Dissecting the Data: The Use of Analytics in Performance Forecasting

Dissecting the Data: The Use of Analytics in Performance Forecasting

Analytics in the HR sphere is not a novel concept, but the sophistication and predictive power of these tools are improving continuously. Employee performance is one area where these advancements are particularly noticeable. Using data from past job performance, skills assessments, training, and even social behavior, predictive models can forecast an employee's future performance with striking accuracy. According to a study published in the Journal of Applied Psychology, these predictive models can increase the precision of performance forecasts by up to 30%.

IBM serves as a compelling example of this trend in action. Leveraging the power of AI and analytics, IBM has developed an internal system that predicts performance and career progression. According to Diane Gherson, IBM's former chief human resources officer, "With AI, we can predict with 95% accuracy what employees will do next —whether they are planning to leave or how they will perform in their next role." But there are considerations beyond the technology's capability to predict. What about the so-called "human factor" that isn’t quantifiable? For instance, the confidence, resilience, or innovative thinking an employee might bring to a role may not always be captured by algorithms.

Decoding Behavior: Unraveling the Human Factor through Analytics

As powerful as HR analytics tools may be, understanding human behavior is an intricate task. It's not just about tracking performance metrics; it's also about understanding employee motivations, job satisfaction levels, interpersonal relationships, and more. But how exactly do analytics tools approach this complex task? Firstly, behavior analytics often begin by collecting data from a variety of sources. These might include performance reviews, feedback surveys, and even internal communication channels. Certain companies like Humanyze go a step further, using wearable technology to collect data on employee interactions and collaboration patterns.

Once collected, this data is processed and analyzed using advanced AI and machine learning algorithms. The goal is to identify patterns, correlations, and trends. For example, the analytics tool may uncover a correlation between an employee's job satisfaction and their level of interaction with colleagues, or it might identify patterns in an employee's behavior leading up to their resignation.

This analysis then forms the basis for predictive models. By learning from past data, these models aim to predict future behavior. For instance, if an employee exhibits similar behavior patterns to those who have previously resigned, the analytics tool may flag this employee as a potential flight risk.

Retention Revolution: Predicting Employee Turnover

Predictive analytics also show immense promise in forecasting employee retention. By analyzing patterns in employee behavior, feedback, and job satisfaction levels, these tools can alert HR professionals to individuals who might be considering leaving. This enables the company to proactively address potential issues and retain valuable talent. A study by the Society for Human Resource Management (SHRM) highlights this. They found that organizations using predictive analytics for retention were able to reduce employee attrition by an average of 14%.

One of the pioneers in this space is Xerox. The company implemented a predictive analytics program to identify employees at high risk of leaving, allowing them to intervene early. "The program has saved us millions by reducing attrition," says Xerox's Director of Talent Management, Kevin Mulcahy. However, while the benefits are clear, it's important to consider the implications on employees' trust. If individuals feel their actions are constantly monitored and analyzed, this could potentially create a culture of distrust or fear. Balancing the benefits of data insights with respect for employee privacy is a fine line to tread.

The Tech Titans of HR Analytics

Several tech companies are capitalizing on the growing demand for HR analytics. For instance, Visier provides cloud-based, end-to-end solutions that allow HR professionals to integrate, visualize, and analyze employee data. Their platform can predict turnover risk, identify top performers, and provide insights into workforce diversity.

Another industry leader is Workday, which offers an AI-powered analytics tool that provides actionable insights into employee performance and attrition risk. According to Aon's annual HR Tech report, Workday's platform is used by over 60% of Fortune 500 companies. Companies such as PeopleSoft and SAP SuccessFactors also offer robust HR analytics tools, each with its unique strengths. However, as these tools become more widespread and powerful, the challenge for HR professionals is to ensure they're used ethically and effectively.

Scaling Down: The Role of Analytics for Small Businesses

While large corporations have been quick to embrace HR analytics, it might seem like a daunting prospect for smaller businesses. However, the potential benefits — from improved hiring decisions to increased employee retention — can far outweigh the costs. So how can small businesses approach this?

Firstly, implementing HR analytics doesn't necessarily require a massive upfront investment. Many service providers offer scalable solutions suitable for businesses of all sizes. Platforms like Zoho People and BambooHR offer affordable HR analytics solutions specifically designed for small businesses.

Using these tools, small businesses can track and analyze key metrics such as employee turnover, performance, and engagement levels. This data can provide valuable insights and support informed decision-making. For instance, by identifying the characteristics of high-performing employees, a small business can refine their hiring criteria to attract similar talent.

Of course, implementing analytics does come with its challenges. One is the risk of data breaches. Small businesses must ensure they have robust data security measures in place to protect sensitive employee information. Another potential pitfall is data privacy concerns. It's essential to be transparent with employees about what data is being collected and how it's used.

Finally, small businesses must also be wary of over-reliance on analytics. As we've discussed earlier, data can inform decisions but it shouldn't dictate them. Especially in a small business setting, where personal relationships and individual understanding are often strong, the human touch should always complement data-driven insights.

Conclusion: Navigating the New Norm

As we move forward, HR analytics will continue to play an increasingly vital role in shaping workforce strategies. Predicting employee performance and retention is only the tip of the iceberg. With the rise of sophisticated AI and machine learning algorithms, the possibilities seem almost endless. However, we must remember that while data is powerful, it's not infallible. As Josh Bersin, an industry analyst and founder of Bersin by Deloitte, rightly points out, "People are not machines. We are all unique, and our behavior is dictated by our personal circumstances, not just our professional ones."