Great post on performance management as a lever to achieve specific business outcomes. I wrote about the concept in a broader sense for talent management at the end of 2007 here "Unlocking the Strategic Value From Talent Management Application Investments" http://www.gartner.com/DisplayDocument?id=571207 - Gartner subscription required. However, the notion of performance variance as something to manage is an interesting one.
It was interesting to hear Kenexa in a sense moving back toward its roots. They certainly will compete on product capabilities, but their service offerings were definitely on equal footing at this event. In his opening address, Troy Kanter did a nice job laying out how Kenexa’s software, content, and services had worked together to deliver significant value to customers. Unfortunately, the customers were not named specifically (one example focused on movie theater chain). I thought it would have been more effective to have their customers tell the story in their own words, but, nonetheless, the case studies were compelling examples of the power of talent management to drive important business outcomes.
One of the challenges I find working with clients is that they often leap too quickly to a discussion of the feature/functions of various talent management vendors as an answer to an ill-defined or often undefined business problem. Linking software capabilities (and content and services) to the business problem(s) is/are crucial to successful talent management application investments.
Kenexa’s customer engagement philosophy is a good one to help avoid the business problem disconnect trap. Through its service offerings, Kenexa (and other vendors that offer these kinds of services) can help a customer define the business problems to be addressed before moving to the technology enablement. The challenge for Kenexa is to manage the transition to a next generation product (x2) and convince buyers that not only do they need to look beyond just product capabilities, but also that Kenexa is the best-suited to provide that holistic software, content, and service solution.
That is a tall order in a market with strong competitors and investors rewarding a more software-centric business model. However, by focusing more attention on its services core competency, Kenexa does provide a different alternative to most talent management application vendors today. Different is good in a market like talent management where messaging from most of the vendors is the same.
This is the third in a series of posts (here and here) imagining what HCM might look like enabled by emerging (and existing technologies). 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 social networking, telepresence, visualization and smart content to improve succession planning and internal mobility.
Sue runs the quantitative research unit at a large, global investment bank. She is about to start her quarterly talent review meeting with her direct reports around the world. Because of the type of meeting it is, the attendees, their physical locations, the internal social networking solution creates a telepresence session and adds participants to the session as they enter their collaborative workspace for the talent reviews. Once everyone is present, Sue kicks off the meeting.
The group looks at a 9-box that plots performance vs. potential and a chart that plots HiPos on a continuum of retention risk. There is an indicator for the HiPos that have retention risk of the bench strength for the position the HiPo currently occupies as well as for where that person may be a potential successor. Based on the information presented, Sue starts the discussion with Justin. Clicking on Justin in the visualization brings up Justin’s talent profile.
The talent profile pulls together his demographic data, talent data, social data, data sourced from third-parties, and data sourced from the public internet. Greg, Justin’s direct manager, has noted on the talent profile that he is concerned that Justin might leave because he feels he is ready for a promotion, but does not see an opportunity opening up in the next year. Sue acknowledges that it is unlikely that a suitable position will open up in her business unit. So, Sue asks the talent management solution to see what other opportunities may be available to see in other parts of the business. The talent management system compares Justin’s profile data with success profiles for both open positions as well as future projected openings based planned initiatives and the workforce plan. The talent management system indicates that a new joint venture has been approved in the Middle East. Justin’s behavioral profile indicated that he is entrepreneurial. His performance reviews indicate he has been strong manager of his small team. In addition, the talent management system saw from his social network interactions that he has been taking Arabic lessons for the last two years. Sue discussed this potential assignment with the team and concluded that not only would Justin fill be able to fill this need for a Director in the joint venture, he might actually welcome such an opportunity. In addition, they also agreed that it would also round out competencies required for future opportunities within the quantitative research unit if it became available.
The talent profile shown above does not exactly match the one described in the story. It is actually a resume of a graphic designer that demonstrates his skills in information visualization (click on the graphic to go to Justin’s blog). All of the data though could exist in an HCM solution, but we are not quite at the point with solutions to get this kind of visualization to see at a glance an individual’s capabilities.
I also like this story because it emphasizes the important role workforce planning has to play in proactive talent management.
How do you think the visualization and intelligence of talent management solutions will evolve in the future?
Interesting read with implications for performance management solutions. I am not sure I agree that once you are a "B" or "C" player there is no opportunity for movement or improvement in a hierarchical organization, but it is thought-provoking nonetheless.
Another good read from Dr. Sullivan. In addition to identifying where in the process issues may arise, I would also add that you should follow good quality management techniques. The earlier you can identify an error (or defect in QM terms) in the process, the less it costs (using the term loosely here) to fix.
Great post on the importance of metrics HR organizations and communicating results. I would add that it is even better if you can get the CFO to be your ally in terms of formulating those metrics and tying them to business value.
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?