Talent Analytics and Data Implications in Human Resources

Changes to the global economy following the financial crises of the early 2000s have major implications for Training and Development as economic gains push the world out of a difficult recession. A major focus in Human Resource departments throughout the world is a continued trend towards the utilization of large datasets for recruitment, training, and organizational development. Widespread data collection has recently surfaced as a major scandal in America, but it is evident that such collection and utilization have been a large part of nearly every industry both public and private for years. As technological gains push industries in new directions, successful companies will learn how to utilize these big datasets through the Human Resources application of Talent Analytics. By using Talent Analytics, companies will be able to acquire the skills necessary for globalized competition, and they will be able to engage employees by focusing on their interests, needs, and demands.

Talent Analytics is a Human Resources tool that uses different software approaches to data mining in order to match high functioning employees with company objectives. The driving force behind the increased popularity of Talent Analytics is the need for companies to get more out of the talent they hire. The most common way to do that is to engage employees in a manner that will increase productivity across the board. In a look at the state of Talent Analytics in business, Davenport, Harris, and Shapiro (2010) identified six facets of Talent Analytics that will emerge as future Human Resource tools. They include understanding and quantifying Human Capital, increasing the analytical function of a Human Resources department, analysis of a company’s human-capital investment, focusing workforce forecasts, implementing a talent value model, an creating a talent supply chain. These six tools can be implemented to focus Training and Development applications on Talent Analytics and the efficient use of large employee data sets.

The understanding of Human Capital has been debated in detail, and Human Resources departments are making use of the quantification of such Human Capital in order to better match employee skills with company objectives. Examples of human capital include total employee volume, labor use, recruiting practices, employee personal interests, employee academic standards, and turnover rates. Josh Bersin with the Deloitte Consulting LLP predicts that the definitions of current human capital points of interest will become more globally oriented soon. As the economy recovers, gaps in human capital and the rapid technological and regulatory changes taking place across industries will require a deeper focus on specialization within different business sectors. Bersin believes the talent pool for those skills is no longer as local as it once was, stating “in 2014, we can and should source and recruit talent where we think skills will be deepest and where we can best compete for people” (Bersin, 2013, p. 14). The quantification of human capital will go a long way to an effective analytical Human Resources strategy.

While the focus of many Human Resources Departments regarding targeting and retaining skilled and talented employees has made use of large data sets, the process has not been as organized as it needs to be. The collection of Human Resources data to gain insights into certain parts of a company must be met with a collaborative approach to analyzing such data. Davenport et al. (2010) cite Lockheed Martin’s performance management system that associates individual employee performance to organizational objectives. By doing this they can integrate human capital in the company’s subjective goals by linking objective outcome metrics about individual employees. Such an efficient matching of employee production to company standards goes a long way in evaluating a company’s return on investment when they hire an employee.

The analysis of the human capital investment is a product of quantifying human capital, and a Human Resources strategy that will bolster Talent Analytics approaches. The major issue in the Human Resources Training and Development process, in companies across the globe, is that the highest performing employees are looking beyond compensation and material benefits when considering career choices. Today they are considering the future growth of the company, personal recognition, future career growth potential, and personal educational opportunities. Compounding this issue, according to Bersin (2013), is that “the economy has improved, healthcare reform will take hold, and people have been working too hard; they are starting to look around” (p. 28). Focusing on the human capital strengths and weaknesses of an individual employee, and improving the analytical approach to utilizing such data, has implications for both employee and employer.

The Talent Value Model of Talent Analytics could interpret human capital strengths and weaknesses. Davenport et al. (2010) use Google’s Talent Value Model to show how such an approach can aid the designing of incentive programs, the understanding of how to address competitor recruitment offers, and the decision-making process of when and how to promote certain employees. Google’s Talent Value Model doesn’t look at average-level employee performance; instead, they focus on the two extreme ends of whatever human capital or performance-based spectrum they are considering. By doing this they can identify why their lowest 5% of employees are failing to meet company standards. If a great deal of Human Resources energy goes into hiring an employee, Talent Value Models can help in determining why their perceived value at hire has not yet translated into true performance value.

The creation of a Talent Supply Chain is another facet of Talent Analytics that allows companies to make real-time decisions with specific employee skills and production measures in mind. Talent Supply Chains are difficult to construct, but their strength lies in their quick application for company management. While they require high-quality datasets, rigorous analysis, and the integration of a talent or skill-based management process, the changing platform of Human Resources Training and Development will make it easier to do so. According to Bersin (2013), “the Human Resources and talent management software market, which is no more than $6 billion in size, will continue to grow…the theme for 2014 technology is integration, ease of use, and data” (p. 47). Keeping up with everyone else, in terms of technology and personnel capabilities, will remain a very common theme in business.

The race to maintain, and effectively utilize, large data sets will require significant effort on the part of Human Resources Departments, but the focus doesn’t necessarily need to be on staying far ahead of the curve. Instead, it should focus on being more efficient with the use of large data sets as Talent Analytics software enables Human Resources personnel to conduct better recruitment, engagement, and employee development. In their report on projected needs in Human Resources and Talent Analytics, the Chartered Institute of Personnel and Development detailed the barriers between cross-organizational utilization of employee and human capital data. These “structural silos” are difficult because such complicated data sets make collaboration a difficult achievement (“Talent Analytics and Big Data”, 2013). Compounding this issue is the increased concern about data security and the capabilities of individual Information Technology and Database Management employees.

The volume, the velocity, and the variety of large data sets will be major roadblocks in the implementation of Talent Analytics. The volume of data is readily available, easily attainable, and constantly expanding. According to McAfee and Brynjolfsson (2012), “as of 2012, about 2.5 exabytes of data are created each day, and that number is doubling every 40 months or so” (p. 1). The attainment of data is also becoming easier across the board, with changes constantly surfacing, and spreading to every competitor, alternative market, and geographic region. Davenport et al. (2010) identified this issue of the speed with which data is shared, collected, and transported by highlighting Netflix and Best Buy’s approach to unconventional scheduling. Such scheduling allows for a high level of employee engagement across numerous departments worldwide. While the volume and velocity of data is increasing, so too is the variety of the data collected. Quantifiable standards of human capital will organize this issue, but companies must also stay at the forefront of the methods and tools by which data is collected and accessed. McAfee et al. (2012) provide an interesting example of their colleague Alex Pentland and his efforts at using cell phone meta-data to see how many people were in Macy’s parking lots on Black Friday. The use of cell phone searches and text data allowed Pentland to “estimate the retailer’s sales on that critical day even before Macy’s itself had recorded those sales” (McAfee et al., 2012). Efficient data management and the utilization of different data platforms can also be applied to Human Resources Departments and new approaches to Talent Analytics.

From Wall Street in New York to the aboriginal territories of New Guinea, the ability to collect large swaths of personal data is ubiquitous. While pools of data are stored, successful companies will be those who utilize large data sets in a focused and objective manner. By doing so, Human Resource Departments can make use of Talent Analytics to identify better recruitment, training, and employee development practices. By identifying human capital through these datasets, skills and talents will meet company objectives quicker, easier, and more efficiently.

References

Bersin, J. (2013). Predictions for 2014. London: Deloitte Consulting LLP.

Davenport, T. H., Harris, J., & Shapiro, J. (2010). Competing on talent analytics. Harvard Business Review, October 2010, 1-6.

McAfee, A., & Brynjolfsson, E. (2012, October 1). Big data: The management revolution. Harvard Business Review, October 2012. Retrieved from http://hbr.org/2012/10/big-data-the-management-revolution/ar

Talent analytics and big data: The challenge for human resources. (2013, November 1). London: Chartered Institute of Personnel and Development. Retrieved from http://www.cipd.co.uk/hr-resources/research/talent-analytics-big-data.aspx