In 2016, Deloitte published the results of a management survey that suggested that over 80% of people-based decisions in the commercial world are made using gut feel rather than any data or science. A totally different picture emerges when comparing this approach to decisions taken in the sporting environment. Based on our own experience of working with leading cycling, football and rugby teams, all performance reviews, recruitment, selection and athlete wellbeing decisions are informed or guided by data analysis.
What does this tell us about the key differences between commercial organisations and the sporting environment?
Will sporting analytics approaches really work for businesses?
Sport has access to data that is not easily replicable in business which may mean sporting analytics is dismissed as unworkable in business:
1. Closed loop of data – this is a review of a specific sporting event which comprises a closed loop, or fixed data set, with a definite end date and no new data can be added
2. Objective data capture – Video, GPS and other bio-mechanical data is used routinely in most sports after years of investment
The focus of sporting teams, and their use of data, is on performance delivery and athlete and team development. Sports teams know that to achieve success they must ultimately grow or attract the best players, managers and support crew that they can.
Sport and business are two different environments, but are they really that different?
Of course, the definition of success and winning might take a broader set of measures than in sport but ultimately a core focus is on performance and development.
And although having the ‘best’ people and teams alone does not guarantee you success as there are other environmental factors such as product, systems and technology that contribute to success, it does increase a company’s chances of success. Is objective data attainable in business?
Is objective data attainable in business?
Although business organisations do not normally video their people in the work place, they are still data rich environments where analytics can be successfully applied to a broad range of data sets in areas including:
• Customer satisfaction
• Individual and team performance
• Company goal achievement
• Organisational structure
• Promotion rates
• Personality and psychometric
This data can be used in conjunction with the right models to identify profiles of individuals, teams and departments that function well and to align teams around values, behaviours and performance. By extension, people data can also be used to analyse the cultural alignment of teams, identify silos that exist and predict and model risk outcomes from non-delivery of performance.
Data driven profiles of this depth can help support hiring decisions, promotion decisions, succession planning, team organisation and the support processes and training and development approaches of an organisation.
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