Prescriptive Analytics Power Leadership Decisions
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Prescriptive Analytics Power Leadership Decisions

Cecile Alper-Leroux, Vice President of HCM Innovation, Ultimate Software
Cecile Alper-Leroux, Vice President of HCM Innovation, Ultimate Software

Cecile Alper-Leroux, Vice President of HCM Innovation, Ultimate Software

Predictive analytics enable companies to analyze key data and gauge the likelihood of an employee’s flight risk. Managers can then take strategic action to prevent this departure.

“Machine learning is a necessary step in this evolution to a more positively perceived HR and more impactful work force”

But, what if managers take the wrong action?

Enter prescriptive analytics. This next stage in machine learning absorbs the knowledge provided by predictive analytics and produces recommended actions to help managers to increase an employee’s engagement and productivity.

It’s not meant to replace human intelligence. Rather, it’s an additional, strategic tool designed to empower managers and improve talent management. Presented with a range of suggested actions, each formed by data specific to the employee’s situation, managers can choose the best plan. Big Data becomes Bigger Data.

Gauging Engagement

How can a machine know such details? It accesses a wealth of information.

Machine learning is a progression beginning with descriptive data—what’s already happened. Predictive analytics digest this information and suggest possible outcomes. Prescriptive analytics assess these outcomes and recommend ways to achieve or avoid them.

Let’s say predictive analytics identify Marjorie as both a high-performing employee and a flight risk. Using an algorithm analyzing dozens of data points relevant to the employee, the technology suggests to Marjorie’s manager a variety of proven (and specific) strategies for helping maintain Marjorie’s vital employment.

For instance, the prescriptive analytics may determine Marjorie is concerned about her career-growth prospects at the firm. It might then suggest the manager present Marjorie with a stretch project—a task beyond her perceived level of skill that serves to “stretch” her developmentally. On the other hand, the analytics may decipher Marjorie feels she hasn’t been adequately recognized for her achievements. It may then advise the manager to provide Marjorie with a financial reward.

In each case, the manager determines which of the suggestions seem best to help ensure Marjorie remains engaged in her work.

Winning the War for Talent

Now, many managers may feel they don’t need another technological tool in order to better perform their jobs. However, countless studies affirm that, the more engaged employees are in their tasks, the less likely they will pack their pens for greener pastures.

While annual employee-engagement surveys are good at defining wide-scale cultural misalignments between the organization and its workforce, they generally fail to address engagement on an individual-employee basis. That’s because every employee is different. Marjorie’s feelings about work may vary greatly from those of her colleagues.

Predictive analytics tackle this problem by mathematically determining which employees are high-performing, and then scoring their respective flight risks. Assuming the risk is high, prescriptive analytics transform this personalized knowledge into a set of different actions that will, hopefully, alter this outcome. It’s not perfect, in some cases; chosen actions may not yield desired results. But, repetition increases the overall odds of success. Each action taken is additional grist for the mill, a finding that informs future actions proposed by the analytics. This continuous improvement process is designed to constantly clarify “best practices,” culminating in improved workforce retention.

It sounds almost too good to be true, yet these opportunities are within reach today. The sooner companies deploy these predictive and prescriptive solutions, the sooner they can leverage essential data to help improve employee performance, productivity, and engagement, while lowering retention risk.

This is great news for senior executives, HR specialists, and business leaders. Rather than just report on what happened in the past, HR can now predict and influence the future, assisting managers to make better workforce decisions aligned with senior leadership strategies.

It’s an enhancement most likely to be welcomed by organizations across industries. After all, it’s no secret HR hasn’t always been viewed favorably.

In a 2015 Deloitte survey, for example, business leaders expressed doubts regarding HR’s ability to perform analytical projects, such as “conducting multi-year workforce planning” and “using HR data to predict workforce performance and improvement.” Harvard Business Review went even farther that year, urging in a cover story, “It’s Time to Blow up HR and Build Something New.” A new-style HR organization is needed, determined a 2014 study by KPMG, suggesting HR acquire two, key skill sets—a head for analytics, and the corresponding ability to comprehensively and compellingly present analytical findings to senior business leaders.

Machine learning is a necessary step in this evolution to a more positively perceived HR and more impactful work force. And it’s a step too crucial to postpone, because of high-performing people are vital to an organization’s overall success. As the competition for the top talent intensifies, it’s the enterprises incorporating such sophisticated HR analytics that ultimately serve to gain the competitive advantage.

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