Last week, I published the first in a series of three posts imagining what HCM might look like enabled by emerging (and existing technologies). I will reiterate the same disclaimer. Much of what I describe is possible today, but you cannot buy it off the shelf. Also, you might read the story and say to yourself that it has no applicability to your organization. That is fine. If it is not applicable, then you should write your own story for your organization. The easiest way to predict the future is to create it yourself and imagining what may be possible can be a good first step.
This story leverages mobile computing, social networking, pattern recognition, large information spaces, and smart content to highlight retention risk.
John is a sales manager at a software company based in Manila. On his way to work in the morning, John gets an alert on his mobile phone that there is a potential retention risk that requires his immediate attention. Paul, a pre-sales consultant on his team, has been flagged as a retention risk. Most of the time when the talent management system flags a retention risk, it is not an urgent matter. John also is surprised that Paul would be a high priority. He is a good performer, but he is not in a critical position. The alert provides additional information to John about why this is an urgent matter. Though Paul is not in a critical role, the talent management system, through social network analysis, has noticed that most of the high performing account executives interact frequently with Paul. John drills down into the social networking system and sees that these high performing account executives often go to Paul for advice on simplifying complex technical issues for prospective customers. John now understands why this is an urgent issue.
The alert also provides information about why the talent management system has flagged Paul as a retention risk. There are a number of signals that it has found that point to a potential issue. The system has noticed that Paul’s internal social network activity is down and that his external social networking activity has risen. It has also noticed that Paul has taken an unusual number of sick days in the last 3 months. In addition, a regular background check indicated that Paul’s credit rating has lowered.
Related to the pre-sales consultant job, the talent management system has also noticed that there is an increase the voluntary turnover rate for pre-sales consultants. Market compensation data indicates that the compensation rate for pre-sales consultants at John’s company is below market average. Thus, risks around the job are higher than normal.
The alert also indicates that the external environment has started to improve. Paul is located in San Francisco where the local unemployment rates are falling. In addition, the national unemployment rate is falling. The turnover rate for pre-sales consultants at John’s company is higher than the local and national averages. Also, competitors have increased the number of job openings for pre-sales consultants.
John thinks about all of these data points. He knows that Paul has been battling the flu bug on and off for the better part of the three months. That would likely explain the changes in social networking behavior. It is also likely that this may also be causing Paul to pay his bills late. The talent management system suggests several potential actions to take mitigate the retention risk. John decides that he needs to talk to Paul right away to better assess the situation and to see what next steps to take.
I ended the story this way for a reason. There is a significant opportunity to leverage analytics to make better talent decisions, but not all of it can be automated. It should be decision support. A supervisor/manager/executive still needs to apply judgment to the information presented. In our example, even though there are many signals that indicated that Paul may leave, there may be information missing that is not tracked but the leader knows that would lead to a different interpretation of the data. However, it is better that leaders are informed and have information in context to help them make better decisions about talent (or any other business issue for that matter).
Does this scenario go to far? Is it impractical to try to bring all of this information together? Can it lead to the wrong conclusions? What do you think?
