I had a chance to read “Rethinking Analytics for the Social Enterprise“, a whitepaper written by Don Tapscott and Mike Dover, sponsored by SAP. Tapscott and Dover explain some of the use cases of analytics (in the context of Big Data) for various functions within the organization. Here is a reproduction on what they think will be the application of analytics for the Human Resource function.
Human Resources can measure employee sentiment, employer brand, and employee risk and knowledge management in real-time and analyze the data by location, gender, job role, or whatever other subgroup is relevant. This evaluation can be completed not just through “official” polls and surveys but also by listening to chatter on sites such as the Vault.com and Glassdoor, as well as LinkedIn and alumni groups, and even by analyzing language in email and other company correspondence.
JetBlue monitors a “crewmember net promoter score”—every month, employees are asked if they would recommend working there to a friend. This data is used to evaluate employee satisfaction.
Too often the first time a manager hears of star employees’ dissatisfaction is when they announce that they are leaving for another opportunity. Sentiment analysis can look for such language clues as “happy” and “frustrated” to assess overall morale as well as evaluating employee preferences. In fact, a 2011 study by the Corporate Executive Board indicated that the top six indicators of employee retention did not involve salary increases. In order, they were: job-interest alignment, manager quality, co-worker quality, people management, respect and a collegial environment. In addition, a strong analytics program will assess the specific financial risk of the loss of a key employee.
In addition, better organized and robust performance metrics help identify top performers for promotions and reveal employees with opportunity/vulnerability areas so that they can receive training or be disciplined or terminated. During the recruiting process, the time to find good candidates is reduced, and more precise job descriptions can be developed based on the characteristics of successful incumbent employees.