Why You Need to Attend the Women in Analytics Conference in 2020

Present Beyond Measure Ep. 054

Why You Must Attend the Women In Analytics Conference in 2020

Rehgan Avon is on a vital mission to put women data and analytics practitioners at the forefront of the tech field and help overcome obstacles along the way.

Rehgan is the founder and CEO of the Women in Analytics Conference, an organization and industry event that increases visibility to women making an impact in the analytics space by providing a platform to lead the conversations around the advancements of analytical research, development, and applications.

The WiA conference is becoming an increasingly popular and highly respected annual event that gathers cutting-edge female data science and analytics expert speakers and hundreds of practitioner attendees.

Rehgan is also the Head of Solution at Mobikit, an analytics and automation platform for mobility and connected vehicles, as well as the recipient of Columbus CEO’s Future 50 award.

And, quite appropriately, she is the latest in the line of amazing role models in the podcast’s Women in Analytics Spotlight!

In this episode, Rehgan shares the journey of creating the WiA conference and why it would make an amazing stop on your conference attendee agenda this year!

I’ll be delivering a 6-hour masterclass at the conference so if you’d like to meet me and level up your game, register today!

In This Episode, You’ll Learn…

People, Blogs, and Resources Mentioned

How to Keep Up with Rehgan and WiA:

P.S. – If you’re interested in attending but can’t convince the boss to send you, here’s a great blog post on the importance of company-sponsored professional development. Because, sometimes a gentle, inspiring kick in the pants 🙂

Thanks for Listening!

Thanks so much for joining me. Have some feedback you’d like to share, or a question for Brent? Leave a note in the comments below, and we’ll get back to you!

If you enjoyed this episode, please share it using the social media buttons you see at the left of the post.

If you liked what you heard, I would love if you could leave me a rating or review in iTunes. Ratings & reviews are extremely appreciated and very important in the rankings algorithm. The more ratings, the better chance of fellow practitioners getting to hear this helpful information!

And finally, don’t forget to subscribe to the show on iTunes to get automatic updates and never miss a show.

A very, very special thank you to Brent for joining me this week.

And as always, viz responsibly, my friends.

Namaste,

Lea Signature

EPISODE TRANSCRIPT

Lea: [00:00:04] Hello, hello, Lea Pica here. Today’s guest is here to showcase the analytics conference that needs to be at the top of your to do list. Stay tuned to find out who’s making waves on the present PR measure show episode 54. Hey there, welcome to the fifty fourth episode of the present Beyond Measure Show. The only podcast at the intersection of presentation, data visualization, storytelling and analytics. This is the place to be if you’re ready to make maximum impact and create credibility through thoughtfully presented insights. And today you are here either because you have a hankering to attend a conference that is going to totally level up your analytic scheme and help support a vital mission in our field of tech. Or maybe you just really want to hear what today’s guest secret hobby is during the wild card question. I got your number. So I’m actually going to jump right into this because I’m really excited for today’s interview. It’s the first time I’ve been able to feature a conference with such a valuable mission that I firmly stand behind. And I’ll be teaching there as well. Let’s get to it. All right, everyone, please help me welcome today’s guest. She is the founder and CEO of Women and Analytics, an organization that increases visibility to women making an impact in the analytic space by providing a platform for women to lead the conversations around the advancements of analytical research, development and applications. She’s active and involved in fostering collaboration around emerging analytical methods and technologies. And she is also the recipient of the Columbus CEO’s Future 50 Award. And she is running an amazing conference that needs to be on your radar for 2020, especially because I’m going to be there, too. And quite appropriately, she is the latest in my lineup of amazing practitioners in the women and analytics spotlight. Please help me welcome Reagan Avon. Hello.

 

Rehgan: [00:04:28] Hi. Thanks so much for having me.

 

Lea: [00:04:30] It’s my pleasure. And you know, I’m really excited because this is the first time that I’m featuring a whole event on the show. You know, I have done. I don’t know how many different conferences and events, but this particular one obviously hits really home for me as a practitioner. And I really just wanted to get the word out as much as possible to my analytics community and anyone else who might be listening. So I’d love for you to tell us your origin story first. How did you develop such a passion of of being a data practitioner?

 

Rehgan: [00:05:09] Yeah, I absolutely fell in love with the analytics and data space probably about seven years ago. I was still in college at the time and I was studying industrial systems engineering and I was looking at know my different options of where I want to take that type of degree. And as I started taking more computer science courses, that’s actually when I got introduced to data and analytics, which I kind of thought would have been through some kind of statistics course. But it was it was actually through computer science. So I was looking to to figure out ways that we could apply data and analytics to different use cases and just became absolutely fascinated by. At the time it was. The hype was around distributed systems for four big data. And so I started there and kind of the data management aspect of it. But I started kind of seeing what all of these use cases derived around. What can we do with this data? Applicable to when I was like, wow, for somebody that, you know, likes to to to research a bunch of different things, this was kind of the perfect field that I could dove into. I could look at health care, automotive insurance. I could look at all these different components and industries that we’re all using data to make our lives better. And that was just really fascinating to me.

 

Lea: [00:06:34] Oh, that’s amazing. And then wanting to know all about this amazing conference, I’m so excited I’m finally getting to go. So it’s June 2020. Correct. And, you know, how did how did the Women in Analytics conference come to be and what was the role that you played in that?

 

Rehgan: [00:06:54] Yeah, so as I started getting super jazzed up about about analytics, I spent probably countless hours on YouTube pool watching other conference talks and like trying to learn these different topics. So I went out seeking for my own community in Columbus, Ohio. That’s where I’m located. And I did find a community, but it was a rather non-diverse one. And I was like, there’s gotta be you know, I read all these stats about how many women are in statistics and maybe fields that weren’t considered analytics or data science yet. And like, there’s got to be more people who are interested in this topic. So I started up my own unconference. It was kind of like an evening event and basically just to attract these types of people together. And it was kind of a selfish thing, like I just wanted to meet more women in the field. And I thought maybe 15 or 20 people would show up and like a hundred and twenty

 

Lea: [00:07:52] Wow.

 

Rehgan: [00:07:52] People. And I was like, oh, my gosh. So it’s not just me who’s looking for other people. There’s a hundred and twenty other people who are like, this is a need.

 

Lea: [00:08:03] I couldn’t agree more with that. You know, I would say that I was on mostly male dominated teams and departments, even in the digital side of companies. The one exception was my scholastic team. It was almost all women and it was the most fun job her had in my life. Nothing has compared to that with the way when women support each other and really have each other’s back. I don’t think there’s anything that’s not possible. And you know, I know what you mean. I. I did the keynote at the Digital Analytics Hub four years ago, and I attended their roundtable for The Huddle for Women in Analytics. And that was absolutely the most enlivening conversation that I had attended

 

Rehgan: [00:08:54] Yeah.

 

Lea: [00:08:54] About these

 

Rehgan: [00:08:54] And

 

Lea: [00:08:54] Real issues.

 

Rehgan: [00:08:54] What’s so interesting, I’m finding it’s like this community of women in this particular space where we all kind of care about data and analytics. It’s I’ve never seen a more supportive group and like genuine group of people who are not, you know, so much focused on they’re just really, really focused on the analytics aspect of it that we all really care about it. We’re really passionate about it. We want to share that with each other. And I think that’s so great to just have that as the focal point.

 

Lea: [00:09:25] Yeah, of course. Awesome. So why is that why this event exists? Because women are craving this sort of community with in their professional field. You know what? What are you what are some of the trends or experiences of women that you see that make an event like this so important?

 

Rehgan: [00:09:44] Yeah, I think there’s a couple of things. So, one, we started I was like, okay. This event, drew, in that many people. You know, if we made the conference bigger, would would even more people in Columbus or Ohio join. So the second year we sold out at four hundred and fifty people.

 

Lea: [00:10:01] Wow.

 

Rehgan: [00:10:01] And then, yeah, I

 

Lea: [00:10:03] It’s

 

Rehgan: [00:10:03] Was

 

Lea: [00:10:03] Amazing.

 

Rehgan: [00:10:03] Crazy. It’s was like this huge jump. And then the third year, which was at two thousand nineteen, we we sold out at seven hundred people. And this time we had people from five different countries flying into the United States to attend. And it’s just it’s it was a hypothesis of, you know, is this is this community lake community only needed in Columbus or is this a global thing? And then I started realizing as we started digging into this a little bit more, there’s really a lack of women that exist as kind of thought leaders and people driving the space forward. It’s not that they don’t exist, it’s just that for some reason there’s just a lack of visibility to them.

 

Lea: [00:10:48] They’re not at the forefront.

 

Rehgan: [00:10:49] And so though. That’s right. So that’s why that’s where the mission of the organization kind of turned into it was not only rebuilding a community to help each other out. But it’s really about this visibility component.

 

Lea: [00:11:02] That is so interesting. And you know what? There are a lot of different analytics, digital focused conferences, and many of them are amazing. But what I think you know, what I think the story is here is how unique you made this particular event, that there really isn’t another one out there like that, like this in our field. So I really give you a lot of props for recognizing that need and that desire and then executing so well and getting so much buzz around. It’s amazing.

 

Rehgan: [00:11:35] Yeah. It has been quite the journey for sure, and I think the fact that we’ve kind of broken down the barriers of how how conferences typically select their speakers, because we do have this like you must be, you know, a woman or gender minority in order to speak at the conference. It’s funny because, you know, there’s a lot of conferences that say that they have trouble finding women speakers. And I mean, we’re six months out of the conference and we’ve gotten over two hundred and fifty speaker applications.

 

Lea: [00:12:06] Wow, that’s amazing.

 

Rehgan: [00:12:09] There’s definitely people that know there’s there’s women and gender minorities out there that know their stuff. And they do want to speak and they feel welcome in this community to get up on stage and talk about what they’re working on.

 

Lea: [00:12:21] Yeah, exactly, and you know, one of the lists that I refer to, especially because I’m I’m really trying to round out my podcast show lineup, is there is a list on Twitter from Google Analytics. They made a really big list of women speakers and really well-known practitioners. So I’ll definitely make sure that link is there as well, because that might be a great resource for events that are trying to balance out the ratio of it.

 

Rehgan: [00:12:52] Yeah, I can tell the trend is headed in a good direction because

 

Lea: [00:12:56] Her.

 

Rehgan: [00:12:56] There are a couple studies done on the proportion of women represented in A.I. and analytics conferences and the numbers getting better. Which is great. So I’m optimistic.

 

Lea: [00:13:09] Oh, that’s awesome. Yeah, I’d love if you can send me a link if you have some of those studies. I would love to put that as well. Awesome. So, you know, what are the challenges that you feel women other than maybe a lack of connectivity? What are their kinds of challenges are women facing in this tech based? And I would probably say still male dominated or at least outwardly, like you said, hourly male dominated field.

 

Rehgan: [00:13:36] I think it’s a couple of things it’s you know, a lot of people will lump analytics and I under this kind of tech umbrella, which, you know, it’s it’s really I mean, if you go back to the the origins of it, it’s applied statistics to to data. And the fact that we’ve been able to capture more and more data adds this kind of computational challenge act with scale. We’re having to see more of computer science blend into Internet. So it is more and more umbrella under the technology umbrella. And I think that that, you know, we’re all pretty aware of the statistics of women in general tech companies. And so there’s kind of a culture associated to that. And I and like analytics, are starting to adopt that a little bit. And there’s a huge push for, you know, the large enterprise to start having tons of initiatives around analytics and data science internally. So I think it’ll that’ll help. But a lot of the people, a lot of the companies on the forefront who’ve been successful are kind of these data first companies like you and left. And if we look at those cultures that were established kind of early on in those companies, we think the analytics field tends to adopt some of that challenge of the technology field and what’s already kind of been established there. So I think there’s and that’s another kind of barrier to entry, is that it’s getting more and more computer science based and that we’re starting to some of the companies who have established this and are doing really well or are tech first companies that that

 

Lea: [00:15:18] Interesting.

 

Rehgan: [00:15:18] Have this challenge of a cultural kind of divide.

 

Lea: [00:15:22] Mm hmm. That’s really interesting. Are there any organizations or companies that come to mind for you that are leading the charge in having a better balance in that area?

 

Rehgan: [00:15:33] That’s a good question. I mean, there are some some local companies I can reference that are that are kind of data science first companies there. There’s a company called Upstart, which does a really great job of diversity and reaching out to communities. I mean, it’s really a network problem. It’s like, can you just, you know, find the right community to tap into you, to find your talent? And they and they do a great job culturally setting up their organization. And they’re actually based out of San Francisco. But they have a presence here in Columbus, too. And I think, you know, they’re they’re doing it well.

 

Lea: [00:16:09] Oh, that’s awesome. I’ll have to definitely look that up. I think it’s really important to take note of the companies that are setting the example for that way to see like what are where are they winning, what are the challenges they’re overcoming? How so those. That’s great. And, you know, I. It almost gets me a little emotional because, you know, I think about my mom, who was the only female grad student at Stevens for computer science in her class and went on to be a satellite engineer at Bell Labs. And literally, it was a desert of women. And there I think about a lot of the struggles that she had that were just based on gender role. You know, that’s why missions like this and the fact that times are changing and shifting so much is so exciting.

 

Rehgan: [00:17:04] Yeah. There couldn’t be a better time for an organization like this to thrive and exist. I mean, for all of the women that kind of paved the way to get us to where we are today or where we’re actually seeing some changes, some fundamental changes. I think it’s it’s great.

 

Lea: [00:17:18] That’s awesome. So who should attend this conference?

 

Rehgan: [00:17:23] Yeah. That’s an excellent question. There’s a there’s kind of a misconception and naturally of the Women in Analytics conference that that only women are allowed to attend. And that is not true.

 

Lea: [00:17:36] All right.

 

Rehgan: [00:17:37] So

 

Lea: [00:17:37] That’s

 

Rehgan: [00:17:37] Far

 

Lea: [00:17:37] My next

 

Rehgan: [00:17:37] This

 

Lea: [00:17:37] Question.

 

Rehgan: [00:17:37] From the. Yeah. It is. So the only aspect of the conference that is specific to two women and gender minorities is the speakers and that’s on purpose. So we do this because, you know, the visibility aspect is important, not just other women. It’s important for for men to see this as well. And the content we deliver is know regardless of gender. It’s applicable to absolutely anybody that’s interested in the analytic space. So, you know, I think that perception is needed for absolutely everyone. And so we’re we’re trying to be as inclusive as we can and push towards this inclusivity and involving everyone in these conversations, which I think is is what is going to push us to to co-habitating and and working well together, like all of our scholarships and competitions are even open to anyone to to submit and join. So, you know, we’re we’re just really pushing that inclusivity

 

Lea: [00:18:46] Yeah.

 

Rehgan: [00:18:46] And.

 

Lea: [00:18:46] Talk it, talk about the scholarship. A bit.

 

Rehgan: [00:18:49] Yeah, so there’s quite a few scholarships that we have that we offer. So if you if you don’t live in Columbus, Ohio, and you want to come, we have a flying scholarship, so we’ll pay for your flight to get here and we’ll comp you a ticket as well. And then their student application process. So you as a student, you can get a free ticket. I know what it’s like to be

 

Lea: [00:19:10] Oh,

 

Rehgan: [00:19:10] A

 

Lea: [00:19:11] That’s so

 

Rehgan: [00:19:11] Student.

 

Lea: [00:19:11] Great. Poor.

 

Rehgan: [00:19:13] And then the data science competence or the sorry, the data visualization competition is another way that you can attend, which is is really on our third year of this. And we’ve just gotten so such great feedback about the

 

Lea: [00:19:30] I

 

Rehgan: [00:19:30] Competence

 

Lea: [00:19:30] Am so

 

Rehgan: [00:19:31] That we.

 

Lea: [00:19:31] Excited about the competition.

 

Rehgan: [00:19:33] Yeah, it’s it’s awesome. So what’s what’s funny about the competition as well as the the original thought was kind of like an art gallery for data. And so we wanted both like virtual submissions, which would be like a tableau dashboard. But we also really encouraged people to like think about different mediums that they could express. You know, data visualization could be in a form of a sculpture or some data driven. We

 

Lea: [00:20:01] And

 

Rehgan: [00:20:01] Were really

 

Lea: [00:20:02] I was like, I am not thinking out of the box

 

Rehgan: [00:20:04] We haven’t

 

Lea: [00:20:04] At all.

 

Rehgan: [00:20:05] Had any submissions like that so far. But the concept was that it was supposed to be like a gallery that that people could go through and and observe this these stories through the expression of data. So that’s an opportunity to come as well and potentially win some money. So.

 

Lea: [00:20:22] So the really big question I have is, are you accepting interpretive dance as a medium?

 

Rehgan: [00:20:29] Know you’ve been gone. No one yet

 

Lea: [00:20:32] I think there’s

 

Rehgan: [00:20:32] To be

 

Lea: [00:20:33] Limitless

 

Rehgan: [00:20:33] Determined.

 

Lea: [00:20:33] Potential. Oh, that’s so neat. You know, I I’ve been missing the mark on just entering the competition. And I am so excited about the topic that I found. It’s just the execution part and the actual output where I’m like, oh, I have to figure this out because it ends in five days. But am I allowed to mention what the subject is?

 

Rehgan: [00:20:58] Oh, sure. Yeah.

 

Lea: [00:20:59] So I don’t know if people are familiar with something called the Bexhill Test for movies. So I just learned about this recently where every movie is is rated by someone out there according to three criteria. And it is whether there’s more than one, either one main female character or two female character. I’m blanking on that first one. The second is that two women have a full conversation with each other. And the third is that the conversation is not about a man. And

 

Rehgan: [00:21:33] Oh.

 

Lea: [00:21:33] You’d be shocked how many movies fail this test. It’s incredible. So I managed to extract some help to extract all all data, all of the rated movies back to the late eighteen hundreds to

 

Rehgan: [00:21:49] Wow.

 

Lea: [00:21:49] See how the production of movies have shifted and how the ratio of bexell past back to approve movies is happening. So the data is like the most exciting thing. It’s just the action, like figuring out all the different calculations and visuals that I want to do. But I’m super

 

Rehgan: [00:22:08] Yeah,

 

Lea: [00:22:08] Excited.

 

Rehgan: [00:22:08] That’s fascinating. That’s awesome. I love when submissions are are like they’re exposing some kind of kind of like meta

 

Lea: [00:22:17] Right,

 

Rehgan: [00:22:17] Idea about

 

Lea: [00:22:19] About women and empowerment

 

Rehgan: [00:22:20] Write about women.

 

Lea: [00:22:21] And all that.

 

Rehgan: [00:22:21] It’s so great.

 

Lea: [00:22:22] It

 

Rehgan: [00:22:22] Yeah.

 

Lea: [00:22:22] Was

 

Rehgan: [00:22:23] Know.

 

Lea: [00:22:23] Really fascinating to dove into to see which directors fail more than pass. Which movie

 

Rehgan: [00:22:29] Interesting.

 

Lea: [00:22:29] Studios. You know, interesting stuff around Disney in there. You know, it’s really it is so cool. So I’m so

 

Rehgan: [00:22:36] Yeah,

 

Lea: [00:22:37] Excited.

 

Rehgan: [00:22:37] That’s awesome. I did.

 

Lea: [00:22:39] So by the time this airs, it might actually be out for four judging and everything. But it’s just an amazing thing to participate in. Maya, my mentee that I’m mentoring through the Digital Analytics Association, I’m helping her with her entry as well. So we’re both really psyched about it.

 

Rehgan: [00:22:56] Oh, super cool. Yeah, we get we get some serious like there are some really emotional submissions we get sometimes too. They’re very personal to that person’s story. And those are always really interesting. We do like a full anonymous assess assessment of all of the submissions. And and then we do have the live voting on our Web site for for the top five to to come to the conference. So there’s a couple of rounds that they’re asked to get through. But there’s the submissions are usually just so interesting to look through.

 

Lea: [00:23:27] Well, I think it’s so important to have these outlets simply for participation, because the more opportunities you have to practice and sometimes it’s just really fun to not look at your own company’s data for

 

Rehgan: [00:23:42] Oh, yeah.

 

Lea: [00:23:43] The umpteenth time, but actually pursue an analysis that feeds into a passion that you have. I think it’s really amazing that you’ve created that forum as well.

 

Rehgan: [00:23:54] Yeah. Well, I think part of it was like, how well rounded can we get about this word analytics because we know that such a broad term. So like when when we talk about it, people are like, so what do you include in that? Is it data science? But we I mean, we try to hit everything, which is where this competition kind of sprouted from.

 

Lea: [00:24:14] That’s awesome.

 

Rehgan: [00:24:16] Yeah.

 

Lea: [00:24:16] So I wanted to go back to something you were talking about with being inclusive with men, which I think is so important. I am not Team Woman. I’m definitely team human. And I would imagine that the men that are going to attend or be attracted to this conference are going to be ones that are really invested in helping to support both sides of the equation. So how’s that been in terms of the men showing up for this?

 

Rehgan: [00:24:44] Yeah. So we had. And I think because it’s kind of a less tension length eco system and environment, it feels more welcoming. And we did some surveys as well because of course we want to track how well we’re doing in this department. So we did a survey last year and one hundred percent of the people that filled out the survey. They were both men and women and even gender minority all mentioned that they felt accepted and welcomed at the conference, which is super, super important to us. We you know, we tried to establish that that pretty clearly upfront, that if you’re gonna be there, you know, we expect everyone to be welcoming of everyone. But it’s interesting because I’ve gotten some feedback, too, where people are like, I totally forgot I was at a women and analytics conference just just like the content is so good.

 

Lea: [00:25:36] It was

 

Rehgan: [00:25:37] And

 

Lea: [00:25:37] Just

 

Rehgan: [00:25:37] Like,

 

Lea: [00:25:37] A great conference.

 

Rehgan: [00:25:38] Yeah, it’s just awesome at a conference.

 

Lea: [00:25:40] It’s great.

 

Rehgan: [00:25:40] And I I forgot that all the people on stage were women.

 

Lea: [00:25:44] Huh? They’re

 

Rehgan: [00:25:46] So

 

Lea: [00:25:46] Just people.

 

Rehgan: [00:25:47] Which

 

Lea: [00:25:47] Yeah,

 

Rehgan: [00:25:47] Is great. I mean, that’s

 

Lea: [00:25:49] Yeah.

 

Rehgan: [00:25:49] That’s also pushing towards what we want. Right. This kind of normalized view,

 

Lea: [00:25:54] Gender blindness.

 

Rehgan: [00:25:54] We’re trying to normalize this view of women talking about critical aspects that are going to affect our society, like how legislation is going to form or artificial intelligence and and how we’re not going to get left behind and representation when when companies are making critical decisions about implementing these algorithms. So I think just normalizing that view is is a huge part of it. But we’ve gotten the second year we had about twenty five percent of our attendees were men,

 

Lea: [00:26:24] Wow,

 

Rehgan: [00:26:24] Which was awesome.

 

Lea: [00:26:25] That is amazing.

 

Rehgan: [00:26:27] Yeah,

 

Lea: [00:26:27] Wow.

 

Rehgan: [00:26:28] I think the biggest barrier we we had is through marketing. We tried really hard to let everyone know that you can absolutely attend. So so we’re still navigating that.

 

Lea: [00:26:41] I would imagine that word of mouth is a big part of that. You know, if FIFA, an organization, maybe has a speaker in the lineup or has women interested attending that they’re going to be like, guys, this is the this is the place to go. We really want to show up.

 

Rehgan: [00:26:56] Absolutely. I think there’s there’s the baseline motivation for men to obviously want to support this initiative, which I think is great. But then there’s also the motivation that they have personally, which is just to obtain more knowledge in this space. They care about. So I’m hoping with the double motivation there, can Kit

 

Lea: [00:27:14] Huh?

 

Rehgan: [00:27:14] Work in attendance?

 

Lea: [00:27:16] That’s amazing. So

 

Rehgan: [00:27:17] Yeah.

 

Lea: [00:27:17] What kind of roles and organizations can people expect to network with their.

 

Rehgan: [00:27:23] That’s a great question, too. So the demographics of people that attend we have I think majority is it falls under the category of like analysts or data scientist.

 

Lea: [00:27:35] Mm hmm. Okay.

 

Rehgan: [00:27:35] We have a decent amount of engineers that attend as well. I think that’s mainly due to the fact that a lot of companies who are, you know, tinkering with analytics and machine learning still consider their employee to be labeled as an engineer, even though they may still be doing a lot of stuff in the analytics realm. And then we get a decent amount of kind of the director sea level folks in attendance also.

 

Lea: [00:28:01] Oh, OK,

 

Rehgan: [00:28:02] Yeah,

 

Lea: [00:28:02] So more

 

Rehgan: [00:28:02] We

 

Lea: [00:28:02] Stakeholder

 

Rehgan: [00:28:02] Have

 

Lea: [00:28:03] Level.

 

Rehgan: [00:28:04] Yeah, we do have a strategy track

 

Lea: [00:28:07] Call.

 

Rehgan: [00:28:07] That we that we absolutely love and we usually put that one on the main stage. So even if you feel like, OK, I’m not super technical, I don’t know if I should go to this. We have tons and tons of kind of higher level strategic conversations around analytics as well.

 

Lea: [00:28:25] That’s awesome, it sounds like you’ve really tried to think of a really nice variety of different types of attendees, but focus on the issues that they’re going to care about the most.

 

Rehgan: [00:28:38] Yeah, for sure. And we’ve gotten and we’ve obviously collected and analyzed data of what people want to hear about. So.

 

Lea: [00:28:46] Here, of course, are Analects.

 

Rehgan: [00:28:49] We factor

 

Lea: [00:28:49] Shocking.

 

Rehgan: [00:28:49] That in, of course. And so, yeah, we hope, you know, we’re touching on everything. I think one miskin another misconception is, is around these topics of data and data management. And in supporting infrastructure, we also touch on topics of data as well. So

 

Lea: [00:29:05] Mm hmm. So.

 

Rehgan: [00:29:06] It’s fairly broad.

 

Lea: [00:29:08] That’s and it’s awesome, it’s broad, but it also feels like it’s specific enough where it’s not all over the place feels focused. So who is in your lineup that you’re excited about? Can you share any names?

 

Rehgan: [00:29:23] So many, so we’ll have a total of around forty seven speakers this year.

 

Lea: [00:29:29] Nice.

 

Rehgan: [00:29:30] So that’s very exciting for us. We have a V.P. from from IBM. pre-Games Ritika got her and she is awesome. Not only is she like her background is as astounding, but she’s also a huge advocate for women in the intellect space. And she’s been you know, she discusses that publicly, which I think is great. We also have a doctor for all as well who was on the team that essentially generated that first image of a black hole ever.

 

Lea: [00:30:03] Oh, my gosh.

 

Rehgan: [00:30:05] Now she’s

 

Lea: [00:30:06] That was like

 

Rehgan: [00:30:06] A keynote.

 

Lea: [00:30:06] The most exciting thing that happened that week.

 

Rehgan: [00:30:09] Yeah, I mean, she. So she’s keynoting this year and I am. I mean, from the conversations we’ve we’ve had just about, you know, forming kind of the center core of the talk. It’s it’s so much of data science has come out of her space that no pun intended, but

 

Lea: [00:30:33] Good one.

 

Rehgan: [00:30:33] Has come out of the area that she works in. And I just want to hammer home this idea that that we as an organization is assessing the types of technologies they need to solve these problems, that there is a very strategic way to do that. So typically there’s like a hype cycle about new technologies that get, you know, popularized like, for example, like neural networks.

 

Lea: [00:31:01] His.

 

Rehgan: [00:31:01] And so people just kind of want to use these tools for four tons and tons of use cases that may not be applicable. So she’s gonna kind of walk through that process of assessing

 

Lea: [00:31:11] Oh, well.

 

Rehgan: [00:31:12] Algorithms and methods and technologies based off of your situation. And so she’s gonna use kind of the black hole image projects as the example.

 

Lea: [00:31:24] I’m

 

Rehgan: [00:31:24] So.

 

Lea: [00:31:24] Going to be like front with popcorn and like making a mess.

 

Rehgan: [00:31:28] I got so many questions for her. There’s there’s a. The main panel discussion is gonna be super interesting as well. We’ve got there’s someone we’re still locking in. But but I’m I’m optimistic. We’ll get her. But essentially it’s around this this topic of how law firms are adopting this strategy around A.I. when it goes wrong. So how do you like what kind of method do you apply in assessing the situation where, you know, maybe you were sold an algorithm and it didn’t work as you thought it might? And so is that a communication issue between the vendor and the person that botnets and there are real implications by using it. And so I think that there’s from a from like law perspective, there’s still a lot that we need to figure out who’s liable for some of the outcomes that happen through these kind of like blackbox algorithms.

 

Lea: [00:32:32] Of course. I mean, ice must be the Wild West for law.

 

Rehgan: [00:32:38] Totally.

 

Lea: [00:32:38] It’s like bleeding over the cliff edge. And that’s great that you’re incorporating something that’s such a such a relevant topic right now, because I would imagine lawyers are tearing their hair out over trying to state not even stay ahead, but just keep up.

 

Rehgan: [00:32:57] Yeah. And a lot of, you know, state level government. They’re still looking at data privacy policies. I mean, we’re still on this topic of, you know, what’s going to work in this kind of data privacy aspect as well. So even going back to just data capture and storage and where is it set and who owns it and who’s allowed to do what with it? You know, it’s still a huge topic of discussion as well.

 

Lea: [00:33:24] Yeah. Oh, man. I’ve a feeling I’m gonna really enjoy it, huh?

 

Rehgan: [00:33:30] It’s great speakers.

 

Lea: [00:33:31] Great conversations, too. So speaking of which, you know, what other other than just regular sessions, do you have any other kinds of sessions or activities that you have planned that people can look forward to?

 

Rehgan: [00:33:44] Yeah, tons. We’ll have an opening party this year. Which I’m excited about, so we’re doing. We were trying to think of more ways that we could get because we have got all these great applications and we can only, you know, take so many speakers. And so we’re doing poster sessions that people can also apply to. So you can do have an academic poster if that’s something you shouldn’t. We also have, as I mentioned, the data visualization competition is a great way to interact. And then we’re we’re hosting our second annual artificial intelligence showcase.

 

Lea: [00:34:19] Oh.

 

Rehgan: [00:34:20] And so this is an opportunity for companies to get their products in front of an audience. And so typically we see a lot of startup companies interested in this because they’re looking for a product feedback or they’re trying to recruit people who might be interested in helping them develop it. But larger companies are welcome to do this as well. It’s a great way to demo your product to an audience that potentially would want to use it. But the criteria is really around. If you know, the the product is driven by a guy in some capacity or enables creation around it. So you could be a deployment tool. It could be something like data robot that actually does kind of this automated machine learning process. Or it could be a consumer facing product that is using A.I. in some capacity. So it’s fairly broad in that sense, but we have that as well. And then we are working in some actual specific networking events throughout the conference that we’re really excited about this year. It’s new. So we’ll have a genius bar where we’re going to

 

Lea: [00:35:30] Nice.

 

Rehgan: [00:35:30] Experts that are able to basically they’re giving they’re donating their time to us. And you can sign up for a slot like a 20 minutes lot with them to just ask them whatever. So I’m excited for that. And there’s some additional networking facilitated events that we’re going to be doing as well.

 

Lea: [00:35:52] May I also suggest a karaoke competition?

 

Rehgan: [00:35:55] That’s a good one.

 

Lea: [00:35:57] There was one at the Web Quebec conference that I did this fall and in the midst of a total blizzard and a really challenging trip up there, it was like the total high point for

 

Rehgan: [00:36:09] Wow,

 

Lea: [00:36:09] Me.

 

Rehgan: [00:36:09] That’s awesome. I love karaoke. So fun.

 

Lea: [00:36:12] I’m telling you, we should talk after this. Well, I mean, it sounds like you’ve put together such a thoughtfully organized and, you know, attendee oriented event. And I’m just so thrilled. So I’ll be actually giving the workshop beforehand on data presentation. And I’m just so excited to become part of this finally. And so all the links to register and sign up will be on the page for this. And now want to I want to learn a little bit more about you. You know, I know this isn’t your full time job. So what else keeps you busy? Because sure, you’re not insanely busy.

 

Rehgan: [00:36:52] Right. Yeah. This I think some people come to come to come to find out that this is not my full time gig.

 

Lea: [00:37:00] Mm hmm.

 

Rehgan: [00:37:01] Usually later

 

Lea: [00:37:02] It

 

Rehgan: [00:37:02] On.

 

Lea: [00:37:02] Could seem

 

Rehgan: [00:37:02] But

 

Lea: [00:37:03] Like it.

 

Rehgan: [00:37:04] Yeah, I absolutely, you know, I’m a practitioner of this. And that’s what drives me and keeps me passionate about building a community around it is I also get to benefit from this community. So it’s great just the amount of people I get to meet. But my my background is really in this. You know, for a long time I’ve been focused on how are we implementing machine learning and a large scale. And so what is the underlying technology look like with all of these, you know, data management systems and these tools that are both open source and proprietary around and generating machine learning models? How do we kind of come to a common ground of assessing these and deploying them? And so that’s where I spend a decent amount of my time was really around the technical challenges, but also the kind of the organizational challenges that that these large enterprise companies are facing around just getting their models to be used.

 

Lea: [00:38:05] Yeah, sure.

 

Rehgan: [00:38:07] So I spent years working on that problem. And recently I’ve started a new venture. I’m working for a startup in Columbus called Mobi Kits and weren’t just looking at essentially telematics data. So data that’s coming off of vehicles and being a minute off of vehicles.

 

Lea: [00:38:28] Oh, that’s so interesting.

 

Rehgan: [00:38:31] So,

 

Lea: [00:38:31] Cool.

 

Rehgan: [00:38:32] I mean, I never I guess you see all of these kind of subtle signs of the change in mobility and how we’re getting from one place to another. But I guess until you’re like hyper focused on it, it is such a drastic change and it’s going to continue to to be very drastic. And so just just the the individual trends of this kind of like micro mobility, where there’s all these ride shares and there’s obviously the line scooters that are moving right now. And

 

Lea: [00:39:03] Yeah.

 

Rehgan: [00:39:03] So Uber and Lyft came into the market as well. We’re starting to see kind of this different change in mentality about how to get from one place to another. And so all the data being emitted off of these vehicles, we need to understand it fully. And that’s that’s the problem I’m I’m helping to tackle now. So.

 

Lea: [00:39:24] Well, you know, that’s an interesting promise. One of those things that I see could have huge benefits. You know, we’re our cars are being charged, driving habits are being tracked by our insurance company.

 

Rehgan: [00:39:37] Yeah.

 

Lea: [00:39:37] And, you know, it’s hilarious to kind of check on the app and see like a very like dubious, like, man kind of face for your

 

Rehgan: [00:39:46] Yeah.

 

Lea: [00:39:47] You’re like, oh, darn. But there’s also the privacy aspect, too. It’s like, you know, the idea that people or data center somewhere know where you’re going or know where you are at all times. I would imagine privacy comes up as a.

 

Rehgan: [00:40:03] Yeah, that’s why a decent amount of innovation actually is happening in commercial. So if you think about all

 

Lea: [00:40:09] Ok.

 

Rehgan: [00:40:09] Of that. Yeah,

 

Lea: [00:40:11] Amazon

 

Rehgan: [00:40:11] Because

 

Lea: [00:40:11] Prime

 

Rehgan: [00:40:11] There

 

Lea: [00:40:11] Drivers.

 

Rehgan: [00:40:12] There’s there’s there’s a lot there’s a lot going on in the commercial space, which is super fascinating. And I think it eventually bleed over into how we personaly it around. But you’re right, the privacy piece is the number one concern there. So. And rightfully so. I mean, there are and there are very specific technical challenges of this type of data as well. And even visualizing it, which I think you’ll appreciate, it’s data that’s spanning over time and space.

 

Lea: [00:40:43] Mm hmm.

 

Rehgan: [00:40:44] So,

 

Lea: [00:40:44] Wow.

 

Rehgan: [00:40:45] You know, we’re moving in different directions over a certain amount of time. I mean, overlaying things like weather and traffic is like non-trivial. So, yeah, those are some of the challenges that we’re tackling on a day-To-Day.

 

Lea: [00:41:00] Well, speaking of time and space, you know, you had mentioned that you’re building this product that is visualizing data with space and time. So can you talk about that a little bit?

 

Rehgan: [00:41:11] Yeah, so we do have. There’s two kind of ideas and all that I’ll mention about this. The first is this idea of like data fusion, which is what I was talking about in order to actually visualize all of these individual kind of independent data sets into one place. We need to join them somehow. And so this idea of joining weather conditions at a very specific time, in a very specific place to you, driving your car. Subvention, very non-trivial. And on top of that is visualizing it. And so, you know, when you think about filters, we can start to filter on geography. And then we can also start to filter on time and see and play play that visually. So we can do like a 3D map on most of different variables of attributes that are happening at any given time and play that over a series of days or weeks. And there’s things that get like pop out at you visually that you’re like, well, that’s super interesting.

 

Lea: [00:42:20] I would imagine.

 

Rehgan: [00:42:21] And so just just trying to get that into this exploratory data analysis process for data scientists who are working with telematics data. I think that’s you know, we’re really focused on doing that well.

 

Lea: [00:42:35] That’s awesome. What was it like? Give me an example, if you can, of something you saw in the state when it all worked and you were like, Whoa! Didn’t expect

 

Rehgan: [00:42:43] Yeah.

 

Lea: [00:42:43] That.

 

Rehgan: [00:42:44] So there are certain areas in in Columbus specifically that you can kind of point to and and be like. Like basically the density of accidents have happened in certain areas. And then if you overlay that with alternative modes of transportation like bus stops, for example, where if you’re, you know, an insurance provider or something, you could say, look, it’s going to be less risky on you to go through this area and you could take the bus if you’re going to at this time of day,

 

Lea: [00:43:17] Wow.

 

Rehgan: [00:43:19] You could just take the bus. Alternatively, because it’s gonna be less risky for you as a driver. And so I think there’s there’s some of that that you see that you’re like, you know, the red area and then you see all the bus routes along that area and you’re like, oh, and then all the businesses that surround that area are like, that’s that’s fascinating. Or a rideshare company that

 

Lea: [00:43:40] Writes.

 

Rehgan: [00:43:40] Can take

 

Lea: [00:43:42] Carpel.

 

Rehgan: [00:43:42] People. Right. So that was really fascinating. There was another instance where you could look at, you know, an analysis of weather and whether or not people were weren’t distracted during a thunderstorm or something. And you can start to see when people get more focused on driving what people feel relaxed enough to where they could look at their phone or text somebody here. So that’s also really interesting.

 

Lea: [00:44:11] That is interesting because you I would think that bad weather would automatically result in more accidents, but it’s true. I’m not focused on anything else except my white knuckle grip

 

Rehgan: [00:44:23] Race.

 

Lea: [00:44:24] Steering wheel, especially with some of the rainstorms we get around here. So. Wow. I love when data just shows you something that counteracts a story that he might have had. That’s so cool.

 

Rehgan: [00:44:36] Yeah, it’s it’s really interesting, I’ve never been very well-versed in the data visualization side, I’ve always been kind of more on the technical implementation. So this is definitely newer to me, but it’s super

 

Lea: [00:44:47] Yeah.

 

Rehgan: [00:44:48] Interesting.

 

Lea: [00:44:49] You all right? Awesome. And then I think you very recently did a TED talk or.

 

Rehgan: [00:44:55] Yes,

 

Lea: [00:44:56] Oh,

 

Rehgan: [00:44:56] On Sunday.

 

Lea: [00:44:57] Congrats. Congrats. So excited

 

Rehgan: [00:44:59] Thanks.

 

Lea: [00:44:59] For you.

 

Rehgan: [00:45:01] So, yeah, that was definitely an interesting experience and there were some kind of meta things that existed in that as well. I mean, one of the that became very obvious to me as I was preparing for it. One of my slides was literally a microphone and like a not equal to Stass because I think, you know, just because you’re really, really talented and technical or maybe you’re an expert data scientists or researcher doesn’t mean you can communicate what you’re working on. Well.

 

Lea: [00:45:32] No.

 

Rehgan: [00:45:32] And so, I mean, I think that’s an obvious statement once you say it. But it’s I think it’s one of the barriers to entry for, you know, people who are working on really cool stuff is just to like to get on a stage. You have to be able to to tell a story. And, you know, we provide free speaker coaching for all of our speakers, something that we do as a conference. And it’s something rare that not a lot of other conferences.

 

Lea: [00:46:00] I know I loved when I saw that.

 

Rehgan: [00:46:03] Yeah. So we do that because, you know, we want access to people who are working on really cool stuff. And if and if you’ve never had to prepare it as a presentation, we don’t want that to be a reason that you don’t

 

Lea: [00:46:18] It’s amazing.

 

Rehgan: [00:46:19] Apply. So and then I personally went through that during this TED talk

 

Lea: [00:46:23] I’m sure because

 

Rehgan: [00:46:24] Speech.

 

Lea: [00:46:24] You have to have a coach, right?

 

Rehgan: [00:46:26] I’m not a great public speaker and I’ve given technical talks before and I love technical talks. Have you been taught, you know, courses on data science and web development in front of a large audience? But when it comes to like telling a story, even if there’s like an emotional aspect to it, I it’s it’s hard. It’s so much harder. And I underestimated that completely. But yeah, the storytelling aspect is so key and just the exercise of going through speaker coaching was a great experience.

 

Lea: [00:47:00] Well, that’s amazing to hear. And I also love that you’re doing that because I think that it’s only going to also improve the conference experience for the attendees. I mean, the speakers are the capital of of each conference. So when you are creating an, you know, a really top shelf experience, not only do the speakers benefit, but the attendees get the most out of the stories that are hidden in those amazing brains, you know?

 

Rehgan: [00:47:28] I know that’s exactly right. And the counter. So the way other conferences do this is they say you must have spoken before and we want links to your talks. Right.

 

Lea: [00:47:37] Yes.

 

Rehgan: [00:47:38] So

 

Lea: [00:47:38] Yep.

 

Rehgan: [00:47:38] And that’s it further narrows the pool of people who can even get through that. So I think there’s a couple of ways to approach it. You can help fix it. You can help solve that or provide resources for people or you can make a criteria that they already have experience with it.

 

Lea: [00:47:57] And then you end up with these very like tightknit clicks of speakers that are the head are the the headlining every conference that you go to, and that can be fun. But it definitely, I think, opens doors for so many people. And it’s another yet another thing I appreciate and it’s so true. You know, storytelling is probably the practice that I’m most passionate about in life in general. And even though it’s like the most ancient way that we humans have of passing down wisdom and entertainment, it is an actual language that you I see it as a separate language. You have to learn how to speak as if you know English, but you want to speak French. It’s just

 

Rehgan: [00:48:43] Higer sent.

 

Lea: [00:48:45] Different mechanics. There is everything from planning the story, the narrative structure, the narrative arc that happens, the rise in the fall to like the visual support that you’re bringing, but then also the confidence that you’re bringing, the trust and rapport you’re building with the audience. It’s just its own its own microcosm, really.

 

Rehgan: [00:49:08] I totally agree. I was at the Ted X Columbus watching some of the talks and one speaker jumped out at me specifically merely because of the way that he delivered the talk. Like the content itself was absolutely awesome, but his delivery was amazing, like

 

Lea: [00:49:27] Here.

 

Rehgan: [00:49:27] Just the way he established confidence and trust with the audience and then conveyed his message. It was. So I agree with you. It’s an art it’s completely an art that that is not technically an intuitive one.

 

Lea: [00:49:40] No. No kind

 

Rehgan: [00:49:42] Yeah.

 

Lea: [00:49:42] Of life parenting. We may be built to be parents, but we do not know the language of parenting

 

Rehgan: [00:49:49] Yeah,

 

Lea: [00:49:49] To be learned.

 

Rehgan: [00:49:50] So this is why we were seeking, you know, people like you did to do the workshops, too, because they storytelling and do to go hand in hand. Like you can’t communicate what’s happening or if you miscommunicate what’s actually happening. There’s a lot of issues with that. And so that’s why we find it so important.

 

Lea: [00:50:12] You 100 percent.

 

Rehgan: [00:50:14] Yeah. So I actually have a question for you.

 

Lea: [00:50:19] Oh.

 

Rehgan: [00:50:20] I am curious, how did you make your way into storytelling? And then my next question would be, you know, if you had to give one one tip about storytelling that that the listeners could walk away with today. What would it be?

 

Lea: [00:50:38] Oh, gosh, do you have a year? Oh, well, first of all, I love when people turn things, turn the tables on me and I’m totally unprepared. It’s so great. Well, it’s kind of funny. It’s not. It was kind of the other way around. I didn’t start started analytics and fall into storytelling to me. I started in storytelling and I fell into analytics and I eventually found where they intersected. So I have a long, deep background in musical theater and dance and classical voice training. But other than that, I was always the kid at the family dinners, always rehashing every story from all of our family trips. And man, we had some whoppers. But, you know, just from a very early age, I was very attuned to the power of telling a story. And I after telling them over and over, I would see the kinds of mechanics and dynamics that made them really compelling. So I had a lot of practice before I came in. And then when I fell into analytics, you know, I had to cut my Broadway aspirations short. But I noticed that we had to present at our data. So the good news was, was that I had the confidence and I was accustomed to being in front of people and speaking, but I would fall totally flat in terms of like keeping people engaged or getting people to take action afterwards, which was really frustrating.

 

Lea: [00:52:17] And then I had this like total eureka moment when I had to do a presentation in Prezi once and realized the whole paradigm that I had learned just from being in the corporate world of very crammed slides with tons of charts and tables and bullet points and checker board transitions and all the exciting PowerPoint features. I just understood that there’s kind of like this silent epidemic of a skills gap in that whole piece. And I’d like put every number I had in my platform in there and just go one after the other. So when I started to learn in terms of visual storytelling and I was like how there are elements that I love from movies and TV shows, that because I’m a huge movie buff, too, I can see how to weave some of these elements into this because it’s one of a meeting is one of the only gatherings we have as a species where there’s no entertainment, really

 

Rehgan: [00:53:24] There’s a.

 

Lea: [00:53:24] Weddings and no party birthday parties. There’s always a form of entertainment bonding, but not in meetings. And I think, you know, I’m not necessarily telling people to tap dance their story. You can try that. But there are absolutely ways to incorporate story structure, anticipation, surprising twists of events, even character roles that are really, really effective.

 

Rehgan: [00:53:54] Well.

 

Lea: [00:53:55] If I had to give one tip, I just actually gave this to my mentee because she was struggling with having like a starting with a giant block of data and trying to find a story in there. And she was like, I don’t know where to start because a lot of times were asked questions, but a lot of times were just asked like, tell us what’s going on. You know,

 

Rehgan: [00:54:20] What’s interesting is the.

 

Lea: [00:54:21] You’re like, oh, what’s interesting is that question is interesting. So what I like to do sometimes is I like to imagine that I am having tea or a drink with the data like I imagined this cube, three dimensional cube of Excel table data sitting in front of me on a bar stool or some coffee shop or something. And I know what the topic of the data is. And I imagine having a conversation like a human to human conversation. And I think what are the questions I would ask this data if it were a real person. So this spectral test data is a great example of this. If I sat across from a person who knew from the last the beginning of the movie industry how women are represented according to these criteria, I’d be sitting there and being like, well, which movie studios kind of maybe are leading the charge in representing women more effectively? Which actors are participating in movies that. Are nots who are the winners and the losers kind of in that camp and like, you know. Would it surprise me with certain directors has. How have things shifted over the last 30 years? You know, have the ratios shifted? Are we trending upward or downward? So this is how I would talk in a conversational capacity if there was a person who knew everything like a Siri. And that’s one of my favorite ways to just get started. And also kind of puts you in a relaxed state. You’re not tense trying to think of something to find. You’re more like, oh, hey, I like how would I. How would I talk about this? Or if someone was asking you about this data set, like what kinds of stuff would you want to answer? You know, like that sometimes can get you over a hump of just seeing the wall, as they call it, when it’s just a bunch of numbers, but actually understanding what the mystery is that’s locked in there. So make sense.

 

Rehgan: [00:56:42] Yeah, no, that makes a lot of sense, and I I’m such a questioning person typically have questions for people, just probably why I found myself

 

Lea: [00:56:51] And

 

Rehgan: [00:56:51] In this field.

 

Lea: [00:56:52] As an analyst, really,

 

Rehgan: [00:56:54] Right.

 

Lea: [00:56:54] It’s kind of weird.

 

Rehgan: [00:56:56] So that helps because when you add that human element to it, it it feels more natural.

 

Lea: [00:57:01] Mm hmm. Exactly.

 

Rehgan: [00:57:02] Yeah. And then translating that into like queries or whatever is kind of the easy part.

 

Lea: [00:57:08] Yeah, and the reason why that’s helpful is because the the recipient of the information is also going to be a human being. So starting with a human question and ending with a human answer is really going to give you a leg up with just a place to start.

 

Rehgan: [00:57:26] Yeah, absolutely. That makes tons of sense. I noticed, too, in my experience doing these types of deliveries to like sea level folks is. I started asking myself, what do they care about? To to kind of blend in. Cause there’s a million questions you can ask. But I’m like, what does this person who’s asking me to do this care about?

 

Lea: [00:57:46] What’s

 

Rehgan: [00:57:47] Like

 

Lea: [00:57:47] Going to

 

Rehgan: [00:57:47] What?

 

Lea: [00:57:47] Make them successful?

 

Rehgan: [00:57:47] My right. Yeah. And

 

Lea: [00:57:49] What’s

 

Rehgan: [00:57:50] I find

 

Lea: [00:57:50] Keeping

 

Rehgan: [00:57:50] That.

 

Lea: [00:57:50] Them

 

Rehgan: [00:57:50] That’s

 

Lea: [00:57:50] Up in a.

 

Rehgan: [00:57:51] Right. That might be. Maybe they’re not telling you something about a project that they’re working on or there’s no context there. So yeah, I think between those two things you can start to filter. You know what? Why don’t you start with. That’s nice.

 

Lea: [00:58:07] I’m finding that one of the most productive questions that I can ask a stakeholder is like it’s got you fired up right now. What’s got you excited right now? And you’d be amazed. Like, that’s a very different question of like, what do you need or what do you want in this? It’s just tapping right into the fi, whatever is stoking the flame at that particular moment.

 

Rehgan: [00:58:30] Yeah, kind of the more systemic questions that may derive some of the questions are ESCO actually asking.

 

Lea: [00:58:36] Exactly. Yeah. Rather than can you give me every number?

 

Rehgan: [00:58:40] Right.

 

Lea: [00:58:40] Give me all the numbers.

 

Rehgan: [00:58:43] That’s great.

 

Lea: [00:59:19] All right. So this is our final question. Think hard, imagine this very plausible scenario. You are scuba diving the mysterious blue hole in Belize when you suddenly get sucked into a vortex that pulls you back to the moment you’re about to deliver your first presentation. What are you presenting about? And what with today? You say to yesterday you.

 

Rehgan: [00:59:44] It’s funny you say that. So I actually did swim the blue hole earlier this year

 

Lea: [00:59:49] That’s amazing, huh?

 

Rehgan: [01:00:03] Yeah. The first presentation that I delivered. It’s interesting. It it brings me back to a class I took in college. And I mean, I guess the first ever one would bring me back to when I was like seven.

 

Lea: [01:00:22] We’re going way back.

 

Rehgan: [01:00:24] Then when I made a case, a financial case to my dad about buying a hamster.

 

Lea: [01:00:30] Oh, if you can find the PowerPoint for that, I would be.

 

Rehgan: [01:00:32] I have I actually have the comments for it,

 

Lea: [01:00:36] Oh, my God. Please send it.

 

Rehgan: [01:00:37] But I will I’ll try to take pictures of it cause it’s really funny. But the presentation in college we were given a task is actually through a lean Six Sigma course and we had to do a whole project in essentially assessing complexity of Starbucks orders and how that impacted like basically cycle times of getting a coffee drink. So

 

Lea: [01:01:06] Wow.

 

Rehgan: [01:01:08] It was a rather small data sets and we were looking for like statistical significance for some of this and essentially trying to display, you know, actions that that a theoretical coffee shop owner would make, too. How many coffee grinders they had and things like that. And so I just remember presenting like the stats, like the P values and like all these like detailed

 

Lea: [01:01:41] Right.

 

Rehgan: [01:01:42] Points and the. And my professor at the time was like, OK, so I know you did the work and that’s great. But like, I have zero conclusions from

 

Lea: [01:01:51] Oh,

 

Rehgan: [01:01:52] This.

 

Lea: [01:01:52] No.

 

Rehgan: [01:01:53] So it was one of those things where I was like I had worked so hard to, like, analyze all the data and organize it. What I thought was interesting and then just take that data and put them on slides. And myself today would definitely advise against that. Because typically people don’t care. And that’s kind of the hardest. They don’t. They care, but they don’t care immediately. And so if they ask, you should have it ready. But the actual is very specific data points unless they’re asking you to back it up and like they want to make sure that you’ve done the work correctly. They don’t want to see that. They want to see the conclusions. And some of the ten times the conclusion is like there’s not enough data. And so I can’t

 

Lea: [01:02:36] Yeah,

 

Rehgan: [01:02:36] Make

 

Lea: [01:02:37] That’s

 

Rehgan: [01:02:37] Collusion.

 

Lea: [01:02:37] A tough one.

 

Rehgan: [01:02:38] It is a tough one. And so here are some suggestions on what’s next or here’s my actual conclusion. I’m making a recommendation. It maybe does even have to be as far as a recommendation. But here’s the data that you could conclude that a recommendation would be to add a few more coffee grinders into the assembly line. And here’s you know, I started thinking about, you know, I would think about like monetary value of that as well. Like an investment for this particular recommendation would be X. And so I think just adding context around business, context around data is what I would go back and tell myself to do.

 

Lea: [01:03:17] Right. That’s incredibly valuable and also in terms of saying what you need to come to a conclusion. You know, like I more. More times than I can count was saying we missed out on this. This was a gap. But here’s what we need in order to make this complete for

 

Rehgan: [01:03:36] Yeah,

 

Lea: [01:03:36] The next time around, which.

 

Rehgan: [01:03:37] For sure.

 

Lea: [01:03:39] Wow, that is so interesting. And, you know, I I draw a lot of comparison to major love stories like Game of Thrones and, you know, Star Wars and things like that. And when I think about the complexity, on the one hand, practitioners and presenters, we want our audience to know how hard this was,

 

Rehgan: [01:04:03] Yeah. You’re so right.

 

Lea: [01:04:05] How long it took us, how complex all the sets we had to pull. And. And I get that. And, you know, that feeds into a kind of though human core need for significance. Right. Which is a vital need. And I think the reality is, at least in my experience, for the most part, audiences, if they’re watching Game of Thrones, a small fraction of them will care about how they animated the Dragons and made so many people, made some eight deaths look so plastic and all of that. But for the most part, they just want to know what happens in each episode. They want to hear the actual story.

 

Rehgan: [01:04:45] Yeah, that’s right. And I think there’s as you mentioned, it’s it’s hard to convey like the technical details and work that goes into getting these conclusions and like this is and you know, a lot of times people are trying to justify this is why it’s taken us three or four months

 

Lea: [01:05:04] That’s

 

Rehgan: [01:05:04] To do

 

Lea: [01:05:04] A good

 

Rehgan: [01:05:04] This.

 

Lea: [01:05:04] Point. Mm hmm.

 

Rehgan: [01:05:05] And so it’s there’s there’s some benefit in that to just help set expectations for the future or at least identify ways that you could have potentially drawn a conclusion conclusion quicker. But aside from that, probably probably too much detail.

 

Lea: [01:05:24] Tell your boss. Put it in your review, huh? Well, that’s that’s wonderful advice. And part of the fun of that question is hearing what people were first presenting about. It’s just so fascinating to see the journey they’ve been on. Well, Reagan, our time has run out. But I really enjoyed this conversation. Got me fired up. I about the conference and also just about what’s happening in the industry. And I’m sure the listeners are, too. So why don’t you let them know where they can keep up with you?

 

Rehgan: [01:05:59] Yeah. Hundred percent. So with women and analytics, you can find us on women and analytics dot com. So that’s our that’s our Web site. And you can find all sorts of information about us on there. We also have a Twitter and LinkedIn, which you can find through easily through the Web site as well. And we also have a Facebook, so you can

 

Lea: [01:06:22] Oh,

 

Rehgan: [01:06:22] Find

 

Lea: [01:06:22] Nice.

 

Rehgan: [01:06:22] Us

 

Lea: [01:06:23] I knew that.

 

Rehgan: [01:06:24] An Instagram now

 

Lea: [01:06:25] Hoo!

 

Rehgan: [01:06:25] As well. You’ve done a huge marketing push this year.

 

Lea: [01:06:29] That’s awesome.

 

Rehgan: [01:06:31] You can follow us on on all of those social media accounts. You can find through our Web site.

 

Lea: [01:06:35] Yeah. Awesome. Well, all of those links, registering, competition, scholarship, all of it will be on the show notes page for this episode. And, you know, do what you can. Clear the play. Ask your boss whatever you need to do to attend this conference next June. Reagan. I’m so honored to be a part of this. And I just really want to say that I really see you for the contribution that you and your organization are making to this field for women and for men.

 

Rehgan: [01:07:05] Oh, great. Thank you so much. I’m so glad we got to have this conversation too and catch up because we’re still six months out.

 

Lea: [01:07:13] Huh? Exactly.

 

Rehgan: [01:07:15] Yeah.

 

Lea: [01:07:15] We plan ahead, as analysts write.

 

Rehgan: [01:07:17] Yeah, we’re very fortunate to have you participate. Thanks again for inviting me. This is great.

 

Lea: [01:09:07] Wow, what a fun and enlivening session. Well, I hope by now you’re giving serious thought to attending the Women and Analytics Conference this year. I’m so excited I can finally play my part as well. And I absolutely love to meet you. Clearly, this is a can’t miss event. So to catch all of the links to register for the conference and resources mentioned in this episode, please visit the show notes page at Leha Peak AKAM Slash 0 54. I would love if you could leave me a comment or suggestions because I want to hear about the challenges you face when presenting information. And if you like what you’ve heard, I ask please to take one second and hop on over to i-Tunes to subscribe. Leave a rating and maybe even a review. They are so appreciated because they help the show get seen by other practitioners who need this information. And it lets me know that I’m track. And I read my favorite reviews on future episodes. And I’ll leave you with today’s presentation. Inspiration by Mary Wollstonecraft. And that is, men and women must be educated in a great degree by the opinions and manners of the society they live in. All right, so this may not be about presentation, but I’m sharing this quote in the spirit of this episode because I’m not sure I agree with it. You know, I think that in general, men and women are educated by the opinions of matters of the society they live in. And my hope is that we will both be educated by a society that really believes in the support and upholding of both men and women in a way that celebrates cooperation and collaboration, not competition and collusion. Luckily, we have amazing proponents driving that mission of this awakened society like Reagan and others who are creating quality event experiences like WDIA that facilitate just that. That’s it for today. Hop on over to Lea Pica dot com slash 0 54 to register for the Women and Analytics Conference to day. I can’t wait to see you there. Now, I must say. And now I go.

 

Lea is a digital analyst and marketer turned Data Storytelling Advocate. She trains thousands of digital practitioners and consultants in the art and science of impactful data presentation through live workshops, speaking engagements, online courses, her blog and five-star rated podcast, The Present Beyond Measure Show. Lea is also the creator of The PICA Protocol™, her practical prescription for healthy, actionable data stories that inform decisions, spark ideas, inspire action, and make YOU indispensable.