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1.2. Rise of the Machines: Advanced Analytics and Decision Making

According to Gartner, Inc., the term advanced analytics is defined as follows:30

  • As analysis of structured and content (such as text, images, video, voice) data using sophisticated quantitative methods (such as statistics, descriptive and predictive data mining, simulation, and optimization) to produce insights that traditional approaches to BI such as query and reporting are unlikely to discover. It is frequently applied to make decisions, solve business problems and identify opportunities by providing better forecasts, causal understanding, pattern identification, process and resource optimization, and assisting with scenario planning process.

The challenge is that although substantial gains wait, very few firms actually utilize advanced analytics. Only 13% of organizations utilize predictive analytics, and only 3% use prescriptive analytics, such as optimization and simulation.31 To this list, I want to add actionable recommendations, such as provided by machine learning and expert systems.

Recently, the focus on HCM metrics has gone a long way toward establishing the relationships between variables of interest (for example, training initiatives) and performance outcomes (for example, employee turnover by division).32 Advanced analytics provides a deepening of the tools associated with business intelligence, with a focus on predicting and prescribing the optimal course of action. These techniques are increasingly being used in functions like operations, finance, and marketing and can have the same impact within human resources.

  • According to Gartner, this will matter.33
  • Pervasive, advanced analytics will become necessary for leading organizations that want to gain competitive advantage.
  • The explosion of data volume, and its variety and velocity, will enable new, high-value advanced analytic insights and use cases.
  • Lack of skills will be a critical inhibitor to adoption and deriving value from advanced analytics.
  • Embedding collaboration and social capabilities in advanced analytic applications will facilitate higher quality and more transparent decision making.

There is an ever-increasing need for data scientists—those who understand statistics, computer science, and data modeling and analysis. More effective HR decisions can be made when these skills are used to assist with the full spectrum of HR tools.

1.2.1. Advanced Analytics

As mentioned previously, we can improve our decision making. Metrics and analytics have long been used to assist decision making, and as computing power increases (along with our understanding of behavior), our tools are becoming more powerful as we develop models that more accurately predict outcomes.

Figure 1.1 provides an overview of a hierarchy of analytics. Level I is an organization’s use of basic metrics to obtain information such as headcount, employee turnover, and even some simple statistics such as the use of means and averages. Next is Level II, which is characterized by correlations. This consists of determining whether and when variables move relative to one another. For example, as employee morale goes up, what happens to employee turnover? Of course, correlations do not mean causation; however, they do suggest a possible relationship. Level III shows a focus on establishing causation and on predictions of what will happen next (anything from who will make a good employee to whether a specific payment package will promote the intended organizational outcomes).

Figure 1.1

Figure 1.1 Hierarchy of analytics

Advanced analytics can aid in establishing causation, which is generally thought of as the holy grail of analytics. That is, does the intervention we put in place have a direct impact on the bottom line? For instance, does the new compensation approach increase employee productivity, reduce employee turnover, and ultimately impact sales and profitability? This can then be used not only to justify expenditures but also to make determinations about what policy, practice, or intervention is advantageous to use in the future.

Advanced analytics can be thought of in two parts. Part one attempts to predict what will occur. As discussed in the previous section, this requires a broad understanding of how individuals and groups will react. Part two, and the primary focus of this book, is about optimization. The focus here is not about what a decision will be, but rather what it should be.

It is a good thing that these tools are becoming more available, because according to a 2010 survey by IBM, there is a real need for HCM decisions to move toward higher levels of prediction and causation.34 That survey found that advanced analytics were rarely used for activities such as evaluating workforce performance, retaining valued talent, and developing future leaders. Nowhere, on any of these HR issues reviewed, did more than a quarter of the organizations actually engage in advanced analytics. One of the least used analytical processes is the use of collaboration across the organization. Only 5% of the firms interviewed used advanced analytics along with collaboration and knowledge sharing.

HR has nothing to feel bad about. It is estimated that only 3% of firms use any form of advanced analytics. However, it is projected that the use of analytics will grow substantially over the coming years.35 This book covers each of these three perspectives of decision making:36

  • Descriptive: What happened and what is happening?
  • Predictive: What will happen? What might happen?
  • Prescriptive: What should happen? What is the best course of action?

1.2.2. Predicting Outcomes

Recently, the Sundem-Tierney equation has been updated. You may be wondering what exactly the Sundem-Tierney equation is used for. Basically, it predicts how long the marriages of celebrities will last. As one of the authors proclaims, tongue in cheek, “One of great unsolved mysteries in social science.”37

  • The Sundem-Tierney Celebrity Marriage Longevity Equation

    01eq01.jpg

    Where:

  • NYT = The number of times the wife’s name been mentioned in the New York Times
  • ENQ = The number of times the wife’s name has been mentioned in the National Enquirer
  • Ah = Age in years of the husband
  • Aw = Age in years of the wife
  • Md = Number of months the couple dated before marriage
  • Sc = Number of scantily clad photos from the top five photos found during a Google image search of her name
  • T = Time in years for which you want to calculate the percentage chance the couple will still be married

This equation represents a revision of the old equation, and it turns out that this one is a much more robust predictor of the duration of celebrity marriages. For example, the equation accurately predicts that Jennifer Lopez’s marriage to Ojani Noa (her first husband, a relationship that most people don’t even know about) would not last very long (it lasted 13 months), but it predicts a 71% chance that Prince William and Kate Middleton will make it 15 years or longer.

Many become nervous when they hear things like “model building” or “optimization,” but this does not need to be so intimidating. There is nothing intimidating about listing the factors that go into making the best decision. Getting all the best and required data might not always be especially easy, but determining the determinants (the factors influencing an outcome) can actually be rather fun and interesting. Take, for example, the following equation; it is attempting to determine the likelihood of marital bliss. By Robyn Dawes, this model predicts the likelihood of the survival of a marriage.38

  • Frequency of Lovemaking – Frequency of Quarrels

See, nothing at all boring about predictive modeling. As you might imagine, having a negative number associated with this equation is not a good thing. Because of the availability of the necessary data, predictions such as these are becoming more and more common and found across many facets of life. Predicting compatibility is the task organizations such as Match.com and eHarmony attempt to do. Dawes formula is a simple one that essentially attempts to serve the same function as the ones developed by these dating services. They are both attempting to identify a list of factors that will predict the success of relationships. In the case of eHarmony and Match.com, this also consists of information on emotional, cognitive, and social attributes, physical activity, personality characteristics, education, geography, and so on.

One more:

  • Runs Created = (Hits + Walks) × Total Bases / (At Bats + Walks)

Some of you might recognize this formula. William James, the founder of sabermetrics, developed it. If this is not familiar to you, maybe you remember the book Moneyball, by Michael Lewis, or the movie by the same name starring Brad Pitt. James’s sabermetrics is the underlying approach used to predict success at getting on base, and this is exactly what the formula predicts: a hitter’s ability to get on base. It did not worry about exactly how he got there. As a matter of fact, the formula takes into consideration those who walk as well as those who get hits.39

Making predictions is something that we do all the time. Will a stock price go up or down? Will your friends get married? Will this person make a good employee or a good executive? What kind of professional experiences will assist them in becoming better employees?

Within the broad area of decision support systems, a variety of different models are used to aid in decision making.40 The relevant variables when “modeling” HCM decisions include all those factors that influence the outcome you are interested in. For example, what might be some of the causes of employee turnover? This decision will be influenced by, among other things, a number of the following factors:

  • Employee morale and satisfaction
  • Labor market conditions
  • Relationship with direct reports

Another example is workforce planning, which seeks to accurately forecast future employment needs. Again, a number of factors may influence the best decision about the type and number of employees needed, including the following:

  • Business strategy and objectives
  • Current workforce quantity and competencies
  • Required workforce quantity and competencies

In later chapters, we will evaluate factors influencing the ideal job candidate for your situation and the optimal compensation structure. Determining these factors is where expert knowledge and experience comes in, and when these are combined with the right analytics, you are on your way to making much better decisions.

1.2.3. Improper Linear Models: Combining Expert Intuition with Analytics

The work of Robyn Dawes provides an excellent justification and argument for the use of expert expertise combined with the use of advanced analytics. Analytics can be used to develop a comprehensive list of factors that ultimately promote performance, or make a good employee, or any number of different decisions, and the experts can use their expertise to develop the weightings for the various factors.

In his article “The Robust Beauty of Improper Linear Models in Decision Making,” Dawes remarkably concluded that a simple algorithm is accurate enough to compete with regression analysis and, frankly, much better than the opinion of an expert. Consider, for example, the Apgar test. In 1953, Dr. Virginia Apgar, an anesthesiologist, was asked how she would assess the health of a newborn. She wrote down five variables (respiration, reflex, muscle tone, color, and heart rate) and assigned a score of 0, 1, or 2 depending on the strength of the variable. A baby with a score of 4 or less needed immediate attention, and a baby with a score of 8 or more was pink, crying, and good to go. This simple algorithm has certainly saved the lives of thousands of babies over the years.41

Yes, this is a simple algorithm, but identifying which variables are important is not so simple. Picking the important variable that predicted newborn health was done by someone who had very deep practical experience and research. Dr. Virginia Apgar was born in 1909 in Westfield, New Jersey, and was educated at Mount Holyoke College and Columbia University College of Physicians and Surgeons (CUCPS), where she graduated in 1933 and finished her residency in 1937. She went on to become the first woman to become a full professor at CUCPS, in 1949. Dr. Apgar had 20 years of experience around newborns when she developed her test. She had considerable expert knowledge through observation, study, experience, research, and practical experience to establish those five variables. Could there be other (better) ones? Maybe. Could, perhaps, respiration be the most important and color the least at predicting the well-being of the newborn? These are exactly the types of questions that deep analytics can answer.

This approach is further supported by Stephen Hoch in the summary of his chapter, “Combing Models with Intuition to Improve Decisions”:42

  • Most decisions have three stages: (1) variable identification, (2) variable valuation, and (3) information integration into an overall evaluation. Experts are good at the first two stages but are plagued by inconsistency in stage three. By outsourcing stage three to a mechanical model, the quality of decisions can be enhanced. By carefully combining human experts, statistical models, and new data-mining tools, we can improve the quality of forecasts and other decisions.43

We’ll be using this exact approach when modeling our decisions: an expert determining the importance of factors coupled with analytics.

1.2.4. Artificial Intelligence and Machine Learning

What exactly is meant by the term artificial intelligence (AI) garners a significant amount of discussion. Machine learning and expert systems are both forms of AI. There is also natural language and the neural nets and other AI tools. As the name suggests, natural language refers to the capability of machines to understand and act on spoken language. Neural nets are computer systems that mimic the human brain. For our purposes, I will focus on machine learning and sophisticated expert systems (sometime referred to as Deep Q&A expert systems). Both have substantial scope for assisting with the decision making within HCM and elsewhere.

According to Yaser S. Abu-Mostafa, a Professor of electrical engineering and computer science at Cal Tech and the co-author of the book Learning from Data, at its most basic, machine learning can be defined as follows:

  • At its simplest, machine learning algorithms take an existing data set, comb through it for patterns, then use these patterns to generate predictions about the future.44

Machine learning has been utilized within a number of different functions, including finance, marketing, and operations (and in HR, but less so). It is generally associated with the ability, as the name implies, to learn (mostly through trial and error). An example is in gaming settings, where the system can learn by playing the game over and over. This is one of the reasons that machine learning can be used effectively for chess or Jeopardy!; they are games that are repeated. Within HR, there is also repetition; we hire computer programmers again and again, we design and deliver compensation repeatedly, and we put our high-potential employees through executive development programs. All of these activities can be refined through utilizing machine learning.

The following list describes a few instances of when machine learning can be applied to HR decisions:

  • Identify professional experience, educational attainment, personal characteristics, and other life experiences associated with superior job performance
  • Use social media to obtain information on the success of a specific recruitment approach
  • Identify factors associated with voluntary turnover of high-potential candidates
  • Predict future workforce skills and quantity

The applications of machine learning are many, but there are also potential drawbacks. Machine learning relies primarily on the use of an algorithm as it trolls through a dataset looking for instance the “ideal” candidate or the ideal pay package. Again, according to Abu-Mostafa,45 it is not always easy to actually name or identify the attributes that have been identified. In addition, many decisions associated with HCM may need to be explicitly defined or backed out of. An employee (or potentially the courts) may question how a specific decision was arrived at. This might not always be easy to determine when using machine learning. Machine learning tends to use algorithms to do the work. Algorithms are a predetermined set of factors that need to be evaluated to arrive at some required output. An example is calculating payroll; this takes into consideration hours worked, overtime, tax, and other deductions.

Whereas machine learning focuses on the use of algorithms, expert systems utilize heuristic approaches. Heuristic approaches generally follow a set of rules to arrive at some conclusion or recommendation. Expert systems make it possible to see how a decision was made.

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