores the value the company places on each new talent. There is an open two-way communication because each party can negotiate.

3. Financial results

The hardest part is getting accurate financial results on how the talent will contribute to the organization. If this employee is a Workplace Consultant, is the bottom line calculated based on the increase in productivity after the session? Or, in the case of someone washing dishes in a restaurant, can we measure the actual change in washing speed versus the investment in a dishwasher?

4. Probability

In my own classes, I find that my students are very skeptical about how probabilities are derived. I would like to start by saying that detailed market research has been conducted and in most cases additional statistical experimentation has been carried out to ensure that any prediction error has been minimized to an acceptable level - which means that it is unlikely that we will ever be able to make perfect predictions about the future. But here's the thought: if all probabilities had the same margin of error, wouldn't the analysis be combined much more accurately?

5. Dynamic environment

No one could have predicted that in 2000 the world would fall into the Covid pandemic and there would be massive unemployment around the world. Therefore, a typical decision tree has the main disadvantage of being represented statically. However, with improvements in data collection and analysis, the various components of the decision tree can be made more accurate.

For the purposes of debate, what if there is a disturbance in the environment that causes a talent to reevaluate its value? Take for example Lionel Messi's transfer to PSG and his huge salary expectations. PSG need to sell 5 of their current group of players to make up the shortfall. Take the place of a PSG player who has to leave the club and whose children have just moved to the school of their choice. This sudden situation was like a thunderbolt, forcing him to move and adapt.

6. Education

It is essential that HR maintains an open door policy that allows any new talent to provide feedback on the job in a non-confrontational manner. This then allows both parties to agree to receive appropriate training programs to increase talent productivity without unduly affecting their downtime. This creates a high level of acceptance among the talents, who can ultimately play an important role as a mentor if they decide to stay with the company.