After reading Using Targeted Analytics to Improve Talent Decisions. Can you help me answer these questions: What are the advantages? What are the disadvantages? What additional factors and information are necessary when making human resources decisions? Be sure and develop your responses fully.

The past decade saw the emergence of analytics as a potential force for driving data$-$based decision making in HR $($Lawler$,$ Levenson, and Boudreau, $2004$; Levenson, $2005).$ At the beginning of the decade, "human resources analytics" was not part of the language of business. Today, at the end of the decade, a Google search for the same term produces more than $1\mathrm{.}5$ million results. When the topic of HR analytics was raised at the Center for Effective Organizations annual sponsors meeting in $2003,$ it was not part of the formal agenda and there were no established courses or seminars on the topic in the HR consulting and training space. Today, an ever$-$expanding array of providers and content work to train and certify practitioners in HR analytics and automate HR analysis.

Yet despite the apparent progress, there still is much uncertainty regarding how best to design, apply and integrate analytics into the daily workings of the HR function. The challenge lies in understanding what analytics to apply where, and the time and resources needed to achieve true insights. This article discusses the variety of analytics and skills that can be used to achieve business insights related to HR and talent. Case study examples illustrate the importance of matching the analytic method to the issue under study.

There is a good$-$news$/$bad$-$news story. The bad news is that the statistical skills needed to do technically sophisticated analysis tend not to be located in HR$,$ and, when they are located in HR$,$ tend to be concentrated in HR analytics centers of excellence. The good news is that the limited availability of advanced statistical skills does not always restrict HR professionals$$ ability to do meaningful analytics. What matters most is knowing what analytics to apply and where to apply them.

It turns out that some of the best examples of analysis$-$driven deep insights come from projects that require a large amount of time, energy and resources to complete but that do not necessarily require the most technically advanced statistical methods. The challenge is in determining how to improve decision making in everyday settings when very involved analytics projects are not feasible.

These three proven frameworks can be applied to make better on$-$the$-$spot decisions, even in situations where there is little time for extensive data collection:

The Capability$-$Opportunity$-$Motivation model for diagnosing work$-$related behavior and productivity, a model that can be used for job design;

A labor markets model of external opportunities and career development, which can be used to analyze the cost$-$benefit of job design, staffing and talent management decisions; and

An organization design model for diagnosing structural barriers to enterprise$-$wide collaboration and performance.

When HR professionals master these models and apply them to everyday decision making, two things happen:

the path to identifying which analytics to apply becomes clear, and,

if there is no time for intensive analytics, the models$$their logic and the empirical evidence behind them$$are effective substitutes that improve the accuracy and impact of talent and organizational decisions

Analytic Competencies in the HR function

Human capital analytics are most powerful when they help tell and validate a story that illustrates the driving forces behind individuals$$ and groups$$ behaviors and performance. As Boudreau and Ramstad $(2006)$ point out, analytics need to be embedded within a logic framework that is linked to the business; and a change process is needed so they are used in a way that ensures maximum impact. The logic framework ensures that the analytics are focused on the right issues and are set up to maximize the discovery of data and analysis results that are actionable. The process for using the results of the analytics ensures the data is turned into action.

The first challenge in applying analytics is in choosing from the wide array of statistical and analytic techniques that are available. Providing an exhaustive list of techniques would be overkill. Instead, Table $1$ lists categories of analytic competencies divided by type and level of complexity. The categories are drawn in part from Rothwell and Sredl$$s $(1992)$ competencies for a Human Resource Development Researcher and in part from my personal experience conducting and training others in human capital analytics and statistical analysis.

Table $1$

HR Analytical Competencies

Analytical competencies related to statistical techniques

Category Examples Level of statistical expertise required $($and approximate educational equivalent$)$

Basic data analysis

Mean

Median

Minimum & maximum