Using Big Data To Boost Employee Satisfaction And Loyalty

Employers are now using the many applications of big data to better the lives of employees.


Big data adoption skyrocketed from 17% to 53% of companies between 2015 and 2017. Most white papers on big data focus on their benefits in marketing and security. However, there are other applications as well. Many companies are using big data to improve workplace morale.

How Big Data Improves Workplace Morale

Maintaining employee satisfaction is one of the most important roles of any employer. In 2017, employee satisfaction ratings were 89%, which were the highest they had been in years. While a number of factors have played a role in growing employee satisfaction ratings, I suspect advances in big data play a prominent role.

Here are some ways that big data can be incorporated into your strategy to boost employee loyalty.

Use big data to automate routine tasks

Before you can utilize big data to bolster employee satisfaction ratings, you must understand the underlying psychology. One place to start is with Maslow’s Hierarchy of Needs. This model states that employees will try to satisfy their need for survival first, which explains why employees are often willing to work jobs they hate to feed themselves. Once that need is met, they will focus on security, social connections and then self-actualization.

The most important tiers of Maslow’s are satisfied during the course of the job. However, the self-actualization need is harder to satisfy. Fortunately, big data makes it easier by freeing the employee’s schedule to focus on tasks that give them a greater sense of purpose.

Big data is making automation easier than ever. However, there is still a lot of discussion over the degree to which workplace tasks can be automated.

Most experts advise brands to focus on automating the most routine tasks, rather than automating the entire process. This means that employees will be able to focus on more complex and fulfilling aspects of their job. Tools like Zapier connects disparate systems to share data and automate the most routine of tasks to increase productivity and efficiency.

This will help improve employee morale since employees won’t be burnt out on mundane tasks that fail to help them meet their self-actualization needs.

Provide more accurate employee feedback

The perception of unfairness is one of the biggest causes of employee dissatisfaction. Every employee wants to be recognized for their own contributions.

Unfortunately, even the fairest manager may have difficulty providing objective reviews for their employees. With a lack of hard data, they often succumb to inaccurate heuristics, which isn’t fair to employees that receive the short-end of the stick.

Tools like Achievers uses big data and employee engagement to make it easier for employers to objectively review all relevant data on their team members. They can make sound, analytical reports. Even if the reports don’t tell employees what they want to hear, they will at least be based on indisputable facts, so employees will be less likely to feel jaded by subpar performance reviews.

Minimizing wasted time on redundancy

Nothing is more frustrating for an employee than spending countless hours on the same task as another employee. It doesn’t matter whether the employee is compensated for the work they did on a project that was wrongly assigned to them. They will still feel a lack of satisfaction for not contributing and annoyance at higher-level management for wasting their time.

This is a common issue in even relatively well-organized workplaces. Big data makes it easier to recognize overlapping projects and make sure employees don’t unnecessarily waste time on them. It has also led to the development of new employee scheduling tools like Visual Planning that can minimize staffing errors.

Big Data is a Vital Key to Boosting Employee Satisfaction

Maintaining employee satisfaction is vital to all companies. Big data is giving brands new opportunities to build rapport with employees and upper management.


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