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Podcast

The Ever-Growing Importance of Data Literacy

Squirro's Podcast: Redefining AI - Season 1

Jordan Morrow is known as the "Godfather of Data Literacy", having helped pioneer the field by building one of the world's first data literacy programs and driving thought leadership. Jordan is Vice President and Head of Data And Analytics at BrainStorm, Inc., and a global trailblazer in the world of data literacy.

He served as the Chair of the Advisory Board for The Data Literacy Project, has spoken at numerous conferences around the world, and is an active voice in the data and analytics community. He has also helped companies and organizations around the world, including the United Nations, build and understand data literacy. When not found within his work of Data, Jordan is married with 5 kids.

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Full Transcript
Lauren:
Hi, everyone. My name is Lauren Hawker Zafer and this is Redefining AI, a tech podcast that focuses on key narratives that help people explore artificial intelligence, machine learning, insight engines, and insight era.

In this episode today, I've been Jordan Morrow. Jordan is known as the “Godfather of Data Literacy”, having helped pioneer the field by building one of the world's first data literacy programs and driving thought leadership. Jordan, at present is the Vice President and Head of Digital Analytics at GreenStorm Inc, and a global trailblazer in the world of digital literacy. He served as the chair of the advisory board for the Digital Literacy Project, and he's spoken at numerous conferences around the world. He's an active voice in the data and analytics community. He has also helped global organizations, including the United Nations, both build and understand data literacy.

On top of all of that, Jordan is the author of two books, Be Data Literate and Be Data Driven. Now, this is an interesting conversation. I'm really excited about today's episode because it covers topics that I am equally as passionate about, both data and educational empowerment. As usual, the title is luminary of the direction that we intend to go in with our guest and thought leader, Jordan, this episode is called, The Evident Need for Data Literacy.

Jordan, it's a pleasure all the way from Miami. Welcome. Let's hear a little bit about you and find out why you're currently so popular in the data literacy field.

Jordan:
Oh, man, popular, yeah, it's weird. I am coming to you from Miami. I'm home base in Utah, but out here for a teaching engagement at a college out here and hoping the hurricane passes me by. But data literacy to me, I've been asked the question, how did you figure this out? What did you figure out? Reality is I just had a nerdy idea. And that was basically, let's teach everyone how to use data and analytics that you don't have to be a data and analytics professional to use data.

I've had a great opportunity to travel the world to speak on it, to work with companies, and organizations around this. It's fun and it's enjoyable. I think the reason it did become so popular as a topic, not necessarily me, but the topic is because organizations truly wanted to be data driven. They wanted to utilize data to be smart and to make smart data driven decisions. The workforce had to get there with that. In order for the workforce to get there with that, we had to have skills to do that. We know there's a skill shortage and all of that, so it just kind of came at this time when organizations really wanted to use data. Not necessarily me that's popular, but the topic itself is one that I feel like organizations are finally utilizing. I think COVID sped it up. I think COVID actually enhanced the need for this or sped up the timeline for full adoption. But it's exciting and I love it.

Lauren:
I can imagine. I mean, there's a lot to get passionate about when it comes to data. You've certainly hit the nail on the head when you're talking about the necessity and also the movement that organizations are starting to embody around wanting to become more data driven in multiple directions. We'll come back to the core focus of organizations, why they want to become data driven, and how this has impacted, per se, workforces and skill sets.

I want to touch upon a key factor that maybe has resulted in the increase of your own popularity. I mentioned at the start that you are an author of two books. You're the author of Be Data Literate and Be Data Driven. What are your key passes on both of those? And are they aimed for the same audiences?

Jordan:
So, it's an interesting question, I think both have absolute merit with everyone in an audience, right? A story can help illustrate the book, Be Data Literate. I'm a trail runner, and I was running in the mountains once, and I came across someone who knew me. It was probably right around the time my first book came out which was in 2021, I believe, just last year. Anyway, she comes up to me and she says, “Congrats on the book.” And says something to the fact that, I'm probably too stupid to read it. What a horrible thing to say about it yourself, right? But also, number two, it misses the point of that book. That book is designed to empower everyone with an understanding around data literacy. Data literacy is a field and an area where everyone has a seat at the data and analytics table. It is not for just for data and analytics professionals or those who aren't. That's really where the essence of that book is. If we were to look at it from target audience, maybe we can say that that book is based on an individual, but at the same time organizations should be reading it and adopting it to get a better understanding.

The second book, Be Data Driven, my publisher wanted to target leadership. I was totally okay with that. When we think about that term, be data driven, it's essentially organizations utilizing data to meet their business goals and objectives. Leadership needs to understand how to be data driven. So does everybody else, right?

I look at it that they might have specific target audiences, but they are designed for everybody to read and get something out of them so that we can compete and be successful with such an amazing tool, power, and technology, which is data and analytics.

Lauren:
I don't want to jump like 10 steps ahead, but do you have to be data literate to be data driven? Is there an inter-connected relationship between both of those concepts?

Jordan:
I would say absolutely. But what I want to clarify with a response like I just gave, everybody already is more data literate than they know. I think we think of data literacy, data science, and data analytics as a certain reach this level, this or that. I think what people misunderstand is that we use data all the time. For example, I really am in Miami. I flew out a day early because a hurricane is hitting the East Coast or projected too. It is supposed to be north of where I am, but given everything going on, I'm checking weather, reading data and information, so I flew out a day early. That's data literacy. That's utilizing data to make a decision. That's being data driven.

You probably have to be data literate to be data driven. But the reality is, everybody already has a level of data literacy. It might not be the strongest or most comfortable people could be in a business setting using business data. But we use data in our personal lives on a regular basis. What I would hope to convey to everybody is you're not data illiterate, right? Your data literate already. Can we now enhance that skill, grow that skill forward, so you are more successful in your job, career, that sort of thing.

So, yes, to your question. But I want to make sure that people understand, they are already there to a degree. They should be proud of that. They should have an understanding that they are data literate to a degree. But can they grow forward and be stronger and better with it?

Lauren:
That's a really nice thought. Let's move back a few steps. We're going to be talking about all things data, data skills, data literacy, technical literacy, and empowerment. I mean, you've just spoken about empowerment there and recognizing that people already have a certain level of data literacy. It's about enhancing that level.

So maybe before we set off, I think it is important to ground the conversation a little bit. And then get our listeners and warmed up contextually. We normally do this, we start our discussions with a quickfire contextualization. So, I give you a start of a sentence, Jordan, and you have to complete it whilst trying to stick with just one to two sentence answers. This is sometimes the challenging part. You ready?

Jordan:
Absolutely.

Lauren:
Nice. What's the definition of data literacy?

Jordan:
Data literacy is the ability to read, work with, analyze, and communicate with data.

Lauren:
What does it mean to be data literate, when you're not a data professional?

Jordan:
I would say it means being confident and comfortable in those four characteristics we just mentioned and utilizing data to do your job better.

Lauren:
Where do we need to measure the data literacy of data professionals?

Jordan:
Wow, there's a good question. I like to start with assessments, assess where you are with your data and analytic skills as a data and analytics professional, find your gaps, and then get on a path to learn how to fill those gaps or to eliminate them as best you can.

Lauren:
Is data literacy just a way to be able to engage in a data driven society.

Jordan:
I think it is a way to engage in a society that utilizes data, but it also needs to go beyond that. The ability to analyze, find insight, help to make decisions, and then communicate effectively with data.

Lauren:
Is data literacy undervalued?

Jordan:
Yes, absolutely. I think we're seeing it come to light and come to life more and more. But I still think that it's either misunderstood or undervalued at times.

Lauren:
From an organizational perspective, what benefits did data literate employees bring?

Jordan:
I would say an empowered and ability to make smarter decisions where we combine the human element and the data element to bring forth better, more effective decisions.

Lauren:
Does an organization need data superheroes to lead by example, and make a workforce recognize the importance of being data literate?

Jordan:
I think they're important, yes. I think a data superhero or evangelist can get people excited and help lead the charge to be a more data driven organization.

Lauren:
Wonderful. Thank you for the quick-fire introduction.

Jordan:
I could have gone a lot more on those, that's hard to keep my mouth shut.

Lauren:
It is very different as well to sort of condense it down to one to two sentences.

Jordan:
Absolutely.

Lauren:
Is there anything then from that introduction that you feel is important to elaborate before we move on?

Jordan:
I think there's two things I would elaborate on. Number one, when we define data literacy, there's a key term in data and analytics that's very popular that we leave out. And that's data science. That's left out on purpose, right? Data literacy is not data science. Does data science have pieces, yeah, like communicating with data. Can we do that better? But we want to make sure people understand we're not trying to turn them into data scientists with data literacy. We just want to empower them with data and analytical skills.

And then number two, one key aspect of data literacy is data skepticism. What I mean by that is not data cynicism, the world does a good job of making people cynical towards data, i.e., I don't trust it, I don't want to use it. Instead, I want to create a world of data skeptics where they question it, they ask questions of the data that is presented to them. What does this mean? Could we have presented it differently? Could we have done this differently? I think that matters. And I think we need to empower people to understand that skepticism more to be just making smarter decisions.

Lauren:
Yeah, I think that would have actually been one of my first questions, what is it that you are contributing to the efforts around the prominence of data literacy? Are you the voice that wants to create the narrative that makes people more aware, that wants to challenge the skepticism? Are you the evangelist of the narrative starter? What is your role?

Jordan:
I would say yes to both of them. I'm not sure how I feel about the nickname, the Godfather of Data Literacy, but it's probably pretty accurate in the sense that I helped pioneer and invented the entire thing. I still teach on it. I still travel. I'm speaking later today about it in a webinar session. Tomorrow at the college I'm teaching about it. So, I still create division, I still like to think, what is the future of it? Maybe that's not the way to say it but writing about it. What's next and needs to come as we build out and continually execute on the vision and create the narrative around it. Not only that, but I built what probably was the world's first full scale data literacy program. I advise on companies, if you will, maybe not advise is the right word, but helping them understand what they need to do, might be the best way to say it, to run a data literacy program.

Now, I don't necessarily consult like I used to do on data literacy programs, given where I am. But like, this year alone, I might have had anywhere from 40 to 50 speaking engagements by the end of the year. It could be even more than that. It's been a great year. We are seeing it happen. But it's kind of all of the above. I love the space. I love working in it. It's kind of my baby. I like to be out there speaking on it and doing more with it.

Lauren:
It's interesting, and what I'm imagining in my head as well, is that obviously you are creating this movement. But what are your parameters? Who is it that you're educated in that sense. Are you educating a mixed demographic? An older generation or younger generations? Who is your target audience in the sense of being able to set a parameter to define a data literacy program that's adaptable and suitable for entire workforces?

Jordan:
Well, it's an interesting question. Every demographic is something I speak to. And given my experience and background and expertise in the area, I have that ability to do it. Like for example, yesterday I did a webinar discussion with an organization (not really an organization but not sure how to describe it). But it had teachers and people from the manufacturing sector. One of the questions is, how do you teach the younger generation about this? Okay, let's answer that. And if we are looking for people who are older in their careers, how can they advance there? So, I don't have one parameter. It depends on the audience that I'm teaching.

So, for example, today I am speaking to an organization and that organization will probably have some advanced people in it, some beginners in data, some people advanced in their career, maybe, some beginning in their career. Tomorrow at the college, I'm speaking to professors, Dean's, and I believe the President's of the school to help the school be more data driven. So, what I would say is, there's no exact parameters. The parameters for me are defined by the audience that I'll be speaking to. And then it's directly the message there.

The reality is, when you think of data literacy, the backbone and skeleton is probably going to be pretty uniform, regardless of the background. And then it is breaking it down to a more customized approach. If it is young children, like next week I'm at an elementary school teaching about data and analytics. Or is it going to be like when I was at the United Nations and talking to people who might be changing the world. So, it just depends on the audience.

This is a great thing to think about from a data and analytics professional, audience matters greatly. And the quality of a data and analytics professional to be able to cater to the audience that you're talking to matters greatly. And so, that's where my parameters go. It is not defined, it's defined by what audience I'm speaking too.

Lauren:
You already mentioned that data literacy is the ability to read, write, and communicate data in context, including maybe an understanding of data sources and constructs?

Jordan:
Absolutely. And so, I think that definition is Gartner's specifically.

Lauren:
That is the Gartner definition.

Jordan:
The way I look at it is it really boils down to the simplicity of, are we confident and comfortable in using data in our jobs? That would mean to your point, do we understand the sources of where the data is coming from? Do we trust those sources, right? Are we able to find the insight to make a decision? It might not be that your role is defined insight, that may not be the role at all. Your role might be to communicate with the professionals and say, “This is what I need to find. Can you provide me the analysis.” And then, you are communicating out. All those things matter, but can we just boil it down for people to get confident and comfortable utilizing data to do their job better.

Lauren:
Do you think the individual is the one responsible for that? I'm asking these questions because obviously at Squirro we provide a wonderful tool that helps people find certain insights. I'm questioning further if we are trying to enhance the data literacy levels of workforces, are we then augmenting capabilities? Do we need technology as well to ensure that people really are data literate? Can people become data literate without technology and obviously you need it to some extent. Where does that fine line lie of being able to use technology and understand data? Who is responsible for educating workforces about the capabilities of the technology and where is the employee or individual responsible?

Jordan:
I think there has to absolutely be a marriage between the tools and technology and the data literacy of the people. Let me start more from, who's responsible for running organizations? Leadership in organizations. I do think there is personal responsibility, right? The organization can put a program in place, but if an individual doesn't want to do it, that's on them. That's not on the organization at that part.

But you brought up something very interesting data literacy to me, part of it is being able to augment with the the tools, right? We don't want to get rid of human element. We don't want to get rid of your hunches, your gut feeling, and of those things. We want to combine those things with the tools and technology and the data. Without a data literate workforce, it's going to be harder to be able to combine those things.

Again, everybody's already data literate. We already use tools to augment us, like our smartphones, an iPhone, a Samsung. Now can we make them comfortable with the tools? I think you can learn data and analytics concepts without tools. But that doesn't mean you know how to practice them and utilize them. I find tools and technology advancing. To your point, Squirro is doing things for us that maybe 10 years ago, we would have had to do on our own. That's wonderful. To me, tools and technologies should augment us and we should augment the tools and technologies. It should be a marriage between the tools. As we are learning concepts and stuff, if there is a tool or technology that comes along that can do it for us, wonderful. Then the data literacy becomes, can I interact with the tool well enough to use what it does to be effective in my decision making. These things go hand in hand. We can't get rid of the tool and technology and we can't get rid of the data literacy. They need to go together. Part of the learning and data literacy, it's going to be the data and analytical skills that are concepts, but then it's going to be, can you filter in the data visualization, can you query something, can you do these things? It's incumbent of an organization to provide good solid training programs, learning programs so that it is not just mandatory training people have to take. It becomes a part of their skillset.

We are right now on a Zoom meeting. That's a tool. And I think people are comfortable with that. Can we make them that comfortable utilizing data and analytics tools as a part of their jobs to do their jobs better?

Lauren:
Is one of the blockers in the development of the capabilities of an individual on a data literacy program, is that a resistance to technology?

Jordan:
Absolutely. The reality is, the number one roadblock for data and analytics success, probably data literacy success, is the culture of the organization. And what you just described, resistance is going to be a part of the culture. It might be individual, it might be a business unit or group, but the reality is we have to overcome the roadblocks and barriers to do that. It becomes messaging and enhancing. If we sit here and say, "We just bought a technology you have to use." There is going to be resistance, I bet. Because that means they are going to have to change. Instead, we are investing in this technology, and you, here is why, this is what it does, this is how it eliminates things from the past in a good way. That is much better messaging. But yeah, if people are resistant to these things, then good luck. That is going to be the biggest roadblock. It's not the tools, data cleansing, any of that. But if people are not willing to do it, your investment might just go down the tank.

Lauren:
And what are some of the best practices that have proven to eliminate these type of obstacles in positive development of data literacy programs and technology adoption?

Jordan:
I think the messaging matters greatly. People don't want an email that says, "You have mandatory training." They don't want that email. Instead, bringing in someone like myself to do a webinar, to get people excited.

I've got one organization, which is interesting, it's a fire and rescue organization for an entire country. And they're going to have me come in, do a webinar remotely, and I think come in person to this country and speak to leadership. Messaging matters. Can we get people excited? Can we help them understand why these matter? What will take place? How we're going to do it? Versus buying a technology, forcing it on people's heads, and hope that it takes. I think that's how we have to do it. Is the messaging bringing in the outside people to just get people excited? Drive the message forward. And to talk about the data superheroes you were talking about, having individuals in the organization, who are excited about this, who can share the message, who can talk about it, and help teach.

And then the final thing that I think can help get doubters on board is create a library of proof of concepts, where data and analytical work was successful. And you have proof points that it was successful. Bring that forward. And by bringing that forward and showing people, then boom, we have something right there that can help us to be successful.

Lauren:
I mean, obviously a lot of that is about behavioral change management as well, taking people through the five-step process of recognizing it, seeing the benefit, being able to implement it, and integrate it into their own daily activities. I have seen it in action myself in smaller organizations, and larger organizations. If an organization is not data drive, using data to support communication, it is one of the most difficult changes to really change that mindset. Is there a methodology or something that organizations, coaches, mentors, or these superheroes can use to encourage that?

Jordan:
I think there's probably a plethora, right? A myriad that we can go out and study. I think what it really boils down to, at least one piece, is it's very incumbent on the person who wants to drive this change on an organization to not just go out and invest in a change management program and think this is going to be it. It's going to take time to truly study it out.

I'm mentoring a person as she starts a company. I recommended a book to her and talking about something today, it's five archetypes of this, and I don't need to go in that detail. But what I think it's missing in change management, this or that, is what I was telling her to do. You need to sit down and study these things, finding the answers to them. You can't just go out to these big companies that run change management and say, “Come change my company?” How many employees love that work, change? How many employees love that word change? How many people love it when we say we're going to change your organization or your culture? They are just jumping at it, right? So, excited. They are not. It is not necessarily the methodologies that exist out there aren't good. I think it's that we don't take enough upfront time to understand the impact that this is going to have to create the army of evangelist who will help support that change to not send emails of mandatory training. I think that is what we need to do. Whoever is running a data literacy initiative in an organization, take a step back, and really study the organization and understand where gaps are, what gaps exist, hold focus groups within the company to learn more, and that's when you've got the objective, want to be data literate driving this program. Number two, let me create a strategy. And as part of that strategy, I'm going to figure out what is happening. And when I figure out what is happening, that's wonderful. Then, number three, I can pick a methodology that fits that. That upfront work might take time. But it's going to hopefully pay dividends in the long run, versus if you go at it so quickly, then you're going to maybe be running a new program in six to 12 months since the first one did not take.

Lauren:
Great advice, yeah. Really good advice. If we move to an alternative thought that circles on the same domain. There are a lot of parents who listen to Redefining AI as well. Where should they start on the road to data literacy, empowerment? I'm a parent myself, and I understand that you are as well, Jordan. It's a question that I often reflect on in my recent own learnings about becoming more data literate. I took a course on query syntax and looked at advanced query syntax. I tried to train my own search behavior. The benefits were wonderful. I mean, I was exploring the internet in a whole new way. And sometimes I think our younger generations are open to changing that behavior. But how do you try to challenge and encourage younger generations to keep an open mind since they are so soaked and absorbed in data and technology and digitalization?

Jordan:
Yeah, let me talk about the the younger generation. I think, as far as parents, we've talked about it, right? You have to be curious, read, study, and I will get back to that in a minute. But when we talk about children, what I find very interesting about kids, I've got five, is they have a natural data literacy built within them. They are naturally experimenting and their curiosity is growing automatically.

Here's an example, this comes from the astrophysicist, Neil deGrasse Tyson. He was at an event in Central Park, I believe in New York. In front of him was a mother and a child and there was a puddle. And what does that child want to do? He probably wants to jump in that puddle. But what does the parent want to do? Guide them around it so they are not dealing with a mess. The moment we guide them around that puddle, we're eliminating a natural experiment for that child. And that's a natural curiosity. Here we're talking about query syntax, you're going on, and you're exploring it differently. That was a puddle. Children naturally do this. To be honest, one of the things we as parents have to do is get out of the way. Allow them to be, of course, within boundaries, if there are safety issues and problems, yes, I get it. I'm not going to let my kids play with fire in my house, right? But if there are natural experiences they can do, allow them to flourish with natural experiments. Help that curiosity. That's the key. That is my first C. I have three C's of data literacy. Number one, curiosity. Children have it and are taking in information. They are asking questions. I know we get frustrated as parents at the number of questions we get at times. I have five kids, I get way too many questions. But they know that I never want them to stop. Even if I tell them to stop at times, right?

But for whatever reason, as adults, we stopped questioning things. You had a natural curiosity and explored the internet differently. We need to become curious again, as little children. Children have it. They are built this way. This is what they want to do. We unfortunately get in their way. And so, it's unfortunate because of that. I'm not going to teach my kids how to query with SQL coding and Python at a young age. That's not what I'm going to do. What I am going to do is let them experiment. And then as they get older, “Oh, you want to learn how to code?” There are simplified games for coding or simplified ways to learn coding and how things work. That's the direction to go with this.

Now, as parents, like you just did, I want us all start naturally experimenting, again, start being curious. If a question pops in your mind and says, “Oh, I wonder how that works.” Don't ignore it, go find out. That is a data literacy mindset. That is figuring things out. And there are books, there are courses you can take, all of that. But we all need to move our way back to this natural curiosity that kids have, that is the way to get them started. And as they get older, we could inject, "Oh, let's teach you some coding, math skills, statistic." But just let them be and not impede the jump in the puddle. As adults, I want you all to find a puddle and jump in it, right? I want us to figure things out again.

Like it's fascinating to me that I'm in the path, on the outskirts of a hurricane. All right, well, let's learn what I can learn here. And my wife asked me this morning, “If they order an evacuation, what is your plan?” Well, to follow where everybody is going, right? It's one of those that we just have to bring that curiosity back and be that data skeptic where we are just questioning things. It's not bad to question things. Children question things always. Answer them. Answer them and give them answers. At times, trust me, you can say stop asking me questions. But I don't get why as adults, we just don't question things. We just take it at face value. And that's it. Yeah, just start questioning it, and being willing to be questioned ourselves.

Lauren:
So, for all listening today, this is the positive challenge of the week, day, or month, whichever suits you. But it's definitely a narrative that should be encouraged and we should follow.

Talking about challenges as well, Jordan, I think your positive approach and mindset has brought you far along your own journey. But you must have came up against challenges? I think that it's always of interest when people find out about unexpected challenges. So, what has been the biggest, unexpected challenge on this journey?

Jordan:
The biggest one for me, and it's not within the building of data literacy, it was I had burned myself out. I burned myself to the ground building this. And I didn't recognize it. It caused depression. I grew to really not want to speak on data literacy. And you don't notice it.

We hear about burnout. We hear about these things and people talking about it. I didn't feel it coming. I would just caution people who are in journeys, to really figure out how to balance and harmonize your life. I had an opportunity recently, it's got to be less than two months, where a friend who's wanted me to partner with him for a long time and at first I said yes. And within like five days, I'm like, “Dude, I've got to back out. It's too much.” I think that learning that word no, is one of the powerful things that anyone can do in their personal life and career journey knowing when to say no so that your health does not get impacted. It impacted me very adversely. People probably would have been shocked to hear, I don't want to talk about data literacy, or I don't want to speak on it. Now, I've got a better balance. At times I don't want to but that might just be because life is busy with five kids and stuff. But you've got to watch it. I think that is the biggest challenge. I love talking to people, doing a podcast like this, I love speaking. I love interacting. But I'm introverted, which is kind of funny in that way. And I'm traveling the world, I'm doing all this, and I think I can pinpoint when it was that my body was like, "I'm done with you."

Lauren:
It was your body that recognized that or more your mind? External sort of motivation, stimulant that made you recognize that?

Jordan:
No, it was my body. I was on a trip for the company I was with in Spain. And I don't like leaving the family regardless, like I just don't like being too far away. And I used to travel quite a bit but I worked remotely. We would balance it with, I was remote even pre pandemic and travel. But I was on this trip in Spain and the anxiety I felt was, I would say at a different level, a different kind. I think my body just shut down. My brain was like, “You are doing too much, and I can't handle it anymore. So, I'm sorry, the switch is off.” And it took a journey to get back to a better place. It took opportunities, it took me being more forceful in my career, if I can say it that way. And it turned out to be better. It took a shock to the system. And so, what I would say to people on that is, if you think you are experiencing it, talk to people, please. To go into depression caused by burnout and having it diagnosed is not an enjoyable experience. I'm happy to chat about it because I think, especially from the male gender, we don't talk about it. And we keep it in. We are seeing that come to life. I think everybody, I don't care what gender or who you are, if you are experiencing things, just find people to talk to.

Lauren:
Thank you for being so open and for sharing this with us today. I would also agree that it's an extremely important and ever more recognized, unfortunately, personal situation that is affecting people in different ways. I have seen it in my own environment. And unfortunately, it went down a different road. But I do think that it is important that people understand that it happens to more rather than less.

Jordan:
And I would say to that, you know, from the outside, it probably looked like I was great, because I'm still traveling, I'm speaking, data literacy is doing well. But you can't just tell by seeing someone, right? Everybody's dealing with something. Just be kind. I get we are going a little more philosophy or whatever here, but it's like, you know, careers are important. And from an economic perspective, at least in the States and probably elsewhere in the world, we're hitting hard times. And it's good to be one of those where people just need kindness, and they need that opportunity to be themselves, and be open. I would also say an opportunity to be mentored, to grow, to figure out balance, and all of those things.

Lauren:
Yeah. It's a wonderful thought. I think it's a positive note that we can end the podcast on as well. Stay positive, to keep a positive approach, an open mind, an open heart, and to be kind to everyone out there.

Thanks Jordan.

Jordan:
Thank you so much. This was awesome. Thank you so much.

Lauren:
Yeah, and I want to thank everyone for listening today. It's been eventful. It's been informative. It's been enjoyable. It's been poignant. It's been philosophical. And it's been inspiring.

host-f
Lauren Hawker Zafer

Host

guest-m
Jordan Morrow

Guest

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The Integration of AI in Life Sciences and Biotechnology Sectors
Podcast
The Integration of AI in Life Sciences and Biotechnology Sectors
You, and Artificial Intelligence
Podcast
You, and Artificial Intelligence
Digital Humans - Creepy or Cool?
Podcast
Digital Humans - Creepy or Cool?