We have talked, in the past, about automation and technology and the opportunities and challenges that it holds for the workplace and for employees. There is no doubt that technology is having a dramatic impact on how we work and the decisions that we make.
One of the big contributors to these decisions is data. These days, due to advancements in computing capabilities, we often hear about “big data” which is exactly as it sounds—massive amounts of data that can be collected, aggregated and broken down into smaller data. Using data to both analyze what has happened, and predict what is likely to happen in the future, offers significant promise for helping business people make decisions about everything from which applicants are likely to be successful in a position, to which employees are most at risk of leaving the organization, and a host of other workforce management questions. Mining the data to find answers involves creating algorithms, or rules, used to make sense of the data, and predictions based on the data.
But, to what extent are we able to trust what the data appears to tell us?
A recent Harvard Business Review article illustrates this point in a very intriguing way. It asks: “Should an algorithm tell you who to promote?” In this piece, presented as a fictionalized case study, a hiring manager faced with two candidates is presented with data, based on an algorithm that analyzed email and meeting history between the candidates and others in the organization, offering the ability to identify who had the broadest and most robust network, as well as performance review data, resumes and other inputs.
The point of tension: the hiring manager is swaying toward one candidate, the algorithm is suggesting the other. Which is the right choice?
Some, perhaps many, would say: “Trust the data! Decisions need to be data driven.” Others might insert some well-reasoned caution, as this fictional hiring manager does, pointing out that the data driving the algorithm’s conclusions are not, necessarily, non-biased—not any more so than her own conclusions. Performance reviews and resumes arguably contain more qualitative than quantitative “facts.” It is a good illustration of the old IT mantra: garbage in, garbage out. And, it is a compelling point in favor of not dismissing the important role that humans will continue to play in making business decisions, despite the increasingly sophisticated technology aids they will have at their disposal.
As I have mentioned in previous posts, in the workplace of the future, people will still matter. Chances are they will matter in more meaningful and nuanced ways. Informed by data, but still called upon to exercise judgment based on experience, stakeholder inputs and both internal and external environmental realities. Data informs. People decide.
How will organizations find the right balance between insight based on big data, artificial intelligence and algorithms and the business intuition and experiences of their employees, consultants, vendors, etc.? To what extent should personal hunches and intuition come into play when making decisions? Is it always a bad idea to follow intuition over hard data? Why or why not?
The Harvard Business Review piece does not give us any answers and does not tell us which candidate this hiring manager ultimately selected. It does ask readers, though, to weigh in with their decision: “Should she trust the algorithm or her instincts?” I am curious to hear your thoughts. Do you think this is a gray area or more clear-cut? I encourage you to read the piece and then weigh in here about which way you would go, and why.
