Peony

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Inspiration

The inspiration behind Peony comes from our passion for tackling the stubborn racial disparities in pay that still plague many industries. Despite progress toward workplace equity, salary gaps and unequal distribution of bonuses and equity persist. Inspired by platforms like Glassdoor, we wanted to take things further by focusing on these specific issues. We saw an opportunity to create an exciting, data-driven tool that shines a light on these inequities, helping organizations bridge the gap and foster fair, inclusive environments for everyone.

What it does

Peony is a comprehensive tool designed to analyze and address racial pay inequities within organizations. It compares median and mean salaries across different racial groups for similar roles and job levels, ensuring transparency in salary bands and equitable distribution of bonuses and stock options. Ultimately, Peony attempts to helps bring attention to pay gaps and take actionable steps toward closing them.

How we built it

We built Peony using React and Next.js for a smooth, user-friendly interface, and Python for backend data processing and analysis. TypeScript helped us ensure type safety and catch errors early in development. The platform includes clear charts and graphs to show salary differences across racial groups, along with features to evaluate salary bands for transparency and fairness.

Challenges we ran into

One of the biggest challenges we faced was data availability. Obtaining reliable and complete datasets, especially regarding bonuses and equity distributions, was difficult since many companies do not provide transparent salary information. We also encountered issues with missing data, which required us to use reduced datasets, limiting our analysis in some cases. In addition, interpreting extreme cases of pay disparities through mean analysis sometimes skewed results, requiring careful balancing between median and mean calculations to ensure accurate conclusions. We also struggled with proper time management and prioritising the right next steps.

Accomplishments that we're proud of

We’re proud of our first attempt at this hackathon with Peony. While we didn’t accomplish everything we aimed for, we successfully developed a model that balances median and mean salary analysis, and created clear, accessible visualizations. Peony attempts highlights pay gaps and provides actionable insights to help organizations promote pay equity.

What we learned

Throughout this project, we gained valuable insights into both front-end and back-end development. We learned TypeScript and basic front-end skills, which were crucial for creating a user-friendly interface with React and Next.js. We also explored the intricacies of integrating front-end and back-end components, ensuring that our data processing in Python effectively fed into the visualizations and features we built. This experience deepened our understanding of pay equity, statistical modeling, and the importance of data transparency and ethical handling of sensitive compensation information.

What's next for Peony

Moving forward, we aim to expand Peony's functionality by integrating more comprehensive datasets and refining our analysis models. One of our goals is to include additional demographic variables, such as location, to provide a more holistic view of pay equity. We also plan to develop more advanced predictive analytics to help organizations forecast the long-term impact of their compensation policies. Additionally, we hope to collaborate with companies and policymakers to promote the use of Peony as a standard tool for ensuring pay equity across industries. Our ultimate vision is for Peony to become a critical resource in the fight for workplace fairness and inclusion.