Hacking the Diversity Problem With Big Data Analytics

by   |   February 25, 2015 5:30 am   |   0 Comments

Being transparent about diversity shortfall is becoming de rigueur in the tech industry: Consider Intel’s recent CES announcement that it plans to invest $300 million to improve the representation of women and minority groups inside its organization. In May of 2014, Google revealed that, globally, only 30 percent of its workers were female and that, in the United States, only 2 percent were African American and 3 percent were Hispanic.

This kind of open reporting demonstrates that companies are becoming aware of and concerned about diversity shortfalls. For those who are serious about improving the representation of minorities and women in the workplace, reporting on diversity counts is a good place to start. Another best practice involves comparing the organization’s diversity levels to U.S. Equal Employment Opportunity Commission (EEOC) benchmarks.

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But even organizations that already have an adequate representation of minorities and women within their workforces cannot remain complacent. According to Harvard Business School research, a “culturally diverse social network helps improve creative problem solving,” which is key to innovation, particularly in a turbulent, competitive market. To reap these kinds of benefits, and to sustain adequate levels of representation over time, organizations need to ensure their minority populations are having a positive experience in the workplace.

This requires businesses to go beyond knowing simple metrics (such as diversity counts) or establishing benchmarks. Chief Human Resources Officers, heads of diversity and, indeed, all executives must ask questions related to more complex areas – such as pay or performance bias, risk of attrition, and recruitment and succession planning – which requires a more analytical approach.

The following are three key questions organizations need to ask of their workforce data to improve diversity and how big data analytics supports the uncovering of related insights.

1. Do our minority employees receive pay raises at the same rate as the rest of the population? Hitting the overall diversity ratio target is a commendable achievement, but if a company does not monitor and track the experience of minority groups and women, it will not stay on target for long, or it will struggle to move beyond current levels of representation. Pay disparity is a very visible failure within the company and in the recruitment market.

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For example, to determine whether minority groups are being treated fairly within the organization, businesses should track whether pay raises are being awarded in the same way to minority and non-minority employees alike, and that performance standards are being applied evenly across the diverse populations.

However, determining whether a pay-raise bias exists can be easier said than done. It requires a comparison of rates of change across populations while considering the impacts of tenure, performance, market pay rates, and other key elements. Businesses need to identify whether ethnicity or gender are significant factors in awarding pay increases.

Examining pay-raise bias is best done with a solution powered by an in-memory analytics engine, which is designed to deliver a rapid, multi-dimensional analysis. This is the best way to approach any question involving multiple populations and a wide range of data elements. This type of analysis cannot be delivered by spreadsheets or other typical business intelligence tools without substantial time, effort, and error rates.

Differences in pay rates may be justified, but it is important that business leaders know – and can explain – why these differences exist. Being able to quickly bring together a variety of data from multiple sources helps organizations determine whether pay raises are being awarded to minority groups and women in an unbiased manner.

2. Who is at risk of resigning? If the organization is attractive to diverse candidates, recruitment should lead to an overall increase in diversity. But it is also important to know how turnover of existing ethnic or gender populations compares to that of other populations. If diverse populations do not perceive they are valued equally or treated with respect, there is a real risk that the investment that went into creating a diverse workforce will be for naught.

This is where organizations can use predictive analytics that look at specific gender or ethnic populations and determine who is likely to resign, then invest in HR initiatives to improve the experience of those people at work.

For example, the VP of Culture and Diversity at a Fortune 500 company was puzzled that, despite hiring a more diverse workforce, the company’s minority ratio hadn’t improved. By digging into its full range of data and quickly analyzing results for different locations, teams, roles, tenures, pay grades, engagement scores, and more, the company was able to uncover and pinpoint the exact cause of the challenge: Three specific groups of minority employees were walking away faster than they were being hired. The data revealed that diverse employees in a certain department and role, with a certain age and tenure, were more likely to resign. Having identified the outliers in the data, the organization was able to cost out and implement changes related to management training, promotions, job rotations, and compensation to address these specific groups, and get quick results.

This is an example of how big data analytics can help businesses allocate resources to exactly where they are needed, rather than using a typically very expensive mass approach to improving diversity.

3. How do we develop talent internally, and how can we take action internally to ensure we develop a more diverse workforce? Ultimately, enlightened organizations want to ensure that women and minority groups receive equal representation in all leadership positions. To achieve this goal, organizations need to monitor the diversity of their succession pools. However, the diversity data often sits in one HR system, while the promotion, compensation, or succession status of candidates sits in another. Working with a big data analytics tool that combines these sources into a single data visualization is an advantage when addressing this problem.

For example, non-technical people can share a data visualization that traces the career paths for critical roles, uncover the lineage of leaders and role models, and see how departments have developed over time – taking minorities and women into account – all done without involving a data scientist.

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While there is no way to predict future events with 100 percent accuracy, this gives senior leaders instant insight into an organization’s likely future diversity. By integrating this information into workforce plans, organizations gain the ability to see when and where replacements will be required, and where opportunities will exist for minority groups or women to move forward. They can align the changes and opportunities in future plans with the development and growth needs of succession candidates who are women or from a minority group. At the end of the day, being groomed for a leadership role can motivate people in critical positions, helping to reduce turnover and encourage innovation.

Completing the Insight to Action Loop

Because gathering and understanding the data is such a great challenge for most organizations, HR managers tend to resort to the quick fix: throw money at the problem by increasing recruitment of minorities.

However, with applied big data technologies that leverage in-memory and pull data from multiple sources, the cycle of measure, identify, change, and monitor is easier to handle. This puts senior leaders, Chief Human Resources Officers, and heads of diversity in a better position to do what matters: drive cultural change. Adoption of these solutions to improve management of people leads to payback of 10 or 20 to 1 in terms of lower attrition, better allocation of compensation budgets, higher employee satisfaction and productivity, and, ultimately, the kind of diversity representation that companies wish to earn.

John Schwarz CEO Viseier

John Schwarz is the co-founder and CEO of Visier. He brings to the Visier board more than 40 years of business and IT experience. Schwarz previously was CEO of Business Objects, now part of SAP.

Prior to Business Objects, John held senior executive positions at Symantec and IBM. John is a director on the boards of Teradata, Synopsys, and Avast, and is a former board member of SuccessFactors and Verity.

John also is a member of the Dalhousie University Advisory Board.

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