What is talent management?

Traditional methods of talent management involve systems and practices designed to maximize a company’s human capital and are often centered around five core phases of talent management:

• Planning: Determining the company’s talent needs and creating job descriptions to match.
• Attracting: Posting job descriptions on job boards, websites or apps to attract qualified candidates.
• Selecting: Evaluating and interviewing talent and ultimately selecting the right candidate for the job.
• Retaining: Offering growth, continuous learning opportunities and incentives that encourage employee professional development. 
• Transitioning: Offering benefits, conducting succession planning and creating effective workforce planning strategies.

For instance, traditional methods for the planning, attracting and selecting phases may involve recruiting methods such as posting jobs on company websites or job boards, manually screening resumes and cover letters or having hiring managers conduct phone screenings to evaluate a candidate.

While some of these methods may be entirely manual (imagine you’re an HR manager reviewing applicant resumes one by one), some traditional methods may include the use of software, such as a program used for keyword matching to select resumes before the HR manager reviews them.

Although not a comprehensive list, other traditional methods for talent management may include:

• Conducting annual or semi-annual performance reviews
• Providing group training or continuous learning opportunities to current talent
• Using reward systems to incentivize and recognize employee efforts
• Succession planning by identifying and helping to develop high-potential employees for leadership roles
• Offering employee benefits to attract and keep long-term talent

At their core, these traditional methods can offer great benefits to talent management practices. However, especially when compared to the use of advanced analytics in talent management, they have limitations.

These limitations may include subjectivity and bias (including unconscious bias), a lack of personalization to individual employee needs, inefficiency, lack of real-time insights, limited scalability, lack of adaptability to quickly changing markets and more. 

To overcome some of the challenges of traditional talent management practices, employers and human resources teams may consider incorporating advanced analytics into their current systems.

What are advanced analytics?

Advanced analytics — including talent analytics — involves a cycle of practices that companies can use to analyze important data and make strategic decisions.

It differs from traditional methods, which rely on historical data and subjective assessments, through its use of tools and techniques to form actionable insights. Before we apply this to talent management, we’ll briefly explain the cycle of practices involved in advanced analytics and some of the techniques it employs.

Below are the typical steps involved in the cycle of advanced analytics:

1. Search: Identifying metrics, key performance indicators (KPIs) and essential keywords.
2. Advanced analysis: Leveraging tools and techniques to analyze the data collected in the search phase. 
3. Deployment and monitoring: Implementing any changes derived from the advanced analysis and continuously monitoring the performance of those changes.
4. Data preparation: Refining and organizing data, such as deleting duplicates and restructuring, to ease further analysis.
5. Data exploration: Analyzing the data thoroughly and making strategic decisions based on the data results.

The cycle of advanced analytics allows a company to see current business information and use that information to make accurate predictions about the company’s future. This can offer insight into ways the company can improve its overall performance. 

To do this successfully, advanced analytics employs several different techniques. These may include: 

• Predictive analytics
• Descriptive analytics
• Prescriptive analytics

In talent management, these techniques can measure talent analytics, which can be leveraged in many helpful ways.

Examples of advanced analytics for talent management

To enhance organizational performance, you can apply the data-driven insights provided by advanced analytics to your HR systems and processes. 

Below are three types of advanced analytics and examples of their practical uses in talent management.

1. Predictive analytics

Predictive analytics, which includes methods like machine learning analysis, is used to predict possible future trends and outcomes. 

In talent management, predictive analytics can help a manager identify employees who may be suited for more responsibility or those who are most likely to leave due to job dissatisfaction.

Coupled with artificial intelligence (AI), predictive analytics can also assess the possible impact of hypothetical scenarios, such as how it would affect employee productivity to have every other Monday off.

2. Descriptive analytics

Descriptive analytics is a type of statistical analysis often used to identify changes in a business over time. It includes methods like natural language processing (NLP) and sentiment analysis or sentiment processing, a method based on NLP.

In contrast to predictive analytics, descriptive analytics focuses on what has already happened rather than using data to make predictions about the future.

Natural language processing, for example, can facilitate talent management by screening resumes or analyzing employee feedback.

In the case of analyzing employee feedback, sentiment processing can be used to identify the sentiment of the text and categorize it as positive, negative or neutral. 

3. Prescriptive Analytics

Prescriptive analytics uses data to determine what a company can do to see the best results. Essentially, it can help a company identify decisions that will impact its long-term success. 

Heuristic methods are just one example of many methods used in prescriptive analysis and in regard to talent management, can be applied in several helpful ways.

For instance, heuristic approaches can help identify which employees have the skills and experience necessary to increase a certain project’s chances of success. Using this information, a manager can group these employees together for the project.

Another example of the use of prescriptive analysis in talent management is its ability to help with succession planning. Looking at leadership potential and performance, prescriptive analysis can help identify which employees would be best suited for future opening roles. Knowing this, managers can provide necessary training or development that can help prepare talent for those roles.

Other practical uses of advanced analytics in talent management include:

• Predictive workforce planning (predictive): Anticipating future talent needs by looking at skill gaps and company needs
• Employee network analysis (descriptive): Understanding connections and collaboration between employees
• Recognizing potential highly engaged talent (predictive): Identifying which employees are likely to be highly engaged and why
• Optimizing compensation structures (prescriptive): Using market data and employee performance to develop competitive compensation and incentive programs

Benefits of advanced analytics in talent management

The intended outcomes of using advanced analytics in talent management are the same as with traditional methods. However, there are benefits that can come with the use of advanced analytics that traditional methods may not have.

These benefits include:

• Improved decision-making
• Enhanced efficiency
• Reduced bias
• Better employee experiences

What challenges come with using advanced analytics?

A key challenge with the use of advanced analytics in talent management is a lack of employees equipped with the necessary skills to use it.

Coupled with general competition for tech talent, companies may find it challenging to implement advanced analytics into their talent management practices successfully.

Companies and organizations shifting toward the use of advanced analytics for talent management will also need to consider privacy concerns.

Not only should they consider compliance and data protection regulations, but they also need to be transparent with their employees about how they are using these analytics and what type of information they are gathering.

Best practices for advanced analytics in talent management

To increase your chances of successfully integrating advanced analytics into your talent management strategies, consider these best practices:

• Consult your legal team: Talk to legal to make sure that your methods for using advanced analytics comply with company rules and data privacy laws.
• Establish clear company guidelines: Create checklists or other procedural guides to ensure everyone in the company is using advanced analytics appropriately and effectively.
• Check for bias: Although advanced analytics can be a great tool for reducing subjective bias, it can still produce biased results. Review results with an objective eye and adjust your input if needed.
• Communicate with your staff: Make sure your employees understand what kind of data is being gathered, how it’s being collected and how it will be put to use.

Why should businesses incorporate advanced analytics into their talent management strategy? 

Despite any challenges, companies may use advanced analytics to leverage their talent management strategies. 

By adhering to a series of best practices, you may see enhanced decision-making, reduced bias, improved employee satisfaction and retention and increased success for the organization as a whole.

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