Most employees do not feel engaged or motivated at work. To deal with this issue, many companies are turning to data-driven solutions like AI machine learning models.
These models can identify disengaged employees whose attitudes may effect their coworkers or clients. Additionally, they provide a framework for companies to increase employee satisfaction and engagement through team-building, on-boarding processes, and even coaching and goal-setting.
The competition for talent is fierce. Moreover, organizations these days compete to keep their good employees with them as satisfied employees are more productive and create a more positive working environment.
In short, using data-driven approaches to engagement and satisfaction prevents employee churn, promotes positive engagement, and resolves employee issues in real time.
Employee satisfaction models and analytics use many different internal datasets. For example, companies use employee survey data, email and meeting usage data (i.e., hours spent on the computer or in meetings), individual work history, targets, and achievements. Further data includes key company activities, landmarks, and events. These additional data help employers see the correlation between the events and employee satisfaction.
Many survey and natural language processing (NLP) tools that collect and analyze internal data. However, other external datasets may include company reviews and social media profiles and activities.
A good event database with geospatial census-related data can also help pinpoint issues, especially for large enterprises with many locations. Therefore, additional helpful datasets may be employee segmentation and surface engagement about events—global or local.
The two key challenges of this use case both stem from the same issue: privacy. In other words, companies must comply with local laws on collecting personal employee data. By the same token, though, even if company practices are legal and collected in good faith, the company should be mindful of employee feelings about the data-gathering activity.
Microsoft, with the help of a data analytics firm, processed volumes of data. The company examined employee calendars and the frequency of meetings. It could tell that employees spent an average of 27 hours a week in meetings, often with a whopping 10 to 20 people at each gathering. Moreover, although the data showed that workers frequently performed well after transferring to different divisions, Microsoft had barred employees from such transfers unless they’d been at their current division for 18 months and a supervisor approved the transfer.
E-mail use was analyzed and revealed just how shackled workers were to this communication medium—how long they spent reviewing and replying to e-mails and at what times of the day. The results signaled that workers spent long hours on these tasks.
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IBM MarketScan Research Databases provides one of the oldest continually-updated collection of health claims & workplace data in the USA
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