Table of Diversity Weekly: Data

I recently had a few conversations that led be back to the importance of Data as we facilitate DEI within our organizations.

The first conversation was with a client, who had an idea they wanted to implement. My response was 'what problem does this solve?' The question wasn't to dissuade my client from implementing this idea, but because I wasn't part of the conscious and unconscious stream of thoughts that led to this solution, I needed help getting there. As my client and I walked through the problem and the proposed solution, my client stops mid-sentence and says, 'I'm not sure that this solves the problem I'm wanting to solve for.' We quickly pivoted and set a goal of collecting and analyzing data to better understand the issue and its root cause.

That's the beauty of being intentional, and having a variety of people go through the process with you to

The second conversation was at a conference where an attendee, who was enrolled in a DEI certificate program, shared that the instructor said that representation should not be a measure of success for DEI efforts. The attendee wanted to know my thoughts on that point of view.

Representation can be a great measure of progress. Notice, I said 'progress' not 'success'. In the DEI world, the two are distinctly different. 'Success' looks at the end result and assigns a value of good or not good. Think about all the declarations organizations made over the last few years: '25% of our leadership team will be Black by 2025' or '50% of our workforce will be women by 2030'. These types of measures look at the end goal. If the end goal is achieved- success! If not, you'll probably never hear about it! 'Progress' also looks at the process we use to achieve these goals- ensuring inclusion and equity along the way. in this instance, representation is a byproduct of the root cause. If the promotion practices are inequitable, representation of the affected groups may be low. Conversely, if the promotion practices are rooted in equity, representation may be high and turnover may be low.

Luckily, our DEI efforts can be 'both, and' not 'either, or'!

Both of these examples illustrate the importance of data, and intentionality behind the data, as we do this work.

How does your organization utilize DEI data to facilitate change? Let's talk about it in this subscriber only issue of A Healthy Dose of DEI!

Read. Watch. Listen

Your Data Initiatives Can't Just Be For Data Scientists- HBR

"Regular people, those without 'data' in their title, are central to all data-related work. Without buy-in and contributions from your company's rank and file, even the cleverest AI-derived model will sit idle and 'data driven decision-making' will just go around in circles. Conversely, costs go down and products get better when people help improve data quality, use small amounts of data to improve their team's processes, make better decisions, and contribute to larger data science and data monetization initiatives. Yet, recent research confirms that these people are missing from too many data programs, limiting the scale and impact of these efforts.

To drive the importance of regular people home, consider the process of completing a data science (big data, analytics, artificial intelligence) project. In general, this requires five steps: understanding the problem, collecting and preparing the data, analyzing the data, formulating the findings, and finally, putting those findings to work. At each step, regular people have a critical role to play- as collaborators, as customers, and as creators of the data used- and there are serious consequences for not including them. Doing each step will depends on regular people."

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How to Best Use Data to Meet Your DE&I Goals-HBR

"In 2014, several large tech companies including Apply, Facebook, Google, and Microsoft started releasing annual diversity reports detailing their workforce composition. The data themselves were not cause for celebration: The reports showed that women made up approximately 30% of the overall workforce and between 15% and 20% of the technical workforce of these companies. Blacks and Hispanics were represented in the low single digits, on average.

However, the move was hailed as a win for transparency and as a harbinger for more progress to come on diversifying the industry. And as behavioral scientists, we would have agreed with that prediction. There's plenty of research to suggest that data disclosure in domains as varied as credit card terms, restaurant hygiene grades, and energy consumption can be a powerful tool to change behavior. In many cases, disclosure affects the behavior of the providers of the information even before the recipients of the information."

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Data Storytelling: The Essential Data Science Skill Everyone Needs- Forbes

"As data becomes increasingly ubiquitous, companies are desperately searching for talent with these data skills. LinkedIn recently reported data analysis is one of the hottest skill categories over the past two years for recruiters, and it was the only category that consistently ranked in the top 4 across all of the countries they analyzed. Interestingly, much of the current hiring emphasis has centered on the data preparation and analysis skills- not the 'last mile' skills that help convert insights into actions. Many of the heavily- recruited individuals with advanced degrees in economics, mathematics, or statistics struggle with communicating their insights to others effectively- essentially, telling the story of their numbers."

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Making Data Mean More Through Storytelling

"Ben Wellington uses data to tell stories. In fact, he draws on some key lessons from fields well outside computer science and data analysis to make his observations about New York City fascinating. Never has a fire hydrant been so interesting as in this talk.

Ben Wellington is a computer scientist and data analyst whose blog, I Quant NY, uses New York City open data to tell stories about everything from parking ticket geography to finding the sweet spot in MetroCard pricing. His articles have gone viral and, in some cases, led to policy changes. Wellington teaches a course on NYC open data at the Pratt Institute and is a contributor to Forbes and other publications."

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