Develop Workplace Diversity & Inclusion Programs Backed By Data

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Your diversity, equity, and inclusion programs must be backed by data. In this blog, we expand upon the data and analytics discussed in our webinar to help meet your diversity goals.

During our webinar co-hosted by the CEO of diversity and inclusion (D&I) consulting firm, The Kaleidoscope Group, our speakers discussed many ways that organizational leaders can look at their HR data to plan and improve their D&I initiatives.

They emphasized that data from the whole employee lifecycle – from recruiting to job performance to separation – is the only way to get more context from your HR data.

The example measurements in the slides below will help you explore possible biases in your HR processes and practices and help you uncover data correlations that could shine light into why you may not be hitting your D&I goals.

1) Hiring Analytics

  • Recruitment: Look at your candidate pipeline and understand if and where diverse candidates are dropping out of the hiring process. This can be done by baselining all of your metrics across all hiring stages by total number of applicants—with your numerator being the number of candidates that actually reach that state. The goal is to see how flat the trajectory is throughout the pipeline and look for drop-offs. In this example, there is a 9% drop-off between the Interview and Offer stage. This could be indicative of biases present at that stage of the interview process.

  • Candidate Sources: When organizations set out to create a diverse workforce, they often have the opportunity to be more efficient about their cost-to-hire spend. This tree map demonstrates the differences in where majority versus diverse hires are sourced for a sample organization. If you are directing most of your recruitment spend on University hiring and you’re not seeing diverse representation among your candidates, there could be other more economic sources to diversify your candidate pipeline. See what our CTO says about this

  • Experience and Job Level Hiring: While you may be hitting your D&I goals for new hires at a high level, you should dig deeper to see if you are hitting goals within subgroups, such as new hires by Experience Level or Line of Business. Percentage of total (versus raw count) can be a good way to view this data. Are certain lines of business still underrepresented? Are people being brought in with equal opportunity and at equal salary points?

  • Other hiring metrics:
    • Location Demographics: is your candidate pipeline representative of the demographics of the geography in which you’re hiring?
    • Cohort Analysis: A good way to measure your improving is to compare cohorts (i.e. Class of 2021). Are you seeing incremental improvement over time? If not, do you need to adjust your strategies

2) Development and Advancement Analytics

  • Promotion Rates: Compare how long it takes individuals from different groups to get promoted. Percentage of total is a good way to look at this metric, but you’ll get even better insight when you compare other dimensions against the overall promotion rate, such as by hiring class.

  • Compensation: Many organization are working to ensure that employees are compensated equally for equal performance. The type of chart below is very helpful to demonstrate the disparity between two groups, such as diverse vs majority and male vs female. The larger the shift between lines, the bigger the problem.

  • Promotion Opportunities: What percent of your promotion pipeline are of diverse backgrounds? Overlaying this data with the control group shows where the biggest discrepancies are. In this case, we see significantly less diverse candidates in the L4 pipeline. Are grooming efforts lacking at this level?

  • Learning and Development Activities: Evaluating your learning investments for your employees can help you identify if spending is equitable. But, measuring outcome of learning programs is just as important as the amount spent. It’s important to question the potential gain for an individual who takes advantage of a learning opportunity. Will that person gain as much benefit as others on their team who take advantage of the same opportunity? For example, would a female employee gain more benefit from a ‘Women in Leadership’ conference?

3) Performance and Ratings Analytics

  • Performance Calibration: When assessing how diversity efforts have paid off, this chart demonstrates performance ratings among majority and diverse employees, pre- and post-calibration. It would be more helpful to look at this against other dimensions, such as promotions. Do the promotion rates match the performance scores among both populations?

  • Quantitative and Qualitative Metrics: It is important to incorporate both types of measurements in your performance ratings. This allows you to identify what soft skill or other performance objective that is measured qualitatively may be impacting a quantitative business goal, such as sales. This is a good way to identify where training/coaching/mentoring may be needed for individuals to meet their goals.

4) Retention Analytics

  • Retention Rate: Are you having trouble retaining diverse employees? Compare this metric against performance scores while they were employed to see if there was a pattern of poor scores. If performance scores don’t indicate likelihood of employee turnover, are you having an issue with truth-telling? Are managers giving an employee rating that’s higher than what they believe because they believe it’s wrong to give a low score to a diverse individual instead of providing the grooming and training needed to help them perform better?

  • Exit Surveys and Company Reviews: Given that people are often emotional when they leave a company or don’t want to “burn bridges” by providing a bad review, this data alone can be misrepresentative of the truth. Combining exit survey data with company reviews when they were employed can give you a better idea of what may have driven them to leave. Are there consistencies among people who left? Is your exit survey innately biased in the way it is phrased? Who is asking the questions? Is the survey process setup in a in which people from the diverse group are encouraged to answer truthfully?

8 Tips To Get Started

Think big but start small. You won’t solve all of your diversity or data problems at once, but you can start working towards your goals now with the data you have. As you move down this list and mature your data and analytics practices, you will be able to truly understand the state of diversity and inclusion in your organization and make incremental improvements towards your D&I goals.

  1. Collect – Figure out what data you need, where it resides, and collect it throughout your organization. Don’t rely on Excel. BI tools have matured greatly to allow users in any line of business to do their own analysis.
  2. Organize – Organize, cleanse, and format your data before loading it into your tool or performing analysis. Inconsistent, or incomplete data will result in an inaccurate picture.
  3. Automate – Reduce the time required for data collection and reporting and remove bias from your analysis by automating all of these processes.
  4. Analyze – Dig deep in your data, measure against many dimensions, identify trends, apply advanced analytics, and create visualizations that communicate what’s going on.
  5. Democratize – Share the data outside of HR to encourage your company to play an active role in meeting your D&I goals.
  6. Enhance – Bring in more data from across your organization and integrate third party data from the outside. Demographic and location data can enhance your HR data and provide greater context.
  7. Socialize – Get people talking about your data and analyses throughout your organization. When you socialize the data, it shows you are committing to making a change and you create a culture of collaboration.
  8. Share – Be willing to share your data externally outside your organization. With this kind of transparency, others can learn from successes and failures, and you send the message that you are part of a community that brings positive change. Data should be used as a growth tool, not just as a correction tool.

Remember: data shows a part of the story. All data is a partial representation of the facts; but when intuitive thinking and qualitative data are analyzed together, you will gain more context about the state of diversity, equity, and inclusion within your organization.

To get more tips about how to do better with your D&I programs, listen to the webinar recording.

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