This chapter responds to the growing importance of business analytics on "big data" in managerial decision-making, by providing a comprehensive primer on analyzing compensation data. All aspects of compensation analytics are covered, starting with data acquisition, types of data, and formulation of a business question that can be informed by data analysis. A detailed, hands-on treatment of data cleaning is provided, equipping readers to prepare data for analysis by detecting and fixing data problems. Descriptive statistics are reviewed, and their utility in data cleaning explicated. Graphical methods are used in examples to detect and trim outliers. The basics of linear regression analysis are covered, with an emphasis on application and interpreting results in the context of the business question(s) posed. One section covers the question of whether or not the pay measure (as a dependent variable) should be transformed via a logarithm, and the implications of that choice for interpreting the results are explained. Precision of regression estimates is covered via an intuitive, non-technical treatment of standard errors. An appendix covers nonlinear relationships among variables.

“Just as trees are the raw material from which paper is produced, so too, can data be viewed as the raw material from which information is obtained.” To present and interpret information, one must start with a process of gathering and sorting data. And for any kind of data analysis, one must first identify the right kinds of information sources.

previous chapter, we discussed different forms of data. The height–weight data we saw was numerical and structured. When you post a picture using your smartphone, that is an example of multimedia data. The datasets mentioned in the section on public policy are government or open data collections.

This chapter treats pay in nonprofits and the public sector, where the organization’s objectives are not as straightforward as in the typical for-profit firm. It also covers small businesses, a subject which is neglected in standard compensation texts but which is important because some readers are or aspire to be small-business managers. The opening section defines the 3 entities under discussion. Organizational missions and workers’ intrinsic motivation are described, which relates to compensating differentials in that workers who value the organizational mission interpret it as a non-monetary component of pay that creates an incentive to work hard to further the mission. The chapter revisits external and internal constraints on pay, training (and recruitment of desired worker types), performance pay, and turnover, thereby tying the book’s earlier concepts together. Subjects that were covered in earlier chapters are re-examined through the different lenses of nonprofits, the public sector, and small businesses. The chapter ends with coverage of “distance” between managers and owners, which tends to be shorter in small businesses than in larger ones, and its implications for pay.

This chapter introduces some terminology and themes that pervade the book. Compensation is defined broadly to include everything a worker likes about the job. “Strategic compensation” is about managing the compensation system to advance a specific organizational objective, typically profit maximization. The chapter discusses how this relates to talent management, turnover, retention, and employee productivity. Four recurring themes are introduced: (1) “Incentive effects” and “sorting effects (both of which affect the company’s labor productivity) arise when the compensation system is changed; (2) Market competition largely dictates pay levels, whereas employers have more control over pay design; (3) Competition forces employers to care about their employees’ preferences about pay; (4) Bargaining power also affects pay levels. The metaphor of a “3-legged stool” is introduced, in which compensation depends on workers’ desires, skills, and mobility. There’s discussion of what constitutes “fair” pay and the tradeoffs associated with allowing employees to know each other’s pay versus keeping compensation secret. The appendix offers a detailed treatment of nominal versus real compensation.

This chapter on fringe benefits draws on the theoretical support structure of compensating differentials (Chapter 3), given that workers value fringe benefits (i.e., non-monetary components of pay) and are therefore willing to accept lower monetary pay than they would receive in alternative jobs that do not offer those benefits but that are otherwise identical. The chapter opens with a discussion of workers’ valuations of various fringe benefits and how those valuations may differ from the employers’ costs of providing those benefits. From a managerial standpoint, the main problem with using benefits to compensate workers is inefficiency, in that workers often value those benefits at less than their cash equivalents. Against that disadvantage are a number of advantages of paying workers in benefits, and the chapter covers the main ones. Cafeteria plans mitigate the main disadvantage of benefits compensation while simultaneously weakening some of the advantages. The chapter ends with a lengthy section on pensions that provides a detailed distinction between defined-contribution and defined-benefit plans and the implications for worker behavior (e.g., retirement ages).

While a versatile programming language such as Python can provide a framework to work with data and logic effectively, often we want to stay focused on data analysis. In other words, we could use a programming environment that is designed for handling data and is not concerned with programming so much. There are several such environments or packages available – SPSS, Stata, and Matlab. But nothing can beat R for a free, open-source, and yet a very powerful data analytics platform.

And just because R is free, do not think even for a second that it is somehow inferior. R can do it all – from simple math manipulations to advanced visualization. In fact, R has become one of the most-used tools in data science and not just because of its price.