At ServiceNow, we believe making the world work better for everyone begins with a diverse workforce that’s supported by an equitable and inclusive environment. The way we pursue our diversity, equity, and inclusion (DEI) goals is grounded in data and metrics.
It's impossible to become an organization that truly embodies DEI without first knowing the current state through DEI data analytics. We’re fortunate to have a variety of data sets that help us gain insights into how our diverse workforce is progressing, as well as where challenges exist.
Representation data helps us understand the distribution of race, ethnicity, gender, and other dimensions of identity in the U.S. and around the world. Employee sentiment data is captured through our annual employee voice survey (EVS) and other forums, such as our Employee Belonging Groups.
We also have an abundance of process data that helps us analyze performance, promotion, pay, and attrition of our talent. We analyze all these data types to understand where we are as an organization.
The EVS helps us gain insights into the perspectives of employees. DEI is an important topic covered in the EVS. We ask employees to indicate how strongly they agree with statements such as “At ServiceNow, diverse perspectives are valued and respected” and “People from all backgrounds have equal opportunities to succeed at ServiceNow.”
Employee responses are sorted by race, ethnicity, gender, job level, department, region, and other categories to help us identify thematic differences in employee experience among different groups. Insights gleaned from this analysis can point to areas of opportunity within processes and culture to help us build an organization committed to fairness and belonging.
Another tangible example of analyzing DEI data is conducting a talent equity assessment. This is a formal assessment of talent management processes, such as talent acquisition, progression, and retention. In addition to getting direct input from employees through the EVS, we want to know if there are systemic barriers to our ability to attract, retain, and grow underrepresented talent.
The equity assessment identifies outliers to the data that help us pinpoint processes and policies that need a particular focus. At this point, we can start converting data into metrics we want to achieve as we roll out improvements.
Throughout the process of analyzing our DEI data and implementing improvements, newer data sets become available that can help us generate deeper insights. For example, in addition to looking at hiring funnel throughput data, we now scrutinize each phase of the hiring funnel: applicant tracking, recruiter screening, manager screening, offer extension, and offer acceptance. This helps highlight areas where we can do more across the company.
We aim to achieve growth in overall representation, leadership diversity, employee experience, and DEI innovation. This growth will result from process and program improvements, product capabilities, partner engagement, and community impact.
DEI data can help us create a strategy that will increase diverse leadership, specifically regarding women representation. The DEI team noticed a positive trend in women representation around the world. We examined hiring and promotions and found women rising in the ranks of leadership. Survey data also indicated women would benefit from formal support programs.
Based on both sets of data, we crafted a strategy that prioritized professional development opportunities for women across the regions, including leadership training, women support groups, and mentoring. The data helped us get ahead of the growing needs of this demographic and provide the right support at the right time to those who would benefit the most.
Once the strategy is set, we can use data and metrics to track progress. This helps us determine if the initiatives we included in our DEI strategy are working. Year over year, we can see whether we're improving in our metrics of women in leadership representation and sentiment related to employee experience.
From this comparison, we can examine programs, processes, and policies and adjust them accordingly. Initiatives that prove themselves through metrics can then be scaled for broader impact.
Using data to make good decisions that drive DEI impact is a virtuous cycle. Analyzing data leads to designing a DEI strategy. Data is then analyzed again to measure the effectiveness of the strategy. This cycle continues until programs prove their effectiveness and are scaled.
DEI data is a crucial part of calibration and constant iteration to achieve the growth we want to see. Without it, we don’t know where we are, where we’re going, or where we need to make changes.
Find out more about ServiceNow’s commitment to DEI.
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