Its all about the people. And there is a secret recipe that mathematics can find for you. The combination that works in selecting, aligning, engaging and nurturing people.
People science now can help build high performing teams by providing insights and nudges. Each organization is special and its difficult to define it in individual stories that communicate more than specific attributes but the whole picture.
A very simple data correlation that gets missed out in most companies is the correlation between performance and hiring. The best teams are not the smartest teams but teams with high EQ that work well together. Diversity is another aspect that can acquire the shape of a problem that science can also solve. What is the optimum mix of talent in the team.
Quantity and quality. Project science has evolved to a stage to make most service aspects as a measurable plan number. Agile science has also helped in adjusting the number. From a team composition perspective its the quality that is the hard determinant that can also change the quantity of resources needed.
There are abundant disruptive examples of this where the right team of 20 has outshone a regular team of 200. Google search was created by 2 individuals working in pairs to write the core of the search engine that went on to get google in the global big league. WhatsApp was created by a core team of 19 programmers that sold for USD 19billion, much higher than any traditional workforce driven company with thousands of employees.
So how do you identify the secret recipe. The amount and the mix. The individual, the interconnects and the container as a whole. This would mean employees that are individually very skilled, work and collaborate well with each other and are motivated and bought into the corporate vision - while being self starters.
Technology can help. Psychometrics, experience and talent mix formulas, engagement score measurements can provide for the right insights into this secret recipe.
Case in point, the traits that work well in your organization, for a specific function and an objective are very specific to your organization's case and situation. This also has a time variable around it. What worked in 2009 will definitely not work in 2019. It will definitely end up looking like a movie with old stars trying to create the magic of yesteryears still acting as if they were 20 year olds - a sure recipe for a flop. Whats more relevant is real time data, unsupervised learning of the AI model to figure out shifts and changes and make recommendations on what needs to be reinforced and what needs to change.
Platforms like peopleHum https://www.peoplehum.com can now help you with this analysis and take it a step beyond.
The reinforcement algorithm
Most performance platforms that measure traits that succed in a specific time and case situation do not really collate, analyze, interpret and feed that insight into hiring platforms for reinforcements. Hence performance evaluation happens in a black box while hiring is happening in a black box.
The value addition of making these 2 models talk to each other with the real time data that can be fed can lead to better effectiveness and a higher rate of success in meeting your organizations business goals and objectives.
With an integrated platform thats listening to your employees and using data science to analyze and then feed it as mathematical model weightages to hiring algorithms can consistently automate this specific case far better than assuming HR understands the functions and has the right gut to filter out candidates with the right behavior and skills to succeed in a group.
Such analysis and correlations are now becoming game changers in organizational collaboration and construction.
See it happen in peopleHum today https://www.peoplehum.com/#blogs