Many organizations struggle with having a full and “on time” understanding of their workforce engagement and needs. Without this understanding, organizations risk losing high-performing personnel, hiring the wrong people, fostering toxic workplace dynamics, and underperforming.
Luckily, many government organizations’ human resources (HR) offices issue workforce engagement surveys, wherein a large portion of the organizations’ employees and contractors answer pressing HR questions through a combination of multiple-choice and free form answers. For many organizations, their largest centralized source of employee experience information comes from these workforce engagement surveys. However, manual analyses of these surveys are time-consuming and expensive, often taking months to complete. These engagement surveys hold the answers for improved performance, but the data are locked in narrative format!
Natural language processing (NLP) technology is the key to unlocking better and more timely workforce insights.
IT Concepts (ITC) has had the pleasure of working with many of these offices of HR, and we propose using natural language processing (NLP) on these workforce surveys to save HR employee time, achieve in-depth analysis, and get continued return on investment (ROI) from a single algorithm. This timely analysis can then be used to develop data-driven strategic hiring plans, learning and development plans, and much more!
The Benefits of NLP
Sitting at the intersection of linguistics, artificial intelligence (AI), and computer science, NLP is a form of machine learning (ML) which deals with the interactions between computers and language and aims to process and analyze natural language data such as text and speech. The study of NLP is rapidly evolving, and interest in it has grown over the past few years as myriad real-life applications have been discovered for it.
There are many uses for NLP technology, but highly relevant to HR departments, NLP can be used to perform sentiment analysis and topic analysis.
Sentiment analysis uses NLP technology to understand the feeling (positive, negative, or neutral) behind a textual answer. When applied to a workforce survey, sentiment analysis can assist with assessing employee satisfaction with overall organizational performance and specific organizational decisions. When combined with information about where an employee sits and their role within the organization, this analysis could even be used to identify high-performing people managers and well-satisfied teams –key information when deciding how to focus retention efforts.
Topic analysis automatically assigns topics to text data. Some businesses already use this form of NLP to rapidly sift through their customer feedback to understand which issues keep popping up for their customers and respond to them. Similarly, topic analysis can be used on workforce survey results to understand employee feedback and improve the employee experience. Topic analysis could be combined with the subject matter expertise of HR executives to understand how employees are responding to ongoing HR initiatives and if those initiatives are clear to them. Or topic analysis can be applied in an unsupervised way to identify which topics pop up most frequently, such as bias in the workplace, concerns about economic recession, or commitment to the organization’s mission.
Whether applied in a wide-ranging or narrow way, whether supervised or unsupervised, sentiment and topic analysis can be used to answer key HR questions.
From NLP to ROI
While manual analysis of workforce survey data produces valuable insights about employee engagement (a crucial factor in other organizational outcomes) it is a time- and resource- consuming process. NLP can unlock the same benefits in less time, and by freeing up resources, further complex analysis can be conducted to gain even more valuable workforce insights. With improved and faster analysis, this data can also be used to forecast workforce needs in a large-scale, organized way.
The benefits of NLP analysis on workforce engagement data are many-fold:
NLP analysis and automated data ingestion will save time. Rather than months of manual analysis, initial design of an NLP algorithm would take weeks to create. This will allow HR departments to quickly analyze survey results and adjust their work and policies accordingly.
A one-time creation of an NLP algorithm can be used year after year, proffering continued ROI. Subsequent use of an already-created algorithm on future surveys would take mere hours.
ML can identify new and unexpected understandings of organizations’ people needs. Unsupervised learning and anomaly detection offer a different-in-kind benefit than human analysis. This analysis could help HR offices uncover new-in-kind issues within their workforce before they become larger-scale issues; it could also uncover unofficial benefits and policies that should be implemented across the greater workforce.
Due to the rarity of this sort of advanced analysis in most human resource departments (whether in the public or private sector), government HR departments can differentiate themselves from other organizations and will be able to compete more effectively to hire and retain talent.
ITC believes that this is a promising opportunity for many government organizations. ITC is also well-suited to lead projects such as these, as our data science team includes cleared senior data scientists who are highly skilled in NLP. For more information about how ITC can help your organization unlock their workforce survey data, please contact email@example.com.