The best way to predict the future has been history. The basis of this prediction is that history repeats itself.

Raj and Asha finished dinner. They decided to go to an ice cream parlour for dessert in their new car. Raj saw that the ice cream parlour was crowded. To avoid the crowd, Raj quickly picked vanilla ice cream and returned back. He remembered that the last time when he had picked up vanilla ice cream, the car had not started. Asha had also specifically mentioned that the new car does not like vanilla ice cream, and hence, he must not buy the vanilla flavour. But he had already made that mistake. He crossed his fingers and turned on the ignition. The car did not start. Asha said, “I told you, history repeats itself.”

This is how history works. It repeats itself. That has become the base for many practices in HR. Do not be surprised, it is true!

Instances Where We Use History

History is used in all walks for HR from recruitment to retirement.

1. Recruitment: How is recruitment done? It starts with the resume of the candidate. In the new age, it can be a video resume. But it is still a resume, the history of the candidate. Then a reference check and background check are done, which is nothing but verification of history. Even during re-hiring, the past performance of the candidate is checked. That is the dependency of history in recruitment.

2. Employee Life Cycle: Most of the important employee life cycle decisions are based on the past performance of an employee. Confirmation is based on the performance of an employee during the probation period. Key decisions like career progression, compensation correction, and retention are again based on the past performance of an employee on the job.

3. HR Interventions: The HR processes, policies and interventions have become standard over the years. The heuristics for HR decision-making can be written in an encyclopaedia. HR interventions are again repeated based on how they worked in the previous historical situations.

Why is History Used?

If we have to predict the future we can either get a globe from Alice in Wonderland or a Time Machine. However, as of now, both the solutions are fairy tales. The best way to predict the future has been history. The basis of this prediction is that history repeats itself.

Statistics has found a method to model past data to predict the future. This has further increased the credibility of history in the eyes of people. Artificial Intelligence and Machine Learning use huge amounts of historical data for training. Watson, the AI tool has recorded data of thousands of cancer patients and studied patterns. Now looking at a scan, Watson can recognise patterns that even doctors are unable to see through the naked eyes and predict cancer.

Is it Right to Use History?

PT Usha is called the Queen of Indian track and field. She brought glory to India by winning a silver medal in the 1982 Asian games. She finished 4th in the 1984 Los Angeles Olympics falling behind the bronze medallist by 1/100th of a second.

All Indians had high hopes from her that she would bring a medal in the 1988 Seoul Olympics. However, she was not included in the Indian team. There were protests which forced officials to include her in the Indian team. This is the belief of people in history. However, she disappointed all her fans by finishing 7th in the qualifiers.

1. Is past performance a guarantee for better performance?

Interestingly, most of the companies give a decent weightage to the past history of achievement while hiring people. Yet, as per a career builder survey, 74% of employers say that they did hire the wrong person for a position in the last year. The cost of the wrong hire is up to 30% of the first-year salary of the wrong hire.

That brings us to the question whether hiring should be done on hopes built on past performance? Selection of past performers based on hope is a bias similar to the halo effect. Past performance is an indicator of capability, but it does not guarantee future performance. Future performance depends on multiple factors. In the case of an accomplished athlete, future performance is based on fitness, form and even hunger to prove oneself. The same applies to the accomplished corporate executives.

2. Modelling Historical Performance

Many organisations model high performers and try to put the traits of high performers as a criterion for selection for future recruitment. This is supposed to be one of the best practices and is being used in the development of machine learning algorithms as well.

If our cricket team would have modelled Gavaskar as the high performer and hired cricketers on that basis, then there would have been 11 Gavaskars in the team. We would have definitely played good cricket. However, we would have missed people like Mahendra Singh Dhoni, and the helicopter shot would not have been there and India would have never won a World Cup!

Performance is not dependent only on the talent or fitness of the individual. It is dependent on the competency of the competition, work environment, team dynamics and many other external factors. That is why successful executives of one organisation do not click in the other organisation despite all their capabilities as the work environment and culture contributes a lot to the success of individuals.

3. Are there any alternatives to history?

People trust history for a reason. It truly repeats itself. It actually works, and we possibly, do not know the reason behind it. It could be as weird as Raj’s new car not liking vanilla ice cream. The ice cream story is a true incident for General Motors. When they received a complaint that their car Pontiac does not like vanilla, they delved into the details and realised that the complaint was true. Engineers discovered that the problem was that of the vapour lock. Whenever any other ice cream flavour was bought, it took time allowing fuel vapour to settle down. Since vanilla ice cream was quick, vapour could not settle and the car did not start.

Reasoning is the alternative to history. Then you are not living by chance. You know exactly what is happening. If people are evaluated on relevant skill sets and cultural fit instead of their history, the chances of wrong hiring go down drastically. In the same manner, data scientists are changing machine learning algorithms with the development of self-driven cars. Now, it is more dependent on real-time environmental inputs like camera feed, sensors and what is happening ahead of you. Of course, you cannot drive a car looking at the rearview mirror.