Increasingly talent management processes and particularly talent development are being infused, if you will, with data from many sources:  external, pulse surveys, internal HRIS systems and still others.     With administrative processes, there are success stories emerging in using Machine Learning and AI.    For selection and development decision support, there are many attempts, promotions and "hype" but real results are not in evidence

How do you optimize your talent decision support?  Making better decisions, with increased understanding from the data, is the key objective.  Something we call Actionable Understanding. Unfortunately,  the experience of many organizations, as others have pointed out as well, has more commonly been more data, less understanding and even more confusion.   As some of the world's top organizations have found out, when you focus on the appropriate data and ensure its validity, you get increased understanding and make better talent decisions.   With effective job-specific behavioral suitability information,  you can get job and competency specific benchmarks on employees and candidates and group data to make better decisions on leadership development, employee development, succession and overall talent planning and  performance management. You can also provide your managers with actionable data that they can use to improve performance and engagement.   In the video discussion below, we explain why this is so.  Please let us know if you have any comments or input.