Unleashing the fact behind the myths of Predictive Analytics
‘Wouldn’t it be nice to know with 100 percent certainty how people — who make both rational and irrational choices — will behave in the future?’
While absolute certainty is never possible, predictive analytics can help organisations look at past workforce behavior to determine what is most likely to happen and plan accordingly. Unfortunately, many individuals mistake this to be a miraculous solution rather than taking advantage of this emerging technology capability if you can sort fact from fiction.
Predictive analytics has turned out to be one that has interpretations and views expressed by different parties across different industries. Given below are commonly identified myths regarding this tool, that circulate around the walls of HR.
Myth #1: Predictive analytics will replace human intervention
Predictive analytics won’t tell you the one clear course of action, particularly when dealing with talent decisions. It is people that need to make people related decisions. The role of analytics is not to replace decision makers with algorithms. One needs to understand that analytics and data represent evidence, not proof, and it is this evidence that can make decisions better.
What predictive models can do is focus, inform and guide managers and HR leaders to better decisions in areas that are critical to the business. If the analytics show that a star performer is at risk of resigning, a manager can use that information to guide decisions and programs that the person would need for career advancement.
Myth #2: The datification of HR starts with predictive analytics
Analytics is a journey. To achieve the value of predictive analytics, some foundational elements must be in place first. Most organisations start the journey with operational reporting, with analytics teams responding to requests from managers and business leaders wanting to identify problems or understand trends. As the analytics maturity level improves, organisations progress from operational reporting to strategic and predictive analysis.
The breadth and quality of data, the ability to support broad and diverse users, and finally the depth of insights that can be produced highly influence the predictive capabilities that represent the levels of maturity.
In order to turn insights into outcomes, one needs to require access to the right data. The most insightful analysis of low quality data will simply lead to the wrong conclusions, and it isn’t possible to perform analysis, when you don’t have the data to begin with. Further, discovering the insights is only half of the work, as those insights need to be turned into a story to inform and engage stakeholders, and then operationalised, which requires the ability to securely share information to executives, HR business partners, line managers and other potential stakeholders on a regular basis.
Myth #3: Predictive analytics is all about predicting the future
The term predictive has become synonymous with a broad set of techniques that can help find patterns and make connections in data, which in return gives a potential prediction about a future outcome. But not everything about the future is really predictive analytics.
Predictive techniques was initially focused in the area of retention. While applying predictive techniques to retention has been a long sought after goal, previous attempts were either too simple or gave answers that were not actionable. To address these shortcomings, trying to unlock the hidden insights that could determine why people were leaving the organisation is more important.
While a prediction can be made regarding turnover, the most valuable insight actually comes from determining the drivers of attrition. For example, promotion wait time versus promotion readiness, how connected people are to the organisation based on the frequency of management change, or more simple aspects such as tenure. This is not technically a prediction, and is usually referenced as a type of data mining, which is all about finding patterns and trends in data. From an understanding of why people are leaving, it is far easier to go after root cause issues and tackle issues like retention holistically. This may have more impact than trying to gain an accurate picture of exactly who will leave the organisation at a future date.
The purpose of predictive analytics, is to overcome the limitations of the human mind. Accurate, predictive algorithms can increase the likelihood that an optimal decision will be made in a timely fashion. However, understanding the limits and benefits of predictive models is key to their effective use.