Thursday, March 23, 2023

Data driven Diversity in IT Services

Over the last decade, there has been a quantum shift in conversation in the business world towards discussions about diversity, equity and inclusion (DEI). For example, the number of people on LinkedIn with DEI-related job titles has grown since 2017 from around 5k to up to 200k. The reasons for this shift are many. Social media has enabled marginalised groups to bring social justice issues to general attention. The 2020 murder of George Floyd by a police officer in Minnesota boosted the momentum of the Black Lives Matter movement.

Diversity, equity and inclusion has gone from being a niche issue for social justice advocates to being entirely mainstream and of significant interest to the corporate world. This is for the good: diversity is humanity’s greatest asset, our main driver of innovation, and what keeps us interesting as a species. The question becomes, however, what does diversity look like on a meta-level, in a global capitalist society, connected by technology and the international financial market? These concepts – diversity, equity, inclusion and their newest sibling, belonging – are qualitative and nebulous. The business world favours numbers. This, many business analysts have thought, has been a chief compatibility problem with DEI(b) in the corporate world. How do we measure qualitative concepts quantitatively?

The IT services world has been the one to seek the answers, with considerable success. There has been a rise in tech companies deploying survey tools among their staff to develop metrics and data on who they are. This marks a shift from the past, where companies knew on a deep, intricate, detailed level who their customers are, to the present, where organisations also want to know who they themselves are. This is what we call a ‘data driven’ approach.

Any business seeking to incorporate DEI(b) strategies must now have quantitative data behind it in order for managers to have confidence in their decision making. It is through this data that key performance indicators can be measured. In our study of large tech conglomerates, including Ebay, Paypal, Facebook, Google, LinkedIn and Intel,  our research indicated that what gets measured gets improved upon, and those companies taking a data-driven approach to diversity are more diverse and have higher employee satisfaction than companies taking a more ad hoc approach to DEI.

Why is the IT industry at the forefront of the data-driven shift in DEI? There are several factors at play. Firstly, tech companies are ever expanding. While COVID-19 and worldwide lockdowns led to many industries shrinking, the tech industry by necessity expanded. Remote working meant businesses of all sizes needed the software and resources to connect employees. In a time of recession, the tech industry was still expanding. The global pandemic took place at the same time as the growth in DEI. It makes sense, therefore, that tech companies were early adopters of systems and programs that measured their diversity data.

Secondly, and more contentiously, the tech industry has long been correctly perceived as dominated a single demographic: youngish, male-identifying and white. Issues of sexism, particularly, are often levelled at all levels within these companies at great damage to their reputations. In order to combat public relations disasters, it is not enough to say that efforts are being made – customers, stakeholders and investors require proof.

The merits of a data-driven approach to diversity in organisations go far beyond reputation. Areas for tracking progress on diversity can include and influence such factors as talent acquisition, pay rates and career advancement. Attrition rates and hiring practices can also be monitored. These factors may be measured against a number of diversity pillars – not only gender, which has typically been the most commonly measured metric of diversity, but cultural background, religion, LGBTQ+ status, languages and other demographic elements.

When an organisation has a robust data set and a thorough understanding of their own demographic makeup, not only broadly, but according to level and even pay grade, it illuminates what was hidden, and enables companies to find patterns in their diversity. It also, importantly, allows for an intersectional approach to DEI(b). A ‘one size fits all’ approach seldom benefits many at all.