Talent Analytics: Another Business Intelligence Research Opportunity

Originally published November 3, 2011

I believe that every person is born with talent.
Maya Angelou

No sooner had my article, 10 Opportunities for Business Intelligence Research  been published than I received an email stating that I also should have included talent analytics on my list. As I am always thrilled to receive feedback on my articles, I thought it appropriate to take this opportunity to examine why someone might want to focus on talent analytics in their academic research.

Talent analytics is a concept that argues that companies should be adopting sophisticated methods of analyzing employee data to enhance their competitive advantage. That is, talent analytics is about examining and optimizing human talent to better map workforce capabilities with organizational objectives.

Talent analytics advocates contend that organizations dealing with workforce- and organization-related challenges need an accurate assessment of the way things really are and evidence as to why problems exist. They argue that data should be collected to evaluate things such as whether work is properly aligned with corporate strategy, to assess how human capital is allocated, to assess skills inventory with performance objectives, to help companies make better predictions of performance, to learn about and share effective management approaches, and to gain a deeper understanding of the organization’s ability to attract the talent it needs to support business strategy. Talent analytics is not about traditional workforce measures such as headcount, turnover, and cost-based metrics. It is about workforce planning and talent acquisition, capability development and performance, and cultivating human potential and leadership development.

In their October 2010 Harvard Business Review article entitled Competing on Talent Analytics, Tom Davenport, Jeanne Harris, and Jeremy Shapiro identified six forms of analytics that can be used to address talent issues:
  1. Human Capital Facts – What are the key indicators of the organization’s health?
  2. Analytical HR – Which units, departments and individuals need attention?
  3. Human-Capital Investment Analysis – Which actions have the greatest impact on the business?
  4. Workforce Forecasts – How do you know when to staff up or cut back?
  5. Talent Value Model – Why do employees choose to stay or leave?
  6. Talent Supply Chain – How should workforce requirements adapt to changes in the business environment?
The authors note that for talent analytics to be successful, organizations need to deal with some peripheral data issues. For example, despite the availability of enterprise human resources (HR) data, organizations might need to supplement this with more qualitative, subjective data such as supervisory and peer opinions. Moreover, some privy HR data would need to be made available to others within the organization for talent assessments.

In their forthcoming book, Calculating Success: How the New Workplace Analytics Will Revitalize Your Organization  (Harvard Business Press), Eric Lesser, Carl Hoffmann, and Tim Ringo show how using analytics can dramatically improve a company's ability to make better and faster talent decisions. This book provides a framework that enables executives to rethink how they use information on talent to answer essential questions instead of simply reacting to metrics that may not fit the problem.

The authors organize the book around the four most crucial questions managers must ask:
  1. How should work be structured to align with corporate strategy?
  2. How should human capital be allocated across the company? 
  3. How can individuals be motivated and incented to achieve organizational goals? 
  4. How can individuals learn and companies develop the new capabilities and talent needed for success?
By using analytic approaches to each of these questions, the authors believe that managers will be better able to understand what  drives – and is driven by – shifts in workforce performance.

Talent analytics is getting a lot of traction on the Web. My own investigation revealed a plethora of Web articles that included anecdotes of company experiences (e.g., Google, Harrahs, AT&T, Best Buy), information about vendor products (e.g., IBM’s Workforce Performance Talent Analytics, Taleo’s Workforce Analytics, Talent Analytics, Oracle Fusion HCM Talent Management), and various blogs with articles and discussions on the topic.

One article in particular got my attention: Future of HCM Analytics – Workforce Predictions (with Poll) . The author shared the results of an informal poll of webinar attendees who are interested in or using workforce analytics software. It showed that 49% of the people responding said their organization has one or more workers dedicated to workforce analytics, while 51% said their organization has less than one worker dedicated to it. The survey also reported that 1% of respondents said their company spends more than 1% of the business intelligence (BI) budget on workforce analytics, 38% of respondents said their company spends less than 1% of the BI budget on it, and 51% of respondents said they do not how much their company spends on it. This is disconcerting because in 2008, Hewitt’s Human Capital Consulting reported the results of a survey they conducted that showed that fewer than 10% of the companies said they tracked the quality of talent or used specific quantitative frameworks to align human capital investments with their business strategy ). The findings of these two surveys bring into question whether talent analytics is making significant inroads into organizational workforce management practices.

While reading about talent analytics, I was struck by the thought that talent analytics is as relevant to knowledge management as it is to business intelligence. We all know that many expert managers use analytics and data (business intelligence) when evaluating people, processes, and situations and when deciding on strategies. However, many successful managers also employ other less quantifiable information (e.g., qualitative data, heuristics) that is often based on their professional experience. If these managers can articulate or tangibly demonstrate how they instruct, mentor, and make decisions, their knowledge can be made explicit, thereby allowing it to be electronically captured, stored, and shared. When this leadership knowledge is made explicit, it can then be incorporated with other data and used in talent analytic efforts. This type of effort is within the domain of knowledge management, and it suggests how knowledge management can and should be incorporated into the practice of talent analytics.

I see the practice of talent analytics as being as much of an art as it is a science. The science part comes from its use of data and analytics. It is an art because talent analytics must also incorporate impressions, insights, and value judgments if it is to be successful. After all, the dictionary defines the word “talent” as a special natural ability or aptitude, or a capacity for achievement or success. It is hardly something that can be defined and precisely tagged. Moreover, talent is transient because it evolves. Hence, talent analytic data repositories will always be imperfect unless they are continuously updated to reflect changes in human resource capabilities.

Up until I received that email, I admit that I hadn’t heard of talent analytics. And my guess is that many of my academic peers haven’t either, since I found only two academic research papers on the topic when searching using Google Scholar. This needs to be corrected because talent analytics provides academics with the potential for a very productive and long-term research stream where they can assess its development and impact over time. Moreover, the multifaceted nature of talent analytics should be of interest to academics because it is multidisciplinary in nature. What I mean is that anyone interested in human resources, management, organizational behavior, knowledge management, or business intelligence should be able to see the potential importance of talent analytics to business performance and therefore see merit in studying it.

The problem is that, like me, they just need to become aware of talent analytics and the research opportunities that it affords.

  • Richard HerschelRichard Herschel

    Richard is Chair of the Department of Decision & System Sciences at Saint Joseph's University in Philadelphia. Before becoming an educator, he worked at Maryland National Bank, Schering-Plough Corporation, Johnson & Johnson, and Columbia Pictures as a systems analyst. He received his BA in journalism from Ohio Wesleyan University, his Master’s in Administrative Sciences from Johns Hopkins, and his Ph.D. from Indiana University in Management Information Systems. He has earned the Certified Systems Professional designation, and he has written extensively about both knowledge management and business intelligence. Dr. Herschel can be reached at herschel@sju.edu.

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