Employee classification. People Analytics for analyzing talent (III)

Talent analysis
The next step after the analysis of the 2015 staff evaluation of the company (talent evaluation, talent patterns) in our study case, is to apply the clustering technique in order to detect any talent clusters, or groups of employees with similar appraisals for each item. The goal of the clustering algorithm is to find groups or “clusters” of employees whose assessments results are similar, thus grouping very similar employees together while having groups as different from each other as possible. Among the existing range of algorithms we will apply a classic and most commonly used iterative algorithm called k-means.

Talent patterns in the organization. People Analytics for analyzing talent (II)

Talent analysis
The next step in our talent analysis in the organization is to try and identify if there is a more or less implicit pattern in the staff evaluations that might show any talent patterns in the organization, i.e. what are the most and least valued characteristics in which employees. This next step will allow us to analyze not just talent perception within the organization, but also the staff’s actual talent, as measured in their performance evaluation.

Employee evaluation. People Analytics for analyzing talent (I)

Talent analysis
This post is the first of a series of articles on People Analytics that will focus on talent analysis in the organization based on annual employee evaluations. The use of People Analytics techniques on the collected data will provide us with a valuable insight into the quantitative and qualitative state of talent in the company, and will help us understand the unique aspects and characteristics that define our workforce. Based on this detailed analysis we can accurately and measurably apply any initiative from the human resources department, since we can rely on practical and actionable conclusions that will result in a live and developing process.

Standard process for People Analytics

Standard process for People Analytics: start game!
This post describes the basic process of any People Analytics solution, which is made up of the following phases: 1) collecting and incorporating the knowledge available to the organization’s HR team, 2) learning from the acquired data through computational learning algorithms and data analysis (i.e. Data Science), 3) analyzing this expanded knowledge, and finally, 4) providing a response (prediction, classification, recommendation, etc.) to objectively justify the results when information is requested.

Work areas in People Analytics for talent management

Talent development
Any People Analytics solution must be able to support decision making in human resources. It should help managers during that process by integrating human models and knowledge. In this post we describe the lines of work in human resource management where People Analytics technologies can provide more valuable solutions.

What is talent management and what is the contribution of People Analytics?

Better is possible: talent management
In this post, we discuss about talent management, a new challenge for human resources and a new way of tackling people management, and how data analytics can provide valuable solutions in this field. The term People Analytics has come to refer to the wide range of technologies, techniques, and best practices used to analyze all the data generated by a company in order to draw conclusions for optimizing management.

People Analytics: The new age of human resources

For the third consecutive year the most important conference of the world dedicated to HR has been held in Philadelphia, the "Wharton People Analytics Conference". For another year at sngular we had the opportunity to experience it live.

Starting out s|ngular Academy with data & analytics

In the division of Data & Analytics, we have started some training activities, intended to cover professional skills in the area of Big Data and Analytics.