Diversity and Inclusion Starts with Identifying and Removing Biases

Diversity and Inclusion Starts with Identifying and Removing Biases

Learn how you can leverage Relational Analytics to ensure diverse talent has access to the internal relationships required to succeed in their roles at

Diversity and Inclusion has been one of the hottest topics in the HR space in the past few years.

From companies revising their websites and advertisements to prominently feature women and people of color to frequent social posts championing the accomplishments of diverse employees as part of employer branding and social media strategy, the increased focus on D&I can be seen everywhere. And it’s great!

Studies have repeatedly shown that high-performing teams are highly diverse in nature, and that diverse teams are more likely to collaborate and innovate better.

However, I feel the implementation of these initiatives in most cases has been lacking- with the emphasis on racial/ gender quotas leading to accusations of reverse discrimination and also undermining the people being hired as well, instead of empowering them.

If someone enters a company through the Urban HR division, everyone will remember that fact, and the employee will be suspect and have to prove herself over and over. Whereas if everyone is hired on the same criteria, then the culture will see people for who they are and what they uniquely bring to the table. — Ben Horowitz, What You Do Is Who You Are; Urban HR here refers to race/ gender hiring quotas

Another mistake made by companies is to not emphasize on cognitive diversity, i.e. how people have differences in their thought processes to solve problems, and thus lose out on the ability to innovate through new ideas being brought to the table, or even reflecting on existing ideas through a fresh pair of lens.

For convenience, the terms “diversity” and “people from diverse backgrounds” in this article refer to both gender/race diversity as well as cognitive diversity.

Companies need to shift from the concept of quotas/ preferential selection for people from diverse backgrounds and instead focus on identifying and removing biases in all their people decisions across the employee lifecycle, from recruitment to promotion to termination decisions.

People analytics can help you get started with this transformation instantly. It isn’t rocket science, but it does require the use of an integrated people warehouse connecting data across all your HR systems and files, preferably along with data from your finance and business systems too to show the business impact of strategic initiatives like Diversity & Inclusion. 

Here are some data points you should be tracking to identify and remove biases across your HR processes.


Start with ensuring your candidate scorecards and the parameters on which a candidate is judged across the recruitment cycle are structured to be as free from bias as possible.

Digging further into the funnel lets you see which jobs are not seeing people from diverse backgrounds go through, wherein the process they’re dropping off and which hiring managers and recruiters could possibly be biased.

You could also identify hiring manager biases in your internal hiring the same way.

It is highly possible that people with less privileged backgrounds are less likely to negotiate their salaries/equity/perks than their co-workers in similar roles. Compare the final offers given to people of diverse backgrounds as compared to the average offer for similar roles.

Career Progression and Performance Reviews

Similar to the recruitment funnel, employee career progression within the company can be viewed and analyzed as a funnel too.

You should analyze the average time spent in a job grade until promotion and the percentage promoted to the next job grade. Comparing the promotion funnel for diverse candidates to the overall funnel across different functions, departments, locations, hiring managers can help you identify existing biases within your organization.

An interesting data point to drill into for identifying possible sources of bias is to compare an employee’s race/gender/thought process(this can be measured through psychometric tools) to their manager’s. This can also be used while auditing your performance reviews to identify managers giving possibly biased reviews.

Keeping the macro results of these investigations public across the company is important. There’s no better way to remove biases than to prove them wrong with hard data.

Compensation and Benefits

In a similar argument to the one I made about people with less privileged backgrounds accepting lower offers, it is possible that people from diverse backgrounds are not comfortable negotiating their pay raises. Comparing the compensation packages given to people of diverse backgrounds as compared to the average compensation for similar roles can help you identify if this is a problem in your organization.

This can be done by drilling down into the pay gap, median base pay for similar roles and the Compa- Ratio for your employees across different aspects of diversity.

Employee Engagement and Inclusion

Diversity without Inclusion means nothing.

A key aspect of inclusion is ensuring employees from diverse backgrounds are integrating well into the organization, have the internal networks to succeed in their roles and progress up the organization, especially the visibility with senior management when it comes to reviews such as performance or promotion, which might be skewed against diverse talent if they don’t have relationships with the decision-makers as compared to their peers.

With Relational Analytics, companies can leverage an objective measure of inclusion alongside their surveys to identify employees from diverse backgrounds who are not well-integrated with the organization and help them get connected with more people in the organization by introducing them to people who have larger networks.

Relational Analytics can help you identify potential risks and points of intervention for blockers to successful DEI initiatives across your employee lifecycle, by providing insights such as:

Does diverse talent have the same access/ relationship to senior colleagues/ managers as compared to other colleagues in the same job, at the same level and tenure?

Does diverse talent get the access/ mentoring that will develop their careers and give them senior visibility?

Is diverse talent actually integrating with the organization and building influence outside of homophilic associations fostered through ERGs?

Which employees should we leverage as influencers and change agents to drive successful adoption of DEI initiatives across the organization?

You can identify whether male managers are forming stronger bonds with their male reports as compared to their female reports, and vice versa for female managers to filter for bias in the performance review/ promotion process. This is also useful for leadership development to ensure people from different demographic groups are able to form bonds with stakeholders across the organization.

Companies can also identify cliques within the network and analyze whether people from diverse backgrounds are actually integrating with the rest of the organization, instead of existing in a bubble. We’ve detailed some possible use cases for Organisational Network Analysis in these articles:

  • Improving WFH Effectiveness with Relational Analytics
  • Surviving 2020 and Navigating the New Normal with Relational Analytics
  • Relational Analytics Business Impact Case Studies from Panalyt, Uber, Genpact & McKesson
  • Improving Remote Employee Wellbeing with Relational Analytics
  • Driving Business Performance with Relational Analytics
  • Relational Analytics: A Magic Lens into Your Remote Organization

Analyzing how the results of employee engagement/ satisfaction surveys vary for people from different backgrounds can also point you to factors that are blocking your culture from being an inclusive one.


Drilling down into your company’s attrition allows you to see if there is a difference in attrition rates for people from diverse backgrounds as compared to the general trend.

Higher involuntary attrition(terminations) could point to biases amongst managers
, whereas higher voluntary attrition(especially early attrition) could be a result of a bias in one of the other areas mentioned above.

Is there a difference in attrition rates for women under male managers as compared to male reports or vice versa with female managers?

I hope you can take away some of the concepts discussed here and apply them to improve your diversity and inclusion efforts in 2021. Remember, the success of any people analytics project depends on ensuring your data is structured properly for advanced analytics!

Feel free to contact me at if you would like to discuss how people analytics and organizational network analysis can boost your diversity & inclusion efforts in further detail!

About Panalyt

Panalyt bridges the People-Data Gap, enabling real-time, uniform access to relevant people data, reports, and insights for CxOs, HR, and business managers.

People data, including employee interactions and connections, is combined with business data empowering businesses to leapfrog to data-driven decision making, eliminating bias and improving engagement, sales effectiveness, productivity, and, as a result, business performance.

Interested in a further discussion on how People Analytics and Relational Analytics can help you drive an improved employee and business outcomes? Book a 30-minute discovery call with our Panalyt co-founders to learn more!