Although interest in measuring the effects of diversity has been growing, the topic still challenges even the most sophisticated and progressive diversity departments. Many diversity professionals and practitioners know they must begin to show how diversity is linked to the bottom line or they will have difficulty maintaining funding, gaining support and assessing progress. But are they ready for predictive analytics?
The Data-to-Wisdom Continuum
Over the past several years, diversity journals abound with volumes of information about the impact of a diverse workforce, primarily from a talent representation point of view focusing on organizational makeup covering race, rank and gender (counting heads). Many of these diversity professionals are working with inconsistent, basic information and have yet to move from being reactive to proactive and predictive. In short, they have made little progress along the data-to-information-to-wisdom continuum needed to provide sophisticated diverse workforce insights that are critical to strategic decision-making. How would you respond to the following questions?
• Do you struggle with defining or measuring the success of diversity initiatives or other diversity interventions?
• Are you constantly fighting the battle to show and justify the value that diversity initiatives or other diversity interventions are bringing to your organization?
• Does your organization view diversity initiatives or other diversity interventions as an expense versus an investment with predicted returns?
• Do you need to link diversity initiatives or interventions with the value they produce for your company?
• Do you need a method of predicting (forecasting) the value of diversity initiatives or other diversity interventions to help decide whether to train or do something else?
• Are your diversity evaluation efforts always after the fact — do you need a way to measure success using leading indicators that drive continuous improvement?
If you answered yes to any of these questions, then predictive analytics for diversity is for you.
For the past six years, I have been researching and developing a new comprehensive predictive analytics for diversity approach and framework that will address all of the above questions and more. My goal is to create the next level of diversity ROI-based tools that give diversity professionals a competitive edge and alignment to drive business performance and results. The Hubbard Predictive Analytics for Diversity framework is designed for professionals looking to break new ground to demonstrate the strategic ROI value of diversity and inclusion, or breathe life into foundering diversity initiatives that have little evidence-based value.
What Are Analytics?
Analytics come in different types with a specific focus. They can be defined as follows:
• Analytics is the science of analysis.
• Descriptive analytics tells what has happened in the past and usually the cause of the outcome.
• Predictive analytics focuses on the future telling what is likely to happen given a stated approach.
• Prescriptive analytics tells us what is the best course of action.
Descriptive diversity analytics can help us understand human capital challenges and opportunities in utilizing a diverse workforce, whereas predictive diversity analytics helps us to identify investment value and a means to improve future outcomes from diversity interventions and initiatives.
Companies struggle with evaluating whether their programs meet business needs and whether they are worthwhile investments. Reasons given for not measuring diversity’s impact on business outcomes include statements such as “It is too difficult to isolate diversity’s impact on results versus the impact of other factors” or “Evaluation is not standardized enough to compare well across functions.”
Sound business practices dictate that diversity and inclusion professionals collect data to judge progress toward meeting the organization’s strategies and annual multi-year objectives. The Hubbard Predictive Analytics for Diversity Framework, for example, is a new approach that provides D&I data to executives, including:
• Predicting success of the D&I intervention in the three areas of intention, adoption and impact and measuring to see if success has been achieved.
• Leading indicators of future adoption (transfer of the intervention outcomes) and impact (business results).
• Making recommendations for continuous improvement.
The framework has two major components: 1) predicting, which is before the fact, to decide whether to launch the intervention; and 2) analytics (evaluating), which is an after-the-fact measurement against the predictions.
The beauty of predictive analytics for diversity is that it uses leading measures (intention and adoption) as a signal of results (impact). If the leading indicators are below predicted success thresholds, actions can be implemented to “make adjustments” so that the desired results are realized. Predictive analytics for diversity involves:
• Predicting (forecasting) diversity’s value to the company.
• Measuring against those predictions.
• Using leading indicators to ensure that you are on track.
• Reporting in a business format that executives easily understand.
You can interweave outcomes and leading indicators into diversity interventions during the design and delivery phases to enhance their predictive validity and consistency in achieving sustained benefits. Predictive analytics practice helps diversity and inclusion organizations move from an event-driven function to one that predicts success, measures its performance against those predictions, and is seen as returning significant shareholder value for the funds invested.
Benefits of Predictive Analytics for Diversity
The areas of human capital analytics and “big data” has been around for a while, yet I have found few diversity professionals who are ready to step up to the challenge and opportunities that utilizing predictive analytics for diversity offers. The greatest strength of a predictive analytics approach for diversity is the active involvement of stakeholders setting their own intentions and measurement of adoption rates. This adds a high level of credibility to the practice of forecast diversity outcomes. To reap its benefits, it requires a genuine commitment to implementing a “science-driven,” rigorous approach to demonstrate diversity’s value as a worthwhile business investment.
In addition, predictive analytics practices involving diversity and inclusion must implement measurement approaches based upon “utilization” (making heads count), not merely representation (counting heads). The presence of diversity alone does not ensure progress; the strategic utilization of diversity does. Diversity’s ability to add investment value and improved capability means, at least to some degree, the ideas, creativity, new perspectives, etc., generated from a diverse workforce have been applied and have generated a modicum of benefits. Predictive analytics for diversity and inclusion offers tremendous insights into the value of a diversity and inclusion initiative’s approach and creates “informed-choice” decision-making potential.
A recent monograph published by The Conference Board cited that organizations begin their analytics journey by using data at hand. Most organizations have access to an employee head count that decision-makers can use to manage staff cost. This usage is akin to organizations that simply use head count data to reflect race, rank and gender to ensure ethnic diversity is present at all levels. Other organizations, however, use people data to make real-time staffing decisions to help drive revenue and performance. MIT Sloan Management Review, in collaboration with IBM Institute for Business Value, describes a continuum representative of organizational capability with analytics. They conducted a survey of more than 3,000 business executives, managers and analysts from organizations around the world. Based on “how thoroughly their organization had been transformed by better uses of analytics and information,” the authors segmented respondents’ organizations into three categories:
1) Aspirational organizations that use analytics to justify actions. The focus is primarily on efficiency rather than revenue growth, and they have limited ability to capture and analyze data to make decisions.
2) Experienced organizations that build on what they learned at the aspirational level and use analytics to guide actions. They focus on revenue growth, with less focus on efficiency. While they still lack an understanding of how to leverage analytics for business value, they are moderately skilled at capturing, aggregating and analyzing data to make decisions.
3) Transformed organizations are highly skilled in analytics across functions. They use analytics as their competitive differentiator. These organizations focus more on revenue growth and less on cost than either the aspirational or experienced organizations. Data analysis includes the most rigorous approaches to make decisions using insights to guide future strategies as well as day-to-day operations. According to the MIT study, organizations described as transformed are “three times more likely than aspirational organizations to indicate that they substantially outperform their industry peers.”
Applying this three-tier framework to your organization’s use of analytics for diversity, what level of practice and application does its use of metrics reflect: aspirational, experienced or transformed? Are you ready for the full implementation of predictive analytics for diversity and inclusion as an integrated practice in your organization’s diversity measurement strategy? If so, you will find an informative body of knowledge and insights waiting for your use to drive strategic performance improvement and success for your organization.