‘There are rich teams and there are poor teams. Then, there’s 50 feet of crap. And then there’s us.’- Billy Beane, General Manager, Oakland Athletics
While the veracity of this film quote cannot be verified, the effectiveness of Mr. Beane’s strategy in Moneyball speaks for itself through the results that it generated. Beane and his assistant GM Peter Brand faced with the franchise’s limited budget for players, built a team of undervalued talent by taking a sophisticated ‘sabermetric’ approach to scouting and analysing players. By hiring players based on this innovative data analysis approach, Beane was able to take his team to new heights, eventually breaking the Major League Baseball record for the most consecutive wins (20). While they failed to win the World Series title as was Beane’s wish, the methodology that they adopted brought upon a scouting revolution in the game of baseball. Gradually most of the teams in the league shifted to this system, and there was a data revolution in professional baseball.
All organisations seeking real, tangible results through their skill management strategies needs to analyse the data available to them, and then act accordingly. Most of this data already exists within the organisation, and with the right tools and processing technology, important information can be interpreted from the same. There are three steps necessary for converting this data into useful information (as explained by www.digitalhrtech.com). These are:
1. Predictive analytics
The attempt to forecast what could happen in the future based on what has happened in the past.
2. Analysis and monitoring
Gathering data related to why events have happened and what is happening now.
Outlining what has happened in a clear way that can be used for comparison in the future.
Gathering and examining data after, during, and before the process of hiring has become relatively easy with the aid of new data technology. As a result of this, organisations are able to make smarter and more efficient hiring decisions. Knowledge of the data regarding salaries, benefits packages, sales data can help companies streamline and specialise their hiring process. When the organisation knows exactly what it needs, the holes it needs to fill, hiring becomes a much more efficient and productive task. Just ask the Oakland Athletics team of 2002 in case of any doubt.
Improve Employee Retention
To understand employee retention, companies first need to look at why their existing employees are leaving. Predictive analysis provides the ability to understand with reasonable accuracy in advance which top performers might be tempted to leave and the reasons behind this, as a result of which important members can be retained before they make the decision to abandon ship. Employee satisfaction itself can also ensure a high retention rate. By tracking employee needs and behaviour, companies can tailor plans to ensure that valuable top performing employees stay with the company. Maybe if such an idea had struck the Athletics, their best players won’t have left them right before their record making season.
Bill Beane was able to push his players to a level that even they did not expect from themselves. He switched player positions and playing formations, adjusting everything to be in accordance to the ideal plan. The plan which favoured their natural game and brought out the best that they could offer. Training programs can be designed to help employees become what is needed by the company, and to further align with the company culture. Data analysis can also be used to study whether any employee is facing performance issues. In such cases, extra training can be implemented to boost that individual in particular.
Irrespective of whether you’re a baseball fan or don’t have even the slightest interest in baseball, the movie’s essence can be understood by all. Traditional practices are going to be supplemented by modern revelations. Traditional scouting and intuition-based hiring, while may have worked in the past, cannot endure in the face of today’s challenges. It needs technological aid, more precisely, it needs accurate data.