All right. So we're at time. Hello, my name is Caitlyn mcgregor. I am CEO and co founder of Plum. I'm really excited to present to you, advancing diversity, equity and inclusion and how that requires a talent data reset today. I see the people are just joining.So I'm gonna give it about 30 more seconds as I'm sure people are transitioning from other sessions, uh feel free to jump in the chat and introduce yourself and, and give a shout out. It'd be great to know your name and, and where you're coming from and any of your background while we just wait to, to get started. Uh I guess I can start with a little bit of background on me uh before diving in. So, uh as I said, I'm the CEO and co founder of Plumb, where a software is a service platform that works with enterprise companies to help them identify the potential within their workforce and applicant pool. And uh I built two businesses for other people before starting Plumb and I was my own customer. Uh I was a small business owner where I was going through the challenges of hiring new people and I had the ability to pay a very expensive consultant that helped me through the journey of learning how to hire and find better talent.

And I really saw an opportunity to democratize access to this highly predictive data. And so today, I'm hoping to kind of share the lessons learned and really this new paradigm when it comes to evaluating and understanding how to optimize and really set talent up to thrive. And so I see we've had a couple of more people join again, feel free to use the chat, shout out and, and add your, your name to it. And I'm just gonna dive in now into the presentation. So, uh as I said, you know, given my intro, but here's also my, my uh handles in case you wanna shout out on social media, it's great to interact now or after the presentation. And so today's objectives, I'm hoping that we're gonna be able to cover these three things, how to advance diversity, equity inclusion in a meaningful way that really caters to, you know, how to focus on talent decisions using a universal unbiased data set that really quantifies human potential.

The next objective is how to build an inclusive employee experience by seeing employees for who they are and what they're capable of, not just what they've done in the past. And then third, how to create a stronger workforce engagement and retention using, you know, by being able to uncover and leverage what drives and drains each employee. So hopefully at the end of the session, you feel like you've gotten those tangible insights. So right now when we think about talent decisions, they're often made using historical data data that really focuses on where someone has been and what they've done in the past. And this really, you know, is data that's usually scraped from resumes and job descriptions. And it's really about turning those hard skills and past experience into searchable keywords. And the problem with this historical data is that it's embedded with the systemic barriers and biases that dictate access to education, internships and career progression. They really give a rear view mirror judgment on where someone has been, but it's not a valid objective predictor of future success is human potential. Data is about where someone could thrive if just given the opportunity. It uses industrial organizational psychology, which is the science for measuring human potential, which focuses more on those transferable soft skills that have been proven to be four times more accurate at predicting future job success.

And so when we look at again, this field of industrial organizational psychology, we have decades of research that show that when you rely on education and past job experience, it's really a coin toss, it's not predictive of future success. Anything below a 0.2 can't correlate to an actual performance outcome. First is when you start to move up, the, you can look at structured interviews and job trials, these are incredibly beneficial. The problem is, is they don't scale, you can't take 100 or 1000 people and all put them through a specific example of what they would do on a day to day basis. And so it's really hard to get that at volume. It may be good as kind of a final stage, but it's hard to do it at a place where you can come before that education more predictive and removes that bias talent data, which is again, those transferable, soft skills like innovation and communication. They're actually, and you bring in cognitive ability, like problem solving ability, those talents together are actually almost as reliable as those structured interviews and job tryouts. But the difference is it can come at the very beginning of the process, this data can be used on thousands of people.

And because it's a completely new data set and it's so predictive, you're ending up with very different outcomes. And so this is when I talk about, you know, industrial organizational psychology has been able to prove that this data is four times more accurate. So the question is, if it's more accurate, why aren't we using it more? And just to really drive the point home, you know, in the past, if you were hiring somebody to sell phone books, door to door and now your company b and you wanna have somebody sell, you know, phone books again from door to door fine. In that scenario, that past experience, it can transfer from one job to the next and really equal about two thirds of the equation versus their soft skills and what's transferable and make somebody successful from one job to the next. Even if the job is very different, that would only be one third of the equation. But this is 2021. The reality is if you take somebody that was selling phone books and now you have them selling cybersecurity, their ability to be successful in the new environment actually comes back down to those transferable talents, their ability to be persuasive, their ability to execute, to adapt to a new job.

And so two thirds of somebody's ability to predict future success comes down to those transferable talents, like I said, at the beginning, I built three businesses. Now, the first one was in manufacturing, the second one was in educational technology and this one is now in hr tech.

And what's consistent amongst those roles is not the past experience. It's what can transfer from one experience to another. And that predicts perf performance. The other piece of a past experience in education predicts eligibility, but you wanna get into performance, it comes down those transferable elements.

Anybody that's looked at future work research would see this, that's all they spew about is how the shelf life of those hard skills really has been reduced. If you think about five years ago, Ruby on Rails was the hottest paid developer position and now nobody's coding with Ruby on Rails, but somebody's ability to learn quickly, their ability to work well on teams, their ability to think innovatively and be able to learn new code.

Those are all things that will actually predict their long term success. So I keep talking about these talents. What I mean is really what comes down to personality and cognitive ability. What we've been able to do at plum is build every part of our business and technology around the science and industrial organizational psychology, creating that scalable objective data that enterprises need. And so in order to quantify and match people's potential to changing job needs, we need to create a universal talent model that really allows us to understand every single person agnostic of the job, every single job in terms of what are the behaviors that are needed to predict that future success and be able to match them together.

So we are able to look at industrial organizational psychology, the big five personality traits, which are most common things like conscientiousness and openness to experience and agreeableness, words that we don't always use every day, we were able to look at problems solving and social intelligence, how to understand people and basically build those together to create 10 talents that consist of about 32 sub competencies underneath those things like adaptation, communication, decision making execution terms that we're much more familiar with.

And that really gives us this universal framework that gives us a common language to then be able to match people to different roles. And as we talked about before, that gives a different outcome when you start using different data. And so I'm gonna show you a really quick video here of how uh this can be used to create a different outcome. Uh I just need a second because I realized I did not share my screen properly with the sound on. So I'm just gonna take half a second here to reshare my screen. So I apologize for just half a second and hopefully I can, this is will be nice to me and allow me to reshare with audio. I keep forgetting to uh to do that. So hopefully this works now and then my sincere apologies and then I'm just gonna press play. I promise the uh worth waiting for when it finishes loading. Uh All right. So hopefully everybody, as soon as it starts playing, you hear it on your end

as a student, your resume started with a template or you copying your BFF layout. Hell, it might even been your career center that made it. But then it was all about word smithing, reviewing ram check people, looking it over advice, constantly changing it. And for what congrats, you got a sheet of 8.5 by 11 that sums up what exactly exactly we're not looking for some generic over processed, oversimplified templated view of what you've done. We want to know what you're gonna do for real, you who you are as a person. Because here at Kosher Bank, we want scratch that. We need you to bring your whole self to work every day, not 8.5 or 11, you, we hired the authentic you that diversity of thinking with different points of view, key strengths and passion. All of that allows us to solve complex problems for our clients and our people and develop the best products and technologies. OK? Don't get us wrong. All that you gain as a student for your extracurriculars, work experience and volunteering is super valuable and it's helped shape you into who you are. But doesn't that story warrant more than a five second review on a single sheet of paper. Plus you still very linked in starting out in Canada, all of our intern co op and graduate program position no longer require student resumes, cut the bias away and meet us online in person at events or get noticed by applying with your LU profile because at Scotia Bank, we're here for every future.

Mhm So that's an example of uh a large Canadian Bank. They're now starting to expand this into their global recruitment as well. And what's been incredible is just the impact that this has had on diversity equity inclusion uh which will get into right now. So over the last year, what they've seen as a result of removing resumes and bringing in this talent data at the very beginning of the hiring process is they've seen an increase in people of color within their campus hires go from 4% to 10%. And they've seen uh a raise in their hiring from a visible minorities go to 60% when other banks are just pledging to get to 40%. So it's incredible how by just changing the data set, you get completely different outcomes and really get rid of that systemic bias and that exists in that historical data. And so what we're seeing is this need in the market to identify the potential that exists within companies, applicant pools, but also within their existing companies. They're seeing that as jobs are dynamically changing, they really wanna be able to better identify the potential in the organization, they wanna develop that potential and then they wanna match that potential uh to future opportunities.

We're really seeing, especially with, with the pandemic, this need for a lift and shift. How do you really move people that were in a role that may no longer align to the company goals? How do you move them into something they may never have done before but now meets where the business is going. Uh I often will talk about Whirlpool that was a manufacturing company with retail distribution partnerships. And now they really are, you know, a company that is gone online and they've become a software company and a cyber security company and an e commerce company. And so there's this need to lift and shift and realign existing employees into new roles. And so what happens is that when we talk about potential, the data set that's often used in organizations for quantifying potential is often the nine box. So one axis is performance, one axis is potential and it's on managers and executives to arbitrarily place their employees into a box that says whether or not they have potential or not and potential to do a job, they haven't even witnessed them doing because we're talking about them doing something different in the future than what they've done in the past.

And so what it's happening is managers and executives are actually they're demanding objective data to guide these decisions and they're recognizing you no longer have to move somebody halfway across the world to contribute to a new department. So this data needs to scale globally across the entire organization.

And so when we look at that objective data, this is what we're talking about is that by looking at people's transferable uh talents, we're able to then create new paradigms when it comes to talent decisions. So if you have a plum profile, which is how we get everybody's uh ta talents for every single person. If you have that data, every single employee, then you can layer on a leadership framework and identify leadership potential, not just at the top uh 10% of your organization, but across every single employee, regardless of them just entering your organization, you can identify that leadership potential and start nurturing much earlier where you have more diversity in your organization.

You can also look at how to optimize and support individuals but also teams, how managers that are, that are managing team members, they may never have met. How can they look at supporting them based on their strengths and supporting them based on areas they may need support in then how do we take that data and understand how to support people with their transferable talents and support them in new ways in their own professional development?

And then how do you use this data to match people for internal mobility succession planning, workforce planning based on their potential. And so right now, when we look at how talent decisions are made, another place that we look at historical data or static data is often in the job description. So job descriptions hopelessly are out of date. They're not always reflective of the brand new needs. And that data unfortunately is being used by a lot of systems that are resume scraping and then matching to job descriptions. And what we heard two years ago is some of the A I solutions out there that were using that resume data, using the job descriptions and then you the hiring data. What happens is that the A I is a real life story was then spitting out that they were recommending people that were named Jared who played lacrosse and read Harry Potter. And when you can see that when you're just inputting this historical data, what you're doing is speeding up the pattern matching. And unfortunately, what that means is that you're not actually getting a prediction that is job relevant versus human potential data.

When we look at understanding people's innate talents, we also need to under understand what are the behaviors that are needed in the job for somebody to be successful. And so just like KP, I's key performance indicators are a measurement of what you need somebody to perform in moving forward. In order to be successful, you can quantify which behaviors are needed by assessing by taking an eight minute survey managers. And uh job experts can contribute to what are the behaviors that are needed by for success. And so what that looks like is if you understand people's uh in a talents and you understand the talents needed for success in the job, you can now reveal where somebody could be successful. So Maya is an example of an underwriter that for six years, she was a top performing underwriter at an insurance company, but she was really drained out at the end of the six years. And so Maya was able to take her plumb profile, which I'm gonna give you a link very shortly. So you can complete yours if you'd like to know what drives and drains you.

And her report was able to come back and say within our 10 talents specifically drives her and gives her a sense of self worth and really allows her to excel compared to her peers. And then on the flip side, what drains her? And if she spends too much time on those activities, what would eventually lead to burnout. And so Maya was able to use this data to guide her own career path and internally the organization thought, hey, Maya has been an underwriter for six years, let's promote her to be a manager of underwriters. The problem is is that she was only a 63 match based on the behaviors needed for the role. So this role to be successful and it needed to require managing others, which she would be good at decision making, which she would excel. But she'd also have to use a lot of conflict resolution, persuasion and communication, which are all draining. So even though she could do the job, 18 months from now, she would just probably end up being burnt out versus internally. They were looking for product managers. It's a really hot job. It's really hard to find a lot of diverse candidates and it's very competitive. So the salaries to poach people externally gets to be a really difficult for companies. So they were able to look internally and see that Maya was a 94 match.

And that because NAA Na Maya naturally excels with execution, innovation and adaptation. She really ranks in the top 6% of the entire workforce in terms of how well she could do that job and teamwork and communication. Is that also, you know, something that the manager is aware of to support Maya and, and to help with professional development with coping strategies. And so here Maya can now use this information. So she actually got hired and within six months was outperforming product managers that have been hired with 15 years of prior experience from outside the organization, she already had the institutional knowledge already understood the customer. And so when she now got into this job, she was able to use this data as part of the 70 2010 professional development role where 70% of the on the job learning happens through on the job, 20% happens through mentorship and then 10% happens through formal learning. And so her development guide allowed to inform how she could be the CEO of her own career, really understanding how to set herself up to thrive. 20% helped to provide that common language to bring in mentors. And then 10% connected it back to the L MS. So if she wanted to work on conflict resolution, she knew exactly what to work on where she get the highest ro I so as promised, I said that I'd let you go ahead and take your own talent guide.

So please feel free, you know, to take your own development guide, you complete your own plum profile. It takes on average 25 minutes. It's totally free. And at the end you'll see all of your 10 talents. And so I really appreciate everybody's time and attention today. It was great to have so many eyeballs on the presentation. I'll let you go to your next session. But please, uh use dot plumb dot IO slash TG to get your own talent guide and complete your own plum profile and feel free to reach out through email, uh or linkedin and happy to answer any of your questions. Thank you, everybody. Talk to you later. Bye.