How Machine Learning and Data are at the Heart of Most Loved Workplaces®
Being a Most Loved Workplace® proves you listen to employees and strive to create a happier, healthier, and more productive workforce.
There are many ways that you can reach this goal, one of which is with machine learning (ML) and data. This focuses on emotional connectedness among employees – the key for organizations and employees to prosper.
We explore this in our whitepaper, documenting how the Love of Workplace Index™ (LOWI) helps predict employees’ love for their workplace and their level of commitment. Using a custom ML model, feedback from workers was extensively analyzed to gain valuable insights that would have originally been a guessing game.
In this article, we will use the findings from our whitepaper to paint a bigger picture of how machine learning and data are at the heart of a Most Loved Workplace®.
Understanding Machine Learning and Data
To fully understand the concept this article explores, it is worth understanding the fundamentals of machine learning and data, particularly when applied in the workplace.
Machine Learning (ML)
Machine learning is a branch of artificial intelligence (AI) that builds algorithms and models to allow computers to learn from data.
This knowledge is then used to make predictions or informed decisions without being explicitly programmed for each task. Patterns, trends, and relationships within large datasets also enable these models to improve continuously, making for a more efficient, accurate tool.
Data is the information collected by ML models to be used in algorithms. In this instance, Most Loved Workplace® gathered employee feedback through quantitative and qualitative surveys to understand their sentiments and emotions toward where they work.
This data is then used to train and refine the ML model, allowing it to make accurate predictions and provide insights on how to improve the organization’s work culture, employee engagement, and more.
Combining ML and data, Most Loved Workplace® can delve much deeper into employee experiences to tailor strategies for a more positive and productive work environment.
The Importance of Emotional Connectedness
With our help, many organizations can recognize that their employees play a vital role in achieving long-term, ethical success. Emotional connectedness must be nurtured to keep employees engaged, motivated, and productive.
It is worth noting that the need for emotional connectedness is only growing, with the COVID-19 pandemic being one of the biggest catalysts to burnout and decreased productivity in the workplace.
Feeling connected to where you work can lead to:
A sense of belonging: Emotionally connected employees develop a strong sense of belonging as they are part of a supportive and collaborative team. This improves job satisfaction, retention rates, and overall well-being.
Positive relationships: Emotional connectedness encourages positive relationships among employees, no matter the department or hierarchy. These relationships enable a streamlined workflow and a harmonious work environment.
Improved mental health: Through emotional connectedness, employees have an emotional support system and a sense of understanding from colleagues and leaders. This safety net reduces stress, anxiety, and other mental health issues.
Increased productivity: Happy, emotionally connected employees are more motivated and productive. With more mental energy and creativity to put into work, employees can increase their productivity and overall performance.
Strengthened company culture: The prior benefits listed are key ingredients to a strong company culture with a shared commitment and motivation to achieving the organization’s goals.
Most Loved Workplace® developed the Love of Workplace Index (LOWI) Pulse Validation to gauge emotional connectedness. This survey-based tool consisted of 28 questions covering five areas, allowing employees to rate their sentiments using a scale from one to five.
Using Custom-Built Models for Employee Insights
Manually extracting valuable insights can be challenging and time-consuming despite how useful qualitative data is. To address this, Most Loved Workplace® turned to ML.
Traditional Natural Language Processing (NLP) models offer high-level analysis but cannot simultaneously analyze multiple angles of qualitative employee feedback.
So, Most Loved Workplace® built a custom BERT (Bidirectional Encoder Representations from Transformers) model that allows for more accurate and personalized feedback by focusing on the following:
Sentiment: This is the overall feeling expressed by employees in their feedback towards the organization as a whole or in regards to specific aspects of the workplace. It helps to understand whether the feedback is positive, neutral, or negative.
Emotion: Analyzing emotions such as gratitude, annoyance, and optimism provides deeper insight into employees’ experiences and helps companies see the emotional impact of their practices and policies.
Topic: This concerns the overall trends or subjects discussed in the employee feedback. Categorizing by topic helps organizations find key areas that are the most significant to employees and thus need attention first.
Theme: Similarly to the topic, the theme is the specific strengths and weaknesses mentioned most by employees. The theme tends to be more specific than the topic and helps companies pinpoint niche areas that need attention.
Gaining Insights from Qualitative Data
For deeper insights, employees were also asked to respond to qualitative statements which showed the main reasons why they loved working for their company and gave suggestions for further improvement.
Over 4000 samples of qualitative feedback were analyzed with ML to reveal common themes and create an extensive list of recurring topics, providing a full understanding of the employee experience.
With this qualitative feedback, it is much easier for organizations to identify their strengths as well as areas that need improvement to benefit employees. When input into ML algorithms, outcomes such as employee performance can be predicted, along with other areas that would aid the organization’s success.
Regarding employees, using ML to analyze qualitative feedback allows for personal insights into areas they can focus on in order to grow within the organization, set their goals, and achieve career success.
How Machine Learning Benefits Employees
Providing leaders with in-depth feedback and employee performance data allows for informed decisions that have long-term benefits for everyone.
This includes identifying areas that need improvement and creating a work environment that aligns with employees’ needs and preferences. There are many ways in which ML can facilitate this process.
Personal Employee Guidance
ML algorithms analyze employee feedback and sentiments to provide personalized insights and recommendations. Employees receive tailored feedback on areas where they excel, potential career growth opportunities, and suggestions for performance.
By analyzing large amounts of data covering employees’ preferences, challenges, and aspirations, ML can offer support that they would not have gotten otherwise.
This benefits employees as it helps with tailoring career paths, developing training courses, and evolving personal needs, which enhances job satisfaction and retention rates due to the ability to grow within the company.
Data analytics enables organizations to find areas of improvement, particularly when it comes to issues that have arisen due to poor communication.
Management can create transparent and inclusive communication channels by using insights from employee feedback. Employees are encouraged to voice their opinions and concerns, knowing what they have to say will be thoroughly analyzed and considered.
This fosters a company culture that values open communication where every employee feels empowered and comfortable to contribute to the success.
Proactive Problem Solving
It is always best to prevent an issue rather than deal with the repercussions. With ML, organizations can predict employees’ commitment, performance, and willingness to go beyond the call of duty to benefit the company.
This predictive ability enables potential issues to be proactively addressed to create a more positive and supportive work environment.
Data analysis can shed light on employees’ work-life balance and potential burnout risks. This allows for measures that support employees in maintaining a healthy balance between work and personal life to be implemented, promoting well-being and reduced stress.
How Machine Learning Benefits Employers
As well as benefiting employees, ML, and data processing offer several advantages to employers, empowering them to create a thriving work environment and drive organizational success.
Through ML, employers can see those within their workforce that have a lot of potential, are experiencing issues, or would be better suited in another department. This makes managing and developing employees much more straightforward and decreases the chance of error and upset.
Because employees are being listened to and are seeing active improvements as per their feedback, they will feel valued. This makes them want to stay in the organization and continue to perform to the best of their abilities.
High employee retention is a huge benefit for employers as they won’t have to worry about regularly sourcing and training new staff. In addition, having a high staff turnover does no favors to an organization’s reputation and can damage its image with potential employees and stakeholders.
ML enables employers to make data-driven decisions that give great odds, especially in risky situations.
Using insights from employee feedback and performance data, employers can install initiatives that align with the organization’s goals and values, leading to more successful outcomes and employees who trust their leaders.
Finally, employers can continuously assess and improve their workplace culture and practices using ML and data analysis.
Regularly gathering data and analyzing feedback allows leaders to adapt to changing employee needs and preferences and, in some instances, keep them one step ahead.
Questions to Ask Before Applying Machine Learning and Data
Before using ML and data to create a Most Loved Workplace®, it is crucial to carry out some introspection and ask difficult questions that your organization may not be prepared to have the answers to yet.
The integration of these advanced AI technologies can revolutionize many aspects of a company, from employee engagement and productivity to decision-making and overall success.
However, to harness their full potential and ensure everything goes smoothly, businesses must carefully consider the impact it will have on their company structure and workforce.
Asking the right, difficult questions allows them to get into the complexities of ML and data utilization; it will be easier to optimize the process and use data-driven insights to power a healthier, happier, and more efficient workplace.
What are your goals?
The first thing to do is define the goals that ML and data integration should achieve when introduced into the organization. Determine the specific areas where data analysis and ML can provide valuable insights and improvements.
How will the data be collected?
Next, carefully consider how the data will be collected – many privacy regulations must be considered. Collected data must be securely stored, which can be done in databases, cloud storage solutions, or data warehouses for larger companies.
What about data quality assurance?
There is no point in collecting and securely storing data that will not provide you with what you need. The data must be high-quality and reliable, achieved by removing errors, inconsistencies, or irrelevant information that affect conclusions drawn from the feedback and every step that takes place because of it.
Can pre-existing systems be integrated?
To ensure ML and data aren’t doing more harm than good, it is crucial that it can be integrated into current software and applications before any more steps are taken. This could involve working with IT teams to ensure that everything is compatible and won’t cause any technical issues.
The Future of Machine Learning and Data in the Workplace
The future of ML and data in the workplace is promising and, from experience, holds a lot of potential for transforming how organizations of all sizes operate and ensure happy employees. Here are some key aspects that highlight the future of ML and data in the workplace:
AI-powered HR: AI-driven HR platforms will likely revolutionize hiring practices, employee engagement, and performance management. Advanced algorithms will also have the required data to match candidates to the right roles based on their strengths, weaknesses, and preferences.
Real-time feedback: ML will enable real-time analysis of employee feedback, enabling organizations to respond quickly to concerns and provide recognition for achievements. This will create a work environment that values continuous feedback and incentives, driving employee morale and motivation.
Ethical challenges: As ML and data become more involved in workplace operations, companies will need to face the ethical challenges that come with it. Ensuring the responsible use of employee data, maintaining data privacy, and addressing AI biases will be vital to building trust and maintaining a positive work culture and reputation.
It’s about time that companies really listened to their employees through personalized qualitative data from employees. The combination of ML and data analytics helps create a Most Loved Workplace® for many reasons.
If it weren’t for the in-depth, valuable insights from employees, then organizations wouldn’t have the ability to make performance predictions, be proactive with problem-solving, and make changes in the workplace that actually matter.
So, take heed of our whitepaper and leverage advanced technologies to gain a deeper understanding of your employees and watch as they become more engaged, productive, and proud to be a member of a Most Loved Workplace® – all thanks to machine learning and data.
Louis Carter is the founder and CEO of Best Practice Institute, Most Loved Workplace, and Results-Based Culture. Author of In Great Company, Change Champions Field Guide, and Best Practices in Talent Management, as well as a series of Leadership Development books. He is a trusted strategic advisor and coach to CEOs, CHROs, and leaders of mid-sized to F500 companies – enabling change and steering employer brand development together with highly effective teams, leaders, and organizations as a whole.