Predictive Analytics: What HR Teams Need to Know

Picture this: It is your job to keep an eye on employee retention at your company. Thanks to workforce data and artificial intelligence, you have tools that can help flag who might be a flight risk. And these tools give you a heads-up: For example, you could be at risk of losing a group of high-performing women who are poised to take on leadership roles. 

Thanks to predictive analytics, you have an opportunity to take steps to retain those high performers. Predictive analytics can also alert you if, for example, you do not have enough employees with the right skills for next year’s big initiative. And it can be used to promote fairness: Before you make a big offer to a job candidate, analytics can tell you if it will throw off your payscale, perhaps by making employees of colour underpaid in comparison.  

“You can ask, are we building glass ceilings in our company?’ ‘Is there a ceiling where we just do not see the same level of representation that we see in other levels?’ And you can identify that with predictive analytics,” says Trey Causey, head of AI ethics and senior director of data science at Indeed. “In fact, I would be hard-pressed to think about how we would identify that happening without using these tools. This is a way to be able to approach these questions quantitatively.” 

The proliferation of general analytics tools is not new to HR; they started gaining ground sometime in the second half of the last century. Today, the majority of large companies rely on analytics to look at data on past performance and inform decisions around hiring, firing and promotions. 

However, the rise of big data and artificial intelligence (AI) means that HR no longer has to rely on lagging indicators. Predictive analytics, coupled with new and innovative AI-powered technologies, can look ahead. How would it impact people if you make every Wednesday a meeting-free day? When should you consider offering an employee a bonus to keep them engaged? 

Although many of the early adopters of novel tools are big tech or financial services companies, uptake is increasing across industries. Appriffy, a Bengaluru-based technology business, is a notable example of how AI is being used to disrupt the employment practice. Appriffy employs artificial intelligence to understand data and approve candidates for employment. According to Sandeep Chaudhary, CEO of PeopleStrong, “Over the course of the next decade, Artificial Intelligence, especially Generative-AI will redefine the role, structure and engagement of HR. PeopleStrong's integration of Generative AI is more than just a technological advancement; it's a strategic move towards a future where HR will transform into an experience led function. It is a future where technology and human insight merge to create a more responsive, intelligent, and human-centric approach to work. We are excited to be at the forefront of this innovation, enabling our 20 lakh+ users to leverage this cutting edge technology to transform their HR and script their growth story.”

HR professionals may make informed decisions by using AI-based HRMS solutions, which analyse large volumes of data and offer insightful suggestions. In other words, these structures search for top talent for open positions by analysing employee data, identifying attributes and predicting turnover. The application of AI to HRMS is revolutionising HR procedures in India.

To use these tools to their potential, HR teams should strive to be proactive, informed and responsible with data, so that the technology helps employees instead of undermining them. 

Follow these six best practices to make sure your company is implementing predictive analytics the right way.

Know What You are Solving For

According to Vineet Pandita, CTO of PeopleStrong "Integrating Generative AI into our product emerged as a logical advancement in our relentless quest to redefine HR technology, we are steadfast in our belief that this groundbreaking technology will not only lift HR practices to new heights of efficiency but also play a pivotal role in sculpting the future of work. It is a step towards a more intuitive and intelligent HR landscape."

Talk to Your Legal Team

It is also important to consult your legal team. They can evaluate whether the tools you are using are sharing data in line with company rules, as well as privacy and anti-discrimination laws. Raw data being shared with groups of people who do not require all the qualities on a report may pose a legal risk.

In India, the security of personal information and privacy is mandated by the Information Technology Act, 2000 (the "IT Act"). HR professionals are responsible for ensuring that the AI system utilised for HR operations adheres to these laws. The organisations also need to make sure that no personal information is collected or processed by the AI system used for HR tasks without the express permission of the employees. The AI system's data processing operations also have to adhere to the guidelines of purpose limitation, data minimisation and storage limitation.

Lean Into the Science

Not all predictive analytics products will align with your company’s values and business ethics. It is essential to assess employees based on bona fide job requirements and data related to their behaviour and performance. Do not base decisions on personality assessments that predict employee loyalty by considering whether someone identifies as a cat or dog person, or is from a small town.  

“There are a lot of vendors selling pseudoscience dressed up as predictive analytics,” Causey says. “Do not ever be afraid to call in a second opinion, somebody who has expertise in the area.” If your people operations team does not have that capability, look for services and consultants that evaluate HR tools in an unbiased way.

Keep in mind that you want tools that evaluate large datasets for trends. Try to avoid using granular data, such as one employee’s performance in a single quarter — particularly when there may be underlying circumstances the analytics tool knows nothing about. 

“It is almost like the stock market — you do not want to day-trade,” Causey says. “You do not want to overreact to small blips in metrics. You want to look at what the long-term trend is and make sure that you are also using context as part of your decisions.”

Take the Initiative on DEIB

Predictive analytics offers the opportunity for data-driven evaluation of practices on diversity, equity, inclusion and belonging (DEIB), which can otherwise be hard to quantify. With responsible privacy protection in place, you can take a data-based approach to help analyse decisions on salaries and raises, training opportunities and promotions. The goal is to ensure that compensation and development opportunities are aligned with performance and skills — and not driven by a hiring professional’s gut instincts, which can be affected by unconscious bias. 

Analysing employee data can also help HR teams ensure that opportunities are offered consistently across demographic groups. 

This type of data can make it easier to make a compelling case for DEIB at a time when some business leaders are discounting or dialing back such initiatives. “If you can use the data to demonstrate that X, Y or Z is happening, rather than just arguing from principle, you are much more likely to be a change agent than if you’re making an impassioned plea because it is the right thing to do,” Causey says. 

Check for Bias

There is always a chance that bias will creep in because AI learns from the data sets that are fed into the system. These data sets' biases will probably continue to support the lack of diversity in workplaces across the world.

If you have in-house tools, make sure to perform bias checks. If you are buying from an outside vendor, ask how they have evaluated their products for bias. 

Do not forget that data is just one input in the decision-making process, Causey says. If you do not agree with the machine’s output, you do not have to follow it unquestioningly. If predictive analytics sets recruiting targets too high or recommends timelines that are too short, adjust them.

Avoid Being Intrusive

When leveraged in a benevolent way, predictive analytics can level playing fields, implement better hiring practices and make employees feel valued. But taken too far and without regard for privacy, predictive analytics can be downright intrusive. What if such tools are monitoring social media updates as data inputs for whether an employee is a flight risk? Or tracking people’s movement around the office to see who they talk to in a day and how communicative they are? Or putting “bossware” on remote workers’ computers to score their productivity? 

Causey stress that it is imperative to be transparent with employees about what data you are gathering and why. No one wants to feel like they are being spied on. 

“Think of it like the golden rule: How would you feel if you were being evaluated this way?” Causey says. “We certainly do not want to evaluate employees in ways we would not like to be evaluated.”