Setting Realistic Diversity Goals with Workforce Data Analytics

Organizations worldwide are increasingly prioritizing diversity, equity, and inclusion (DEI) initiatives, yet many struggle to set realistic, attainable goals. According to McKinsey research, companies in the top quartile for gender diversity are 25% more likely to achieve above-average profitability, but few organizations report being successful in meeting their diversity objectives. This disconnect stems largely from setting aspirational targets without grounding them in workforce reality. Workforce data analytics and people analytics offer a solution by providing market-driven insights that help organizations establish meaningful diversity goals based on actual talent availability rather than arbitrary benchmarks. This article explores how data-driven approaches can transform your diversity strategy from well-intentioned guesswork to strategic precision.

Traditional approaches to diversity goal setting often follow one of several flawed methodologies:

• Population parity: Setting targets that match the general population demographics
• Industry benchmarking: Comparing diversity metrics against industry averages
• Aspirational targets: Setting ambitious goals disconnected from talent market realities

Each of these approaches fails to account for crucial factors like geographic talent distribution, skill availability across demographic groups, and industry-specific talent pools. The result? Unrealistic targets that frustrate both talent acquisition teams and business leaders.

Workforce data analytics provides a better alternative—setting goals based on actual talent availability and creating targeted strategies to achieve them.

People analytics has transformed how organizations approach human capital management, bringing scientific rigor to what was once largely intuitive. When applied to diversity initiatives, analytics provides:

• Market-based realism: Understanding actual talent availability by demographic group
• Precision targeting: Identifying specific locations, schools, or organizations with diverse talent
• Progress measurement: Tracking improvement against meaningful benchmarks
• Strategic accountability: Moving beyond simple percentages to understand systemic barriers

Through utilising workforce data analytics via Horsefly’s platform, Mimecast was able to achieve an increase of 30% for female hires and 70% for ethnic diversity. This highlights the importance of data when diversity planning.

The Concept of Market Availability

At its core, the market availability approach uses workforce data analytics to understand the actual representation of different demographic groups within specific talent pools. Rather than setting arbitrary targets, this approach establishes goals based on:

1. The current representation of various demographic groups in relevant talent pools

2. Educational pipeline data showing future talent availability

3. Geographic distribution of diverse talent

4. Competitor hiring patterns and industry trends

This approach acknowledges an important reality: diversity availability varies significantly by:

• Job family and level

• Geographic location

• Industry and sector

• Experience level and educational requirements

For example, the representation of women in software engineering roles differs dramatically from their representation in human resources, which is much higher. Meaning, setting a blanket 50% target for gender diversity across all functions is neither realistic nor strategic.

Benefits of the Market Availability Approach

Adopting a market availability approach through workforce data analytics offers several advantages:

• Credibility with stakeholders: Goals based on data rather than aspiration

• Improved resource allocation: Focused investment where impact is possible

• Reduced frustration: Realistic targets that motivate rather than discourage

• Legal defensibility: Goals grounded in actual talent availability

• Strategic precision: Tailored approaches for different roles and locations.

1. Establishing Your Analytics Foundation

The journey toward data-driven diversity goals begins with establishing a solid analytics foundation. This requires:

• Internal data audit: Assessing the quality and completeness of your current workforce data

• Benchmark selection: Identifying the appropriate external comparisons for your organization

• Metrics definition: Determining which diversity metrics matter most for your business

• Technology enablement: Implementing people analytics tools that can process complex workforce data

According to Horsefly's implementation data, organizations with mature analytics capabilities are able to segment diversity data across multiple dimensions—location, job family, level, function—providing a much more nuanced understanding of representation challenges and opportunities.

Actionable Tips for Building Your Analytics Foundation:

1. Start with clean, complete internal data. Ensure you have accurate demographic information for the majority of your current workforce before attempting sophisticated analytics.

2. Implement consistent job architecture across the organization. Without standardized job families and levels, meaningful comparisons become nearly impossible.

3. Integrate external market data with internal workforce analytics. Tools like Horsefly's platform, with its diversity data can provide critical context about talent availability.

Learn more about building a strong diversity analytics foundation from our blog on navigating diversity and inclusion.

2. Conducting Market Availability Analysis

Once your analytics foundation is established, the next step is conducting a comprehensive market availability analysis using workforce data analytics. This analysis should:

• Identify the demographic composition of relevant talent pools
• Compare internal representation to external availability
• Highlight gaps and opportunities across different dimensions
• Establish realistic baselines for goal setting

Actionable Tips for Market Availability Analysis:

1. Analyze talent availability at the job family level, not just organization-wide. Representation varies dramatically across different roles and functions.

2. Consider geographic variations in talent availability. A realistic diversity goal for software engineers in San Francisco will differ from one in Atlanta or Minneapolis.

3. Examine both current availability and pipeline trends. Educational data can reveal whether talent pools are becoming more or less diverse over time.

For example, Admiral Group PLC transformed their recruitment strategy via Horsefly’s platform. They improved their women in tech initiatives through the help of targeted university outreach.

3. Setting Multi-Dimensional Diversity Goals

With market availability data in hand, organizations can set realistic diversity goals across multiple dimensions. Effective goals should be:

• Data-informed: Based on actual talent availability

• Segmented: Tailored to different functions, levels, and locations

• Time-bound: Acknowledging that progress happens over time

• Achievable: Setting targets that challenge but don't frustrate

• Strategic: Focused on business-critical roles and areas

Actionable Tips for Goal Setting:

1. Create a tiered approach to diversity goals. Set different targets for different job families based on talent availability and strategic importance.

2. Implement rolling targets that increase as pipeline development yields results. Today's realistic goal might be conservative, but investments in development can increase talent availability over time.

3. Consider both representation and inclusion metrics. Numerical diversity without inclusion is unsustainable.

4. Developing Targeted Diversity Sourcing Strategies

Once realistic goals are established, workforce data analytics can inform targeted sourcing strategies to meet those goals. This involves:

• Identifying specific talent pools with higher representation of underrepresented groups

• Developing location-specific sourcing approaches

• Creating targeted employer branding for different demographic segments

• Building strategic partnerships with organizations that can expand diverse talent pipelines

Actionable Tips for Diversity Sourcing:

1. Use analytics to identify unexpected sources of diverse talent. Horsefly's research shows that certain companies, educational institutions, and professional organizations have significantly higher representations of underrepresented groups.

2. Develop geographic-specific sourcing strategies. The most effective approach to diversity sourcing in Austin may differ dramatically from the optimal strategy in Boston or Chicago.

3. Leverage people analytics to understand which sourcing channels yield the most diverse qualified candidates. Track source effectiveness not just for applications but through the entire hiring funnel.

Discover more about using effective data to support diversity, equity and inclusion in recruitment from this blog.

Implementing Effective Measurement Systems

Setting realistic diversity goals is only valuable if you can effectively measure progress. Workforce data analytics enables sophisticated measurement through:

• Real-time dashboards that track representation against goals

• Pipeline analytics that identify where diverse candidates fall out of hiring processes

• Predictive models that forecast future representation based on current trends

• Intersectional analysis that examines multiple diversity dimensions simultaneously

Actionable Tips for Diversity Measurement:

1. Focus on leading indicators, not just lagging metrics. Track diverse candidate flow, interview rates, and offer acceptance alongside overall representation.

2. Implement cohort analysis to understand retention patterns. Improving representation requires both hiring and retention of diverse talent.

3. Use people analytics to identify specific barriers in your talent processes. Look for statistically significant differences in outcomes at each stage of recruitment and advancement.

Creating Accountability Systems

Measurement without accountability rarely drives change. Effective accountability systems for diversity goals include:

• Executive sponsorship with clearly defined responsibilities

• Inclusion of diversity metrics in leadership performance evaluations

• Regular review cadences with substantive discussion of obstacles and enablers

• Transparency in reporting both successes and challenges

According to Horsefly's client research, organizations that include diversity metrics in executive compensation are much more likely to make consistent progress toward representation goals compared to those without financial incentives.

Data Quality and Availability

One of the most significant challenges in implementing workforce data analytics for diversity planning is data quality. Organizations frequently struggle with:

• Incomplete self-identification data

• Inconsistent job architecture across locations

• Limited access to external benchmark data

• Privacy regulations that vary by country

Actionable Tips for Addressing Data Challenges:

1. Implement privacy-preserving analytics approaches. Techniques like differential privacy and aggregation can protect individual privacy while enabling meaningful analysis.

2. Create transparency around how diversity data will be used. Employees are more likely to self-identify when they understand the purpose and protection of the data.

3. Partner with vendors like Horsefly that specialize in diversity market data. External expertise can fill critical gaps in internal data capabilities.

Balancing Aspiration and Reality

Perhaps the most difficult challenge is balancing aspirational diversity commitments with data-driven realism. This tension requires:

• Nuanced communication about both commitment and constraints

• Education of stakeholders on talent market realities

• Strategic investments in long-term pipeline development

• Celebration of incremental progress toward ambitious goals

As workforce data analytics continues to evolve, several emerging trends will shape diversity planning:

1. Predictive Diversity Analytics

Advanced organizations are moving beyond descriptive analytics to build predictive models that forecast:

• Future representation based on current hiring, promotion, and attrition patterns

• Impact of specific interventions on diversity outcomes

• Long-term talent pipeline development scenarios

These predictive capabilities enable proactive rather than reactive diversity strategies.

2. Inclusion Analytics

While representation metrics remain important, cutting-edge people analytics is increasingly focused on measuring inclusion—the lived experience of diverse employees. This includes analyzing:

• Engagement survey data across demographic segments

• Network analysis showing interaction patterns

• Advancement and opportunity metrics by demographic group

• Sentiment analysis from various feedback channels

According to Horsefly's research, organizations that measure both representation and inclusion are vastly more likely to retain diverse talent compared to those focused solely on numerical diversity.

3. AI-Powered Insights

Artificial intelligence is transforming how organizations analyze workforce data, enabling:

• Automatic identification of bias patterns in talent processes

• Natural language interfaces for diversity data exploration

• Recommendation engines for specific diversity interventions

• Anomaly detection highlighting problematic trends

Setting realistic diversity goals is both an art and a science. The art lies in maintaining ambitious aspirations that drive meaningful change; the science comes from using workforce data analytics and people analytics to ground those aspirations in market reality. By understanding actual talent availability, segmenting goals by function and location, and implementing targeted strategies, organizations can make consistent progress toward a more diverse workforce.

The most successful organizations recognize that effective diversity planning isn't about lowering expectations—it's about focusing resources where they can have the greatest impact. They use data not as an excuse for inaction but as a guide for strategic investment in sustainable diversity initiatives.

As you evaluate your organization's approach to diversity goal setting, consider whether your targets reflect both your values and market realities. If not, workforce data analytics offers a path to more effective, credible, and achievable diversity outcomes—transforming good intentions into measurable results.

READY TO TRANSFORM YOUR ORGANIZATION? LET'S EXPLORE HOW MARKET INTELLIGENCE AND STRATEGIC PLANNING CAN HELP YOU BUILD A MORE SUCCESSFUL WORKPLACE

Sources: Horsefly Analytics, McKinsey