Diversity, equity, and inclusion (DEI) are important goals for any business, including learning businesses. In the wake of George Floyd’s murder, DEI gained ground, but many initiatives, unfortunately, have faltered or been cut completely in the years since.
Our guest in this episode is Miranda McKie, founder and CEO of McKie Consultants, a strategic consulting firm focusing on diversity, equity, and inclusion and inclusive product design. Miranda believes in the need for evidence-based, long-term-goal-oriented DEI initiatives.
She talks with Leading Learning Podcast co-host Jeff Cobb about the role of data in DEI, how some familiarity with data analytics is increasingly becoming a prerequisite for many jobs, and why we need to think about diversity, equity, and inclusion like we approach climate change.
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Engagements Around Diversity, Equity, and Inclusion (DEI)
Jeff Cobb: [00:01:32] Diversity, equity, inclusion, obviously a big focus right now across the business world, across society in general. What does that work typically look like for you? Can you tell us, if there are typical engagements for you, what do those involve?
Miranda McKie: [00:01:48] Yes. So we really focus on three things. The first being what we call design thinking and training. We do a lot of work with organizations and support them on getting people upskilled on concepts of diversity, equity, and inclusion, and we use innovative techniques to do that, which I’m sure we’ll talk today. And, the second piece, we focus on diversity, equity, and inclusion strategy and analytics. So this is really for organizations that don’t really understand where to start, or maybe they have a challenge within their organization that they’re trying to address. What we do is come in and support organizations in developing out their strategic plan as it relates to, obviously, diversity, equity, and inclusion, but making sure that intersects with all of the other different business domains within the organization.
Miranda McKie: [00:02:35] And we like to take an analytics approach. We don’t do diversity, equity, and inclusion based on gut feel, but really evidence-based work. And then the last area we focus on is what we call inclusive product design, where we work with organizations primarily in e-commerce or SaaS-based solutions (software as a service), and we support them in standing up products that actually meet the needs of the diverse users that are using them. So we manage product teams, we do UI/UX design, and we really help organizations ensure that their products are actually inclusive and will meet their users’ needs.
The Current State of DEI Efforts
Jeff Cobb: [00:03:10] I definitely want to come back to that product design component of this because I think that’s just so interesting and, certainly, the analytics as well because I know that’s probably going to go into the product’s design as much as it goes into everything else you do. But, before we turn to that, you seem like a particularly good person to ask about the overall state of DEI efforts right now because this has been something organizations have been focusing on for a number of years now, forefront of a lot of managers’ minds, a lot of leaders’ minds. How much progress are we making? How well are organizations doing with DEI?
Miranda McKie: [00:03:48] Great question. I would say, unfortunately, I think we could be doing a bit better. After the murder of George Floyd, you saw a huge uptick in everyone focusing now on diversity, equity, and inclusion initiatives. Customers, employees started really pressuring organizations to do something about all of the systemic barriers that have existed forever, but really making sure that they’re holding themselves accountable. So over the past couple of years, you saw a lot of initiatives come out. I think some organizations did have good intentions, but a lot of them, unfortunately, were very much tick-the-box types of initiatives.
Miranda McKie: [00:04:25] And now what we’re essentially seeing is it’s not as popular in news anymore, so a lot of organizations are removing some of those efforts. For example, the layoffs that we saw previously, the previous wave of layoffs primarily hit the diversity, equity, and inclusion space and HR space. Because I think what was happening was we highly sensationalized concepts of diversity, equity, and inclusion and put all of these tick-the-box solutions in place, but we’re not actually looking at sustainable goals and sustainable development. If I give an example, concepts of racism, as an example, have been historically a challenge for many years. You’re not going to eradicate racism in a year. And, when you would see a lot of these initiatives that organizations had, they were completely, one, not based on data, so they would say things like, “We want an equal workforce by the next two years.”
Miranda McKie: [00:05:19] Well, when you look at the pipeline of people coming in, you’re not necessarily going to be able to achieve an equal workforce by that timeframe.
So a lot of the solutions that we saw just weren’t based on evidence. They were meant to tick the box. And then, as soon as attention has moved away from DEI, people have forgotten about those initiatives, which is really disappointing, essentially, that that’s been happening. Yes, I would say the state of DEI could be better. I always challenge organizations to think about diversity, equity, and inclusion in the way that we think about climate change initiatives.Miranda McKie
You’ll hear something like, “We want to be emissions-free by 2050.” Now, what do all of the infrastructure and the things that we need to be able to do to get to that goal with?
Miranda McKie: [00:06:01] For some reason, DEI has these very short-term goals—like I said, “We want an equal workforce in two years.” That’s obviously not something that’s sustainable in a lot of organizations, and that’s setting them up for failure. And then, when they’re not achieving those goals, they’re like, “Well, the program is useless, so we’re going to offboard these employees.” I think what I see in this space is really thinking more sustainably about these goals, and I think, when organizations do that, they’re going to start to yield a lot more impact and not just, again, across DEI but across their entire business domains.
Developing a Sustainable DEI Strategy with Data, Analytics, Education, and Training
Jeff Cobb: [00:06:32] I can see how sustainability would be certainly a problem with an issue, a challenge like diversity, equity, and inclusion. Maybe say a bit more—you mentioned looking at the pipeline, for example, in terms of change happening. Say a bit more about how you’re using data and analytics to inform how organizations can develop a sustainable strategy and also how education and training fit in there because, if we’re talking about long-term change, obviously there has to be some change in the way people think, and that requires training and education.
Miranda McKie: [00:07:04] Yes, a hundred percent. How we think about DEI and how we look at data to help support that is we like to look holistically at your internal as well as external data. Let me just explain that a little bit. Within an HR department, as an example, you have tons of access to data. If you’re not too familiar with the HR space, you have your HRIS, or human resources information systems; you have your applicant tracking systems; you probably have learning and development systems; you have performance evaluation systems; and then not to mention all of the other data across other business domains like sales data, customer success data, et cetera, around employees. That’s all very valuable data points to understand what’s actually going on within an organization.
Miranda McKie: [00:07:48] As an example, let’s say you notice that employees aren’t being promoted at the same rate, so you want to see people of color are getting the same access to promotions as non-people of color. Most organizations don’t actually look at their data to understand promotion data. They’ll make blanket assumptions, and, by doing that, they’re not understanding, well, why is this individual not being promoted? So you want to look at all of those different data sources that you have to actually pinpoint some of those challenges. Under the performance evaluation data, for example, are they being evaluated in a biased way? You can do things by literally using natural-language-processing AI to evaluate that, to see if there’s a gap there. Proportionally, are a specific group of people, and it may be a specific department, not moving up at the same rates as others?
Miranda McKie: [00:08:41] Those are all just examples, but things that you want to look at to really understand what’s the root cause of the problem as opposed to diagnosing the problem without ever understanding it. And that’s what we’ve seen today in DEI. It’s like, “This is the problem—racism or something like that.” And it’s like, “Let’s dive into that and actually see where the barrier exists so we can address that and then create change around that topic.”
Miranda McKie: [00:09:03] And then, on the flip side, in the learning and development space, I find a lot of the learning and development focuses on—I’m sure you see it—unconscious bias training. That’s the number-one thing that people have. Well, we know that just doing, for example, training alone isn’t enough to eradicate some of these systemic barriers. It’s great to inform people on some of the challenges that other people may experience or some of the terms and terminology around what’s going on from a DEI perspective.
Changing mindset is one of the hardest things to change, and it doesn’t happen from a one-hour training session that you do once a year. Valuable, yes. It can help support other initiatives, but it’s not going to change the system.Miranda McKie
So you need to bring in all of these different tools and techniques if you really want to change something and change mindset and behavior that go beyond just training.
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Miranda’s Path to Connecting Technology, Data, and DEI
Jeff Cobb: [00:11:00] There are obviously plenty of consultants out there who focus on technology and data and analytics. They’re certainly plenty at this point. You focus on diversity, equity, and inclusion. I have to say, I’m not sure that prior to this, I’d connected with any who’d really put those two areas together in their work to the extent that you have. So, just more from a personal perspective, personal and professional, let me hear what your path was to focusing on that connection between technology and data on the one hand and DEI on the other.
Miranda McKie: [00:11:33] Yes, for sure. I actually come from a technical background. I worked in management consulting as well for a number of years, worked for tech companies, and my background is actually in data architecture and engineering. Everything in that space that you do is based on data. And, especially when you’re consulting, you can’t present to a client, “You need to invest this much in new data architecture” without being able to show those key data points. I was actually doing a project in management consulting, and I was working with the HR, or talent and organization group as we call it, and they were defining different change management initiatives for employees. If you’re not familiar with change management, essentially, they were implementing a new system, and they needed to train a ton of people on this system.
Miranda McKie: [00:12:19] And it was shocking to me that none of this was done using data. It was all just based off of “I think this person is a champion, so needs this type of training, whereas I think this person is technically sound, and they need this type of training.” They weren’t looking at learning, development, or LMS data to understand where they stood. They weren’t looking at their HRIS data, anything to really be able to say that these individuals would require this type of training to support their needs, and it shocked me. It shocked me that all of our engagements were like that, just based off of gut feel or what someone said.
Miranda McKie: [00:12:51] And so we ended up bringing in the concepts that we use in data architecture and engineering to that change management initiative, and that sprung a whole bunch of work around like, “Oh, what a great idea! Why haven’t we done this before?” and then that expanded into the diversity, equity, and inclusion world. It was the same thing. Everything was just based on gut feel, not evidence-based. And then, what was happening was things weren’t working because we didn’t do enough research in the beginning to be able to say, “This is what employees need. This is what will yield the most value. These are what the outcomes were.” It was just like, “Yeah, let’s do it. We think this will work.” So I worked with these organizations to bring in this data and really focused on the people analytics side of it—how do we gain more by understanding more about people? And then it sprung into this. And, after a while, it was like, “Why am I doing this for this consulting company? I should start my own.” And that’s how I moved into this space on my own.
Common Technology Challenges Related to DEI
Jeff Cobb: [00:13:46] I’ve felt that urge myself in the past. You talked about HRIS systems, LMS systems. When it comes to implementing and leveraging different types of technology platforms or systems—I know this is a place where you have a lot of experience—what are some of the common issues that you see arise specifically with respect to diversity, equity, and inclusion? And then what are some of the steps that organizations need to take to manage those challenges effectively?
Miranda McKie: [00:14:19] So many challenges. One I mentioned already, across an employee lifecycle—just to explain that—you have your recruitment, onboarding, professional development, retention and recognition, and offboarding. And those five areas of an employee cycle tend to have their own systems associated to them. So you’re an HR professional. Most HR professionals don’t have a strong understanding or background in data, how HR was historically taught. You’re now expected to bring in all of these different sources of data, merge them to understand what’s going on with employees. That’s the first problem—the experience. It’s a very difficult challenge to do something like that and start to drive very, very important insights out of that.
Miranda McKie: [00:15:06] The second challenge is that HR data is very, very private. So, even if you’re a larger organization, and let’s say you have a center of excellence for your IT departments, and you want to go to them to help you with this analysis, that is also very difficult to do because you’re dealing with such identifiable data that, obviously, you can’t share with just any type of developer. So there are a whole bunch of privacy things that need to come into play. And the last piece is employees also really don’t trust as much organizations to provide a lot of personal data and self-identify, so there’s that piece as well, where you have data that’s not very accurate because, historically, you didn’t want to share your background or share your caregiver status and things like that because of how it would be treated.
Miranda McKie: [00:15:50] Now HR professionals want to move into this data-driven area of space, and, in fact, it’s being demanded. Every business domain across an organization has to prove metrics. But HR, still, is very, very behind, where they’re not doing that as much because of some of these challenges. I always give the example of HR as like where marketing was almost 10 years ago and maybe even 15 years ago, when marketing would be made up of a bunch of creative people—and marketers are still very creative—but they would be like, “Oh, we’re going to go out into this market.” “Why?” “Because I feel like it’s a good idea.” It wasn’t based on data.
Now, you can’t be in marketing if you don’t have an understanding of data. It’s primarily the main role of how they go out into different markets, sell, et cetera. And that’s this transition period with HR, where we’re getting a new wave of HR professionals that are a little bit more data literate and understand this, and we’re trying to transition people that are in this profession to upskill and learn about data so that they can start to also drive a lot more impactful insights.Miranda McKie
DEI and Product Design and Development
Jeff Cobb: [00:16:49] I think that comparison with marketing is particularly fruitful, partly because, in many ways, I’ve often used marketing as a point of comparison to say look at what’s happened with data and technology. They’re using data to learn about behavior and then to actually do what marketers do to help to impact that behavior. There’s a lot that learning and development professionals can learn from that and, obviously, HR professionals as well. But, also, so far, we’ve been focused mostly internally on what we’re talking about, internal technology systems, internal DEI initiatives, marketing, obviously externally-facing, and, of course, organizations have customers or, in the case of many of our listeners, members that they have to serve. So I’d be interested to hear how are you seeing thinking evolve in terms of factoring DEI principles into how products are designed, developed, and managed?
Miranda McKie: [00:17:47] Yes, great, great point. When we think about DEI, too, we like to think about it in almost three pillars of your main users. You obviously have your employees, where most efforts are focused on, I would say, today. But, exactly to your point, you have the customers that you serve, and then, of course, you have your suppliers, and that’s where supplier diversity comes in as well. A lot of organizations, to your point exactly, focus just on the employee side, and we almost forget that organizations are interconnected. So, if you want to be a sustainable organization that’s really supporting DEI comprehensively, you need to also look at the community that you serve, your suppliers, and how you’re supporting supplier diversity.
Miranda McKie: [00:18:28] And, now, we’re starting to see it a little bit more, where I would say, as an example, a lot of organizations are realizing that from the products that they serve, where they’re starting to bring in DEI professionals to comment on that or even employee resource groups to comment. As an example, let’s say a sales team is going into a new market that they haven’t gone into, and this market is predominantly, let’s just say, Black. Some organizations will start to leverage their employee resource groups as an example to say, “How does this communication that we’re using appeal? Do we think these are the right things that we should be saying? What do you think based on your experience?” They’re leveraging DEI organizations like employee resource groups or professionals throughout the organization to help them in these other areas.
Miranda McKie: [00:19:13] Similarly with marketing, we’ve seen enough marketing campaigns that have gone really poorly because organizations have not done that, where they’ll bring it first into DEI professionals to comment on, “Is this something that we should go to market with? Is this something that we’re considering the community?” And, even from a data perspective, looking at, “Does the employee base represent the community that we’re serving?” So, if we have a customer success line for a specific geographical region and no one from our company works there, will they be able to even serve that user base?
Miranda McKie: [00:19:45] Maybe not. It’s things like that that we’re starting to see organizations care about a little bit more, and DEI professionals, their roles are expanding more into those spaces. Similarly, even with supplier diversity, we’re working with a client, and they run very large events, and they were wondering why they weren’t able to attract some diverse people to these events. Even something as simple as their food options. It’s like you’re going into this very diverse community, and the only options you have are hot dogs and hamburgers. Just think about what other diverse suppliers you can bring in from the community that will speak to these people to show that you actually understand the community that you’re running this event in, as an example, so it speaks to those people. Those are some of the challenges across those areas. And I would say, now, organizations are starting, again, to think about it. This is what really drove our service line around inclusive product design, helping organizations actually get there as opposed to just being like, “Oh, we know there’s an issue, but how do we actually address this?”
Exemplary DEI-Informed Products and Organizations
Jeff Cobb: [00:20:53] It’s so easy to find places where things have gone off the rails. Are there organizations, though, that you think are doing particularly well at this? It doesn’t have to be a technology-based product, but I’d be particularly interested if you had technology-based examples that you could offer.
Miranda McKie: [00:21:07] Yes, I think some organizations that have historically done this well, I would say, I always give the example of Salesforce. They were one of the first organizations to actually invest in diversity, equity, and inclusion before it was popular. Like two years ago, where everyone started bringing in chief diversity officers, Salesforce has been doing that for years, and they did it for a couple of things. One, they realized that people were their largest asset and that, if they wanted to develop a really innovative product that would meet the needs of diverse communities, they needed people to do that. They were one of the first organizations also to publicly share a lot of their metrics internally, set up employee resource groups, and leverage those groups for different markets that they go into. Product development, you see a very, very diverse team. They’ve primarily done that very, very well, especially in the U.S. So I’d give them as a good example. There are also some organizations that have not done so well. And just to give an example of that, where you sometimes see challenges around diversity, equity, and inclusion not being built in with the team, I’m sure you probably use Bluetooth in your car.
Jeff Cobb: [00:22:10] Yes.
Miranda McKie: [00:22:11] Now, if you’re a woman or identify as a woman and have a female-sounding voice, you’ll notice how difficult it is to connect with Bluetooth. And then, usually, the partner or the husband will say something, and it connects automatically. That’s such an example of an exclusive product. What’s happening there is the testing data that they’re using to build a Bluetooth app, for example, is primarily using male voices as opposed to female voices. Same thing happened with Snapchat, as an example, where you take a picture. If you’re a person of color, your face doesn’t show up as much as if you are a person that’s Caucasian because the testing data they’re using is primarily Caucasian people. So those are all examples where, now, you have a product that does not meet—like Bluetooth, as an example—half of your population. Women cannot connect to Bluetooth. They’re not going to use the product. They’re going to be angry about it. They’re going to talk about it online. And that’s now pushing organizations to think about how they’re leveraging testing data, as an example, to make better products.
Challenges in Designing Equitable and Inclusive Learning Experiences
Jeff Cobb: [00:23:14] That has me already thinking about artificial intelligence, which I want to make sure we talk about here in a minute. But, before we get there, or as part of the path to getting there, because it’s relevant for our audience, the product is typically going to be some form of educational experience. That might be an event, a conference, a course, or it could even be less formal approaches like community and mentoring. And I know education and training are a big part of what you do. What do you see as the particular challenges in ensuring that education and training experiences are able to serve diverse audiences and do it in an equitable and inclusive way?
Miranda McKie: [00:23:54] Yes, great question. I think there are a couple of things there. One is, if you’re developing a training program, especially on concepts of diversity, equity, and inclusion, or anything for that matter, I personally think—and a lot of research has shown this too—that adults’ attention span is about 20 minutes in terms of just listening to a consistent lecture. But, for some reason, organizations still like to do the four-hour, lecture-based training, which does not work. You don’t retain that knowledge. So, when you’re starting to think about, let’s say, building a training program, even if it isn’t in the diversity, equity, and inclusion space, how can you start to incorporate more techniques to understand that you have a diverse set of learners? I may be someone that loves to read or someone that loves to listen to an audiobook or a podcast, or I may need to draw, for example, to help retain knowledge. So giving people different options when you’re developing out this type of training material so they can find something that speaks to them and not trying to do just a one-size-fits-all approach.
Miranda McKie: [00:24:57] Obviously, budget is always a challenge, and you can’t just go out and develop everything, but really trying to understand your user base so that you’re developing something that will support as much as possible the mass majority of users or attendees in your training program. And there are things you can do very simply. One, ask them, “How do you retain knowledge? What are some of the things that work for you?” You see what you get on previous research. So, if you’re looking at, if you have a learning development system or an LMS system, as an example, understand where do people drop off on different training programs. And then, looking at their demographics, do I notice that women that are caregivers, for some reason, are only doing their training program in the earliest of the mornings? Maybe there’s a time constraint for after work. So really understanding your users by leveraging data will help you develop a more robust program.
Major Concerns and Opportunities Related to the Use of Artificial Intelligence (AI)
Jeff Cobb: [00:25:54] And I mentioned or suggested already that we need to be thinking about how artificial intelligence factors into this because artificial intelligence is going to be increasingly making some of the decisions about how we participate in different types of products, including learning experiences, especially e-learning. I’d love to hear what do you see as the major concerns and opportunities that we’re now facing when it comes to creating products with artificial intelligence, whether that’s using artificial intelligence for design and creation, delivery, or all of the above?
Miranda McKie: [00:26:31] Great question. I’ll start off by saying I think AI is great and something that’s so exciting right now in our space. But, exactly to your point, you need to be very careful with it. And I worry at times that our regulation doesn’t move as fast as technology does, which is sometimes concerning when you see all of the amazing progress. I do truly think AI is amazing in how it’s been moving, but the limited regulation around it…. So let me just explain a little bit. Anyone that knows anything about AI knows that you have to train it. You’re training a model, essentially. It’s based on humans and what humans know. What do we know about humans? We know that humans are unconsciously biased when they build things, and they’re not really necessarily thinking about everything from that perspective.
Miranda McKie: [00:27:18] So you run the risk, especially when you’re building AI models, to have that bias built in. And the challenge there is that now it’s built at scale. Before, you could give the example of you have one bad apple in creating a problem, but, if that one bad apple is building an AI model, then they have a challenge. I’ll give an example. There was a financial services organization out of the U.S., and they were building a mortgage approval process using AI, so, as opposed to going to a mortgage broker and requesting a loan, they were like, “We’ll feed it historical data to be able to train the model. We’ll have people that can help train the model. And then you submit your application; just like that, you get whether you’re approved or unapproved.” So what do you think happened?
Jeff Cobb: [00:28:04] I can already start to see the problems, even as you’re saying it.
Miranda McKie: [00:28:07] Exactly. Historically, who didn’t get mortgages? Black and Latino people in the U.S. So what was happening? Now, you have this AI model that was built that is now creating challenges at scale of these individuals getting access to a mortgage. They retracted the model, fixed it, et cetera. It was a huge challenge. That’s just an example when we’re building AI models and we’re using—because you have to use it—historical data to build them, but that’s some of the challenges when we have these systemic issues that are embedded in things that we’ve done historically.
Miranda McKie: [00:28:40] I always recommend organizations, especially HR practices, because they are very afraid of AI. I would say, for a lot of the organizations we speak to, because of some of these reasons, one thing I recommend, if you are thinking about building some form of AI model within your organization or investing in that, I always recommend that you set up some form of an AI committee or a steering committee that consists of some HR professionals, data privacy, legal, and maybe an ethics professional to really discuss how it’s being built. Where I see that gap is often, “Hey, let’s build an AI model. Hey, developer/data scientist, go build it.” And we’re expecting that data scientist or developer to have all of this knowledge, unbiased, et cetera, just at a whim. They didn’t go to school for that. Why would we expect them to just know that? So we need to put those safeguards in place so that we’re making sure that what we’re building isn’t inherently biased and creating more challenges for organizations at scale.
Personal Approach to Lifelong Learning
Jeff Cobb: [00:29:37] To wrap up this conversation, I’ll pivot us away from AI and machine learning back into human learning and, specifically, your own learning because we are the Leading Learning Podcast. So we always like to ask our guests about their own approaches to lifelong learning. So I’ll just put that out there. How do you approach your own lifelong learning?
Miranda McKie: [00:30:00] Yes, great question. There are, obviously, the traditional ways. I love listening to podcasts and getting exposure that way, as well as reading. But I would say one of my favorite ways to learn is actually getting exposure to groups of people that I would never cross paths with. Just the other day, I was invited to a Russian Orthodox family’s house for dinner, and, given I would never fall or cross paths with someone in that faith, as an example, but it was just so amazing to see their perspectives, different things that they do, to gain exposure into those areas. So I would encourage people to try and get as much exposure as possible. Especially being in DEI, I find there’s a thought and a process of what a lot of DEI professionals think, but I always try to get exposure to the other side of where people are maybe not agreeing with some of those areas so that you can just bring in different perspectives and really be able to think through, “Why is someone not supporting this?” so you can instill empathy on both sides. I’d say exposure is one of the most important things that I think someone could do for lifelong learning.
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