From Insights to Impact: Leveraging Data Analytics for Employee Benefits Success

Data analytics is a rapidly growing field of study. Countries, industries, and companies worldwide are increasing their investments in this field to better understand it and leverage its advantages. Schools are also recognizing its importance and are actively recruiting faculty members while creating courses designed to attract and prepare students for a world where a working knowledge of data analytics is essential.

Similarly, the human resources (HR) function cannot and must not be isolated from data analytics. HR teams, particularly employee benefits specialists, hold a wealth of information that can be harnessed to benefit their companies, employees, and even their employees’ dependents.

To unlock the power of data analytics in the context of employee benefits, we must first define what data analytics means for this function. This can be achieved by breaking down the data analytics process into five broad steps:

1. Goal Setting

Data analytics is only as powerful as its focus. Without specific goals to work towards, it is easy to fall into the trap of “boiling the ocean”, where huge amounts of effort go to waste performing work that is not useful to anyone. On the other hand, clearly defining the goals of a data analytics exercise aids decision making in areas such as the choice of tools, types of data to collect, and the means used to present the results of the exercise.

2. Data Collection

Data pertaining to employee benefits can be collected from a variety of sources:

Internal Data

Examples include benefit plan designs, insured benefit premium rates, census data, claims utilization records, and performance management data. It is also helpful to consider factors such as financial performance, growth targets, downsizing plans, and merger & acquisition (M&A) opportunities, which can significantly impact data analysis.

On a more granular level, we can get information from surveys, focus group discussions, suggestion boxes, and even casual pantry chats. These channels are often overlooked in the process of data analysis but can be a source of extremely valuable feedback.

External Data

Outside the organization, consider referring to industry publications, conferences, and government agencies. These sources provide insights into typical and creative benefits practices, industry developments, and legislative changes.

3. Data Cleaning

Although data cleaning is often labor-intensive, it is crucial for success. Data cleaning involves identifying and rectifying issues with missing, incorrect, duplicate, poorly formatted, and unstandardized data records. We don’t often get excited about data cleaning because it is hard work and the results of good data cleaning are often unseen, but it is necessary to avoid the “garbage in, garbage out” problem.

4. Data Analysis

This is where the fun begins. Data analysis is where a myriad of sophisticated techniques may be applied to the data available. It is also where meaningful insights start to surface, sometimes in the form of beautiful charts and infographics. It is, therefore, no wonder that this step is where most data analytics enthusiasts and experts prefer to focus their time and energy. Data analysis can involve:


Segmentation involves identifying the data fields to be used to categorize data meaningfully. For example, inpatient claims data analysis might involve segmentation by private and public hospitalizations, while outpatient claims data analysis might involve breaking down the utilization by panel vs out-of-panel clinics, to identify employees’ preferences and their potential impact on future costs.

Sensitivity and Scenario Analysis

Sensitivity analysis involves changing one variable at a time, whereas scenario analysis involves changing multiple variables at a time, while keeping all other factors constant. This analysis technique is particularly useful in assessing the impact of potential changes to benefits programs on the company’s budget and employee engagement.


While past performance does not guarantee future results, past data serves as a great point of reference for budgeting exercises. For example, when restrictions related to COVID-19 were gradually eased, we pre-empted a gradual rise in medical claims as employees and their dependents became more comfortable visiting medical facilities for elective purposes. Adjusting our projections for changes in circumstances improves the quality of our data analytics and our chances of making sound, evidence-based decisions.

5. Ongoing Feedback and Improvement

Effective data analytics exercises are often continuous, adapting to changing circumstances, evolving technologies, and new analysis techniques. Ongoing feedback and iterative improvements ensure that data analytics exercises are meaningful and produce a positive return on investment.

By leveraging data analytics, employee benefits specialists can arm themselves with the knowledge needed to design benefits programs that effectively meet employees’ needs, enhance engagement, and achieve greater financial sustainability for their organizations.

Please visit our People Solutions (opens a new window) page, or contact us to discover how data-driven solutions can transform your employee benefits program and drive meaningful results for your organization.