People & Performance Accentuated By Talent Sciences


Talent Sciences is the business capability of using advanced analytics techniques and predictive models to drive HCM decision making. It calls for a logical connection between decisions about people, HR program investments and strategic business outcomes. This can form a backbone of the human resource function of all organizations independent of the sector in which it operates. HR metrics also are known as talent sciences or peoples’ metrics is a sophisticated application of data mining and data science techniques applied to people-related data. It is an essential way to quantitatively gauge the spend and the outcome of employee engagement programs and HR systems to measure the effectiveness of various HR initiatives. They provide the companies with the power to measure year on year comparisons on various parameters.

“Talent Sciences enables the organization with powerful insights to effectively manage employees to reach the business goal quickly and with high productivity. Challenges abound in this field as to identify what kind of data needs to be captured, stored and processed and how to build the model and predict capabilities to maximize return on investment spent on its human resources”

Talent Sciences can be implemented in the following strategic HR avenues:

Talent Management

Talent management is a soft space. Historically, we haven’t been able to measure definitely the things that we intuitively believe to be true. But businesses are mandating it. HR is being held accountable to deliver business results. And the language of the business is AI. The growing importance of sophisticated AI interventions to HR–not simply reporting what already exists in an organization but predicting what could or should be–is a result of “the recognition that the efficient use of labor and deployment of resources is critically important to the business results of the company”.

The goal is to create a model capable of predicting possible career development based on an individual’s previous experience, industry expertise, education, professional skills, etc. The modeling process requires an extensive array of data to ensure a solid base for analysis, including publicly available vacancies, resumes, recruiting requirements, job descriptions and much more. Also, analyzing an employee’s LinkedIn job profile, the solution provides thorough evaluation of individual talent characteristics, including industry experience, skills, and competence to define the factors that are more and less important for a particular career model.

With all this information available, it is easy to create a complete picture of a person’s professional expertise and further compare it against similar available job descriptions, focusing on the career path. This way we can discover not only the best matching positions for every level of competence but also the skills and experience that are required for an expert to move up the career ladder.

Attrition Management

The opportunities to add value through HR practices come more from stopping the wrong outcome from happening than from reporting on what has happened. For example, the cost of voluntary turnover has been established at approximately 1.5 times the annual base pay for salaried employees (Source: PWC Saratoga and CEB). Therefore, if you prevent two high-value employees, with salaries of 50,000GBP, from leaving the organization you have saved approximately 150,000GBP. In order to achieve this type of saving you need to know who will leave before they have left.

This is where sophisticated algorithms, that use historical data to determine the likelihood that someone will resign, come into play. There are a number of known actions that will prevent someone from leaving like signing bonuses, formal agreements around career progression and learning opportunities. However, the crucial part is knowing to whom you should offer these incentives. When it is possible to focus on the right population, through powerful and validated statistical models, it leads to better outcomes for a lower cost.

In addition, companies are using clustering algorithms to determine the common features of employees that are related to higher or lower retention rates. This insight means the right approach can be taken with the right employees, leading to better results at a lower overall cost.

Learning & Development

Talent Sciences can also inform training and certification strategies. If the data shows that specific skills are in extremely high demand, recruiters can advise the business when it might be beneficial to invest in training resources instead of seeking out new hires. Rather than spinning wheels in an environment where hiring is especially difficult and time-consuming, training existing employees may be a more economical or timely approach to acquiring the needed skills.

Learning Analytics can inform the development of every aspect of employee education—including understanding employees, building better programs, and having more insight into the interaction between employees and the software. It allows companies to keep track of learning processes and gauge what is working in employee training and what is not. two areas of management can focus on to determine training effectiveness. An internal matrix used to measure employee comprehension can be developed, with key benchmarks and targets. For example, are employees continuing to struggle with certain learning modules? Do they continue to fail certain quizzes or miss certain questions? Second is evaluating engagement—the measurement of whether or not employees are interacting with the content.

Authors of e-learning courses to receive anonymous feedback from learners. Data on individual exercises and on overall courses will provide developers with important insights into how learners use the course.  This will help reveal what is successful as well as recurring mistakes and misunderstandings, allowing authors to fix problems and improve courses.

Compensation – Pay for Performance

There has been a steady and constant shift towards ensuring that what employees are paid is closely tied to their contribution to the organization. Some organizations are removing the artificial limits that kept high performers from earning more than their managers. For a number of the companies; alignment of pay for performance is the number one shared agenda item between the CEO and the head of HR.

One of the best ways to demonstrate this practice is through a metric called the “Performance based compensation differential”. This metric expresses how much more high performers are paid compared to their average-performing peers.  For example, a score of 1.2 means that on average high performers receive 20% more compensation than average performers. Turning this critical question into a single number allows for powerful insight across the organization; it means that different locations, business units, and groups of employees can be easily compared using simple visual analyses.

One powerful way this translates to business value is during the annual pay review cycle. Most HRIS systems allow you to enter changes in pay, however, these systems do not enable you to analyze how these awards relate to performance and whether or not they are aligned to the goals of the organization. Most HR departments provide guidance and then trust their managers to get it right.

Being able to analyze all of these decisions in real-time, report this back to the organizational leadership and then revise these adjustments before they are confirmed leads to a demonstrated ability to ensure that the budget increases going into labour costs are being applied in the optimal way.

Work Performance Measurement

Many organizations have analyzed the profiles of top performers and now know that screening candidates for grade point average or academic pedigree is no longer considered a strong indicator of future work performance. Companies are turning to AI to get an eagle-eye view of their inherent employees and assessing their competency levels and creating a customized recruiting trend that does away with hiring under-performers. A high-tech company developed an AI model that accurately predicts job candidates who are likely to become “toxic employees” (those who lie, cheat, or commit crimes) and dramatically reduced this population among its hires by scrutinizing special parts of the interview process.

In today’s tight talent market, understanding both the local and macro conditions that may impact talent availability, salary and benefit decisions, and hiring timelines helps recruiters set expectations and adjust their approaches to attract candidates for hard-to-fill positions. Are other companies in the area currently on the hunt for similar skills? Is the salary being offered lower or higher than average? Is the skillset needed easier to find in a different geographic area? Data captured from job boards and other sources of information on talent supply and demand can help answer these questions and give recruiters the ammunition they need to get business buy-in on key decisions related to hiring strategy. Armed with data, recruiters’ discussions with hiring managers change: recruiters build immediate credibility and transform their roles from advisors to decision-makers.

Compliance & Risk

A lot of systematic and unsystematic information relevant to employees is available to the HR which can be leveraged to create, measure and redesign the policies and assess their compliance levels by employees. Sentiment analysis involves more than just the annual surveys. The data should be continuously tracked, analyzed and scrutinized on key topics. External data like Facebook, Twitter, LinkedIn, etc. provide valuable feed for sentiment analysis.

Talent Sciences minimize the threat of internal fraudulent practices from employees through non-compliance by identifying and/or predicting employees at high risk to violate security policies or other company regulations. Once the employees’ risk is assessed proactive actions could be taken for corrective actions. There are several ways talent science is being implemented in this area. Banks are studying patterns of fraud and noncompliance at employees’ end, and can now predict behaviors that will likely result in unethical behavior. Also, a UK financial services company uses AI to evaluate individual employees, spotting potential “rogue traders” and other compliance breaches as a part of proactive risk management.

Building workplace Culture

By examining this data in aggregate, for example, one manufacturing company discovered some of its junior managers spent more than 30 hours every week “managing up” with reports to senior executives or in status meetings. Bottom line: that left just 10 hours of time for “real” work for the host, not to mention the ripple effect across their own teams.

This is the brave new world of talent sciences that uses time management data to help companies understand the relationships—external and internal—driving corporate decision-making. Once a company understands the behaviors that correlate to success, they can measure them. Many of these organizations use the technology to tame meeting overload. Some also use it to examine how the habits of high-performing sales representatives differ from others. There are real behavior differences between great performers and average at each customer.

It is increasingly becoming mainstream to base the intricacies of an organization’s culture based on data and insights. A team of organizational development experts and data scientists from eBay measures the strength and adoption of its cultural values through a combination of internal and external data metrics. To compare eBay employees’ views with external perspectives, the team also conducts thematic analysis and natural-language-based analysis of news articles and Glassdoor to get a view of the external market perspective of eBay’s culture. Talent Sciences enables organizations with powerful insights to effectively manage employees to reach the business goal quickly and with high productivity. Challenges abound in this field as to identify what kind of data needs to be captured, stored and processed and how to build the model and predict capabilities to maximize return on investment spent on its human resources. However, increasingly, organizations are looking at enormous benefits of talent sciences to up the ante on people and performance metrics.


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