Before delving into the analysis, it is essential to comprehend the gender pay gap data being used in the investigation. The data includes job roles, industries, and organisational sizes.

Statistical analysis

Statistical methods can provide valuable insights into the gender pay gap data. This can be used to examine the trends in the gender pay gap over the 2017-2022 period. Plotting line charts or employing regression analysis can reveal patterns, whether the gap is narrowing or widening over time.

Power BI offers a range of statistical functions and capabilities to analyze gender pay gap data.

Descriptive analysis

Power BI’s built-in functions are used to calculate the mean, median, and standard deviation to summarise the gender pay gap data. Compare the average earnings of men and women to identify any significant disparities. These metrics provide an overview of the gender pay gap and its distribution.

Descriptive methods can provide a comprehensive understanding of the gender pay gap data. One way to analyse the data is to use Power BI’s grouping and filtering capabilities to group the data based on factors like industry, job roles, or organisational sizes (small vs large businesses). Segmenting the data by different demographic variables like age, education level, or job experience would also be possible to identify specific subgroups where the gender pay gap may be more pronounced. This helps pinpoint areas that require further attention and intervention.

Power BI’s slicers and filters segment the data by demographic variables like age, education level, or job experience. This helps identify specific subgroups where the gender pay gap may be more pronounced, enabling targeted interventions.

Diagnostic analysis

Diagnostic analysis helps identify potential causes and factors contributing to the gender pay gap.

It is possible to create charts, bar graphs, or heat maps using Power BI to compare men’s and women’s earnings across different categories. Interactive visualisations empower users to drill down into the data and explore real-time disparities.

This can then be turned into interactive dashboards to present the diagnostic analysis results effectively. These dashboards can display key findings, correlations, and trends, enabling stakeholders to grasp the underlying causes of the gender pay gap intuitively.