Is there a simple and straightforward approach to applying human resource analytics to emerging issues? Yes, there is. Two authors have identified a few basic tips that make or break the creation of a meaningful analytics team. First, you must get analysts whose subject matter is human beings. And second, you must use a consulting approach when deciding how to meet business needs.
I would like to endorse the opinions of Alec Levenson and Alexis Fink in their article Winning the HR Analytics Arms Race from April 2017. Their portrayal of what is happening behinds the scenes in human resource analytics very closely matches my own experience of what is really going on.
The article flags that human resource executives are putting top priority on leveraging their data, but they put a low priority on predictive analytics. Just about every other priority area in the top-six are things that could be enhanced by people analytics, like succession planning, workforce planning, and diversity. But notably, executives have determined they are absolutely not ready to enter the ethereal world of predictions. The field is not fully developed and the data set is not yet mature.
The article describes the large number of people who can do clever analytics in different fields, such as engineers, accountants, and rocket scientists. But there’s a problem. Some of these people have never studied employees and their motivations. The authors favor industrial psychologists. My personal experience is that a blend of multiple social sciences is good, as long as everyone has an analytic bent. However, if someone spent the last decade crunching numbers in a field which does not consider the human soul, they’re factually a novice.
The authors also pan the tendency for analysts to try to make something with the data they have available. I ran through this loop in my first four months on the job. The readily-available data is stuff that the payroll and statutory compliance teams needed to get their own work done. But sometimes the most interesting information is stuff that is hard to get. Sometimes you need to create new information from scratch. And often you need to choose a higher standard of data quality.
Levenson and Fink favor a consulting approach, although they don’t call it that. The analyst must meet with the client and figure out what problems keep them up at night. The client won’t name data, they will name a nuanced person-puzzle for which analytics might be finessed into a useful tool. But the solution is several steps in. You have to start with the consulting question. And indeed, this is what business partners do. It is what business analysts do. Face it, if you cared about people, how could you not ask what’s important in the mind of your client?
Which profession should you go to when seeking answers to a numbers puzzle? A true professional advances expertise in the area in which they are knowledgeable. By default this means that you must not advance expertise in an area where others know best. Understanding the boundary between what you know and what you don’t is critical. You make yourself stronger by knowing which profession to seek out. The following list provides examples of professionals who might (or might not) be able to help you, depending on your challenge.
Mathematician. Those who have done proper degrees in mathematics work in abstract mathematics or applied mathematics. Abstract mathematics will be familiar to those who learned concepts in high school that you have never applied since. In my case, trigonometry. Abstract math is required when creating models for applied mathematics, the latter of which solves real-world problems in many fields.
Statistician. These are people who have master’s degrees or doctorates on the applied side of mathematics. They work with large amounts of data solving real-world problems. In my dealings with statisticians, they are all about the statistical model; figuring out whether it works, is compatible with the data set, is compatible with the software they are using… and whether the client’s question has been answered. My impression is that statisticians are far more concerned about happy customers than mathematicians are.
Economist. Economists are in the social sciences and they are cousins to sociologists, psychologists, and a few other fields. Economics grapples with the social problem of finite resources in a context of infinite demand. Economists can work on public policy in areas such as central banking, trade regulation, or in a think-tank. They also work in business using data and models to help the business be more effective. They differ from statisticians in that they match their models to economic theory, not mathematical theory. In public debate in Canada there is a presumption that economic thought is about being politically right-wing; this presumption does not exist in other countries or even within the field itself.
Math Teacher. We need to single-out math teachers because there are a lot of them. They are also the single biggest driver of the public’s ability to deal with numbers. If you did well in high school math you are allowed to say you are good at math. If you say you are bad at math, everyone knows you had an unpleasant encounter with a math teacher who had an off-day.
Business Analyst. According to their professional association these people “…identify and articulate the need for change in how organizations work, and …facilitate that change.” This is great, because it’s problem-solving broadly defined and does not identify their data medium. My experience with Business Analysts is that they’re at the forward edge of re-engineering initiatives, and they function best when they are part of a multi-functional team. They could be accountants but they’re further ahead if they borrow from every business discipline, including process engineering, human resources, information technology, marketing, and finance. They’re the Holmes on Homes of strategy and organizational design. Without the tattoos.
Workforce Analyst. As I mentioned, Business Analysts work best when they borrow from a variety of fields. In human resources, they need business analysts who are able to borrow ideas from every specialization within human resources. This can include recruiting, employment equity, compensation, industrial psychology, health & safety, or industrial relations. Human resources data is immersed in the human element, entwined in statutory regulation, hyper-sensitive to collective agreements and union politics, and is exposed to a unique source of theory and evidence.
Institutional Analyst. This is the field that studies how formal institutions behave according to empirical rules and theoretical rules. There are two Nobel laureates who have influenced this field and the famed sociologist Max Weber influenced it through his work on bureaucracy. Institutional Analysis is at the threshold between sociology and economics. This is a big deal because the two crowds often don’t get along, because of a tweed vs. navy blue dynamic that is completely un-related to the facts at hand.
Actuary. This is a profession that measures and manages risk and uncertainty. A lot of actuaries work on pensions and insurance, because they calculate with some accuracy the likelihood that your house will be robbed, that you will crash your car, or when you will die. Actuaries have degrees in actuarial science, a specialization in mathematics. A lot of them work for consulting firms providing services to the back-office of major corporations. As such, you won’t meet them in your daily working life until you bump into them at a party, at which point they will never talk about the math. It’s like they’re secret agents or something. They calculate danger.
Accountant. This is one of the most long-established number-crunching fields, and makes up a large fraction of people who work with numbers on a daily basis. These people measure and report on financial information that helps others make decisions on investment, taxes, and cost-control. They are typically not boring people.
Financial Adviser. Financial advisers provide financial services to clients in the investment sector. They can help you figure out what insurance to buy, where to invest your savings, how to navigate rules on taxes, and how to interpret research and current events as they relate to your personal finances. Notably, the Wikipedia page on this profession spends two-thirds of its space describing the way the field is regulated. The problem is that they cannot predict the future even though you will ask them to, they sometimes get commissions for investment products they invite you to buy, and there are abundant one-sided horror-stories about poor advice. Yet they are extremely helpful because they can steer you away from obvious mistakes. Just remember: they, like you, are always working for the person who pays their salary.
Demographer. Demographics is the statistical study of populations – their size, distribution, and characteristics such as education and ethnicity. You have probably heard of Thomas Robert Malthus, who described how exponential population growth would guarantee famine and poverty (he was partially correct). Several workforce characteristics can be categorized by demographic traits, which is dicey because often the real driver of differences is the individual people, not their categories. Demographers run your national census, making the field controversial. In the middle ages, Christian thinkers opposed demography, including critics such as William of Conches, Bartholomew of Lucca, and Stephen Harper.
Employers are becoming increasingly frustrated that they can’t find perfect job candidates. And they can’t get perfect information prior to decision-making. Yet there is an abundance of people and information. What’s up?
The Paradox of Choice is a book and a TED talk by Barry Schwartz that describes the downside of having too much choice. Researchers found that consumers presented with more choices in the purchase of jam reduced the likelihood they would buy any jam. The more mutual funds an employee could choose for their pension plan, the lower the rate of participation in the plan itself. In these abundant environments after we make a choice we end up less satisfied with our decisions. It’s too easy to imagine a world where we could have done better. It makes us miserable.
Schwartz recommends that we consider lowering our standards. The concept of “sufficing” is key; that we should make choices that are good enough to meet our needs. If you later discover you could have done better, don’t worry about it.
This attitude is critical to workforce analytics. Trying to get that one quick hit of novel information should be enough for now. Just keep the dream alive that you can make progress every day. Become a little smarter, make a slight improvement, do a fist-pump, and then move on. Lower your standards, cover more ground, and always move forward.
Stuart Murray Speaks 