Does your talent management system actually help you make effective talent decisions?
One would hope the answer to this question would be a resounding “yes!” … but that may not be the case. Likely, it is a question that is not even being asked.
Making better talent decisions is not the central driver for many talent management system implementations and it is likely not how ROI is being measured or viewed. A big driver behind initial technology implementations is realizing efficiency gains — making the performance management process paperless, managing the hiring process with less effort and resources, etc. The pain of paper is highly visible and the return on investment of automating cumbersome processes is easily quantified.
To date talent management software providers have focused on automating processes that were seen as being at the core of talent management. While these solution platforms have contributed substantial efficiency gains through process automation, it can be argued they have had limited impact on increasing talent management effectiveness.
The big problem with talent management applications today is the data. Companies do not have the content or data to understand who their best people are and why. The concentration on process automation has neglected generating the talent measurements needed for effective talent decision-making.
Here are a couple of examples:
The value of predictive talent measurements is well documented. During the last several decades there have been numerous large scale research studies across a number of industries demonstrating their value. Many of these studies were conducted by large consulting firms that offered talent measurement services.
For example, the Aberdeen Group conducted research on talent assessment strategies in over 400 organizations in 2010. Their research highlighted the importance of talent measurements in making talent decisions and showed a clear connection between use of talent measurements and “best-in-class” organizational performance. The type of talent measurements that Aberdeen found which enabled this level of performance included:
One would think that talent measurement would already be a major part of talent management technology and these types of talent measurements would be readily available. However, the relationship between talent management technology providers and talent measurement providers has been modest at best. Most technology providers will have measurement partners as an optional service but the measurements are not an embedded part of the application nor are they fully leveraged throughout the employee life cycle.
There are several reasons for this gap:
To appreciate the importance of talent measurements in making effective talent decisions, let’s consider four key talent management questions:
To answer this question accurately, organizations need to consider both the “what’s” and “how’s” of performance. Results against goals represent the “what’s” and are typically obtained from performance management data. Behavioral ratings within key competency areas represent the “how’s.” These two dimensions can be represented in a nine-block graphic to identify individuals who are achieving outstanding results through the demonstration of exemplary behaviors.
A key to effective competency measurement is a well-defined competency model that includes clear performance standards for making accurate behavioral ratings. Unfortunately, many talent management platforms do not offer such models as embedded content nor do many directly support both aspects of performance management.
The measure needed here is a reliable and accurate norm-based measure of advancement potential. A norm based measurement requires a conceptual model and norm-based assessments. Consider the following multi-dimensional model:
Note: This model suggests that cognitive ability testing, personality testing, norm-based experience measures, and competency performance measures are all needed to produce an accurate measure of advancement potential. Unfortunately, such norm-based measures are typically not available as embedded components of talent management systems which typically just collect subjective manager ratings.
The measurements needed to answer this question are reliable skill and experience gap profiles. Competency gaps for individuals and groups are often generated through a multi-rater process. However, not all multi-rater processes generate quality measurements. Many traditional or basic approaches are characterized by low self-other agreement and inflated ratings with limited variability.
Quality processes should include well-defined competency models with clear performance standards for making accurate behavioral ratings. Also, norm- based experience inventories provide insight into experience gaps. Unfortunately most talent management platforms do not offer quality multi-rater processes or norm-based experience inventories.
The key to matching talent to roles is using proven predictors of job success using data generated from activities such as structured behavioral interviews, job-relevant testing, and behavioral simulations. Ideally, you generate a comprehensive job requirement profile and a comprehensive talent profile that both use measurements that truly matter for job performance.
The ideal summary measurement would be a predictive index quantifying the degree of fit between the two profiles with a clear view of where there are matches and non-matches. Unfortunately, most talent management systems do not truly support comprehensive job requirement profiling, comprehensive behavioral interview management, the administration of tests, or matching analytics that use predictive, rather than descriptive data.
Human resources and technology professionals contemplating a talent management solution should ask how the platform incorporates and utilizes talent measurement capabilities to ensure their solution will support quality talent decisions. As customers reach maturity with the process automation benefits of their talent management platforms, it will become increasingly apparent that they do not have the quality data needed to make optimal talent decisions.
This will drive a marriage between talent measurement platforms and talent management process providers. This relationship will allow the talent management systems to truly deliver on the promise of effective talent decision-making.