Heather Fitzgerald | Building a Data Story

Episode 4 January 11, 2023 00:30:06
Heather Fitzgerald | Building a Data Story
RXA Presents: Real Intelligence
Heather Fitzgerald | Building a Data Story

Jan 11 2023 | 00:30:06

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Hosted By

Anna Schultz Jason Harper

Show Notes

The Real Intelligence team interviewed Heather Fitzgerald, Senior Vice President of Distribution Intelligence, Data, and Salesforce at Jackson. Heather discusses her career journey and how she grew a passion for data storytelling within her work at various companies, industries, and roles. Heather also provides her framework for building a data story and optimizing business practices with data, discusses the importance of mentorship, cultivating a natural curiosity with data, and much more!

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Episode Transcript

1 00:00:00,778 --> 00:00:03,233 [Anna]: You're listening to the Real Intelligence podcast, 2 00:00:03,691 --> 00:00:09,034 [Anna]: presented by RXA, a leader in business intelligence and data science consulting services. 3 00:00:09,453 --> 00:00: 13,069 [Anna]: We're here to bring attention to the unique stories, perspectives, 5 00:00:13,568 --> 00:00: [Anna]: challenges and success that individuals in the data industry face at every career stage. Welcome to the show! 6 00:00:21,060 --> 00:00:22,960 [Anna]: Welcome to the Real Intelligence podcast! 7 00:00:23,555 --> 00:00:37,090 [Anna]: You're on today with Jason Harper, CEO and Founder of RXA, and Anna Schultz, Marketing Coordinator at RXA. Our guest today is Heather Fitzgerald, Senior Vice President of Distribution Intelligence, Data, and Salesforce at Jackson. 8 00:00:38,023 --> 00:00:48,676 [Anna]: Heather is a data visionary and analytics leader stemming from Chicago, Detroit and Silicon Valley with over twenty years of experience in driving innovative global solutions and data driven insights. 9 00:00:49,214 --> 00:00:53,510 [Anna]: She has deep expertise in business automation, data technology platforms, 10 00:00:53,944 --> 00:00:55,201 [Anna]: digital transformation, 11 00:00:55,578 --> 00:00:57,553 [Anna]: sales, marketing, and storytelling. 12 00:00:58,448 --> 00:01:05,189 [Anna]: Heather holds a BA in economics from Michigan State University, and an MBA from the University of Detroit Mercy. 13 00:01:05,810 --> 00:01:07,149 [Anna]: Her professional passions 14 00:01:07,450 --> 00:01:15,284 [Anna]: surround providing holistic data measurement and insights, to tie online and offline platforms together and to drive insight-driven storytelling. 15 00:01:16,185 --> 00:01:24,323 [Anna]: Additionally, she enjoys building teams to meet varied client and corporate needs, as well as mentoring junior staff. Welcome to the show, Heather! 16 00:01:25,976 --> 00:01:27,570 [Heather]: Thank you, Anna and Jason for having me. 17 00:01:28,303 --> 00:01:29,281 [Anna]: Thank you for joining. 18 00:01:29,978 --> 00:01:36,835 [Anna]: We like to start off the podcast by getting to know the real you. So, I have a few questions that we might not find the answers to in that professional bio. 19 00:01:37,888 --> 00:01:40,721 [Anna]: So one of them is, what's the most interesting thing you've read lately? 20 00:01:42,477 --> 00:01:48,424 [Heather]: This was a really interesting question that made me quite honestly, over the last few days, go back and think too 22 00:01:49,924 --> 00:02:01,770 [Heather]: about what I have been reading. But one that I, probably for the last year or year and a half, that I've really been a little, quite honestly enamored with is Dr. Edith Eger, E-G-E-R 23 00:02:02,304 --> 00:02:03,364 [Heather]: is her last name. 24 00:02:03,704 --> 00:02:13,057 [Heather]: She has a couple different books out, but in particular, her first book called 'The Choice'. She's a 96-year-old, still alive, living in Southern California 25 00:02:13,793 --> 00:02:19,081 [Heather]: who actually grew up and was in Auschwitz. So she grew up during the Holocaust, 26 00:02:19,994 --> 00:02:27,845 [Heather]: her sister and her survived, her parents didn't. They all were taken together, but it's... Again, coming back to the title of her book 'The Choice'. It's 27 00:02:28,238 --> 00:02:35,811 [Heather]: how she learned to live and thrive and, you know, in her 50s, she went and got her PhD, for example, here in the United States, 28 00:02:36,703 --> 00:02:41,429 [Heather]: got married, had children. How she ended up thriving after going through something so obviously, horrific 29 00:02:41,886 --> 00:02:43,880 [Heather]: as World War Two and, 30 00:02:44,637 --> 00:02:45,589 [Heather]: you know, what was happening 31 00:02:46,127 --> 00:02:46,826 [Heather]: in Europe. 32 00:02:47,243 --> 00:02:58,244 [Heather]: But really translating that into each one of us, in more of a public sense, in a public realm, in terms of what we can control on a daily basis, in terms of the choices that we make. Whether it's in business, 33 00:02:59,100 --> 00:03:18,944 [Heather]: you know, in our professional lives, or whether it's in our personal lives, we control how we react to every single situation that happens. And looking at being able to start to turn things on their head and flip the script a little bit, even in really horrible situations, and how to have a positive outlook that can get you through some of those times. So that really... 34 00:03:19,361 --> 00:03:22,294 [Heather]: Her second book is called 'The Gift' after 'The Choice'. 35 00:03:22,711 --> 00:03:25,085 [Heather]: So I'd really encourage anybody to read those, 36 00:03:25,422 --> 00:03:32,954 [Heather]: to read those books, she has some really great stuff on her website too. So I would definitely encourage everyone to learn about her. 37 00:03:34,087 --> 00:03:38,495 [Anna]: Awesome. Thanks for kinda walking us through that. And I think, like you said, that has a lot of 38 00:03:38,873 --> 00:03:46,169 [Anna]: applications in personal life, in business and in strategic leadership and everything like that, so that's really interesting. We'll definitely have to check it out. 39 00:03:47,146 --> 00:03:52,535 [Anna]: My second question for you is, what's something within your industry that you consider maybe underrated? 40 00:03:55,342 --> 00:03:57,278 [Heather]: I... Probably some of the theme of probably a 41 00:03:57,775 --> 00:04:00,308 [Heather]: few things that I'll talk about today. But absolutely 42 00:04:01,005 --> 00:04:02,795 [Heather]: that last mile within 43 00:04:03,255 --> 00:04:12,015 [Heather]: data and analytics is something that I've been really passionate about and has been really important to me in all of the different roles that I've been in and the teams that I've led. 44 00:04:12,669 --> 00:04:24,849 [Heather]: And that last mile is how to actually translate the data that you're finally able to access, that you're finally able to get your hands on - how can you actually translate that into actionable insights? Into a data story using visuals, using a narrative, 45 00:04:25,187 --> 00:04:26,445 [Heather]: obviously using data, 46 00:04:27,142 --> 00:04:30,650 [Heather]: to really influence and impact decisions that are being made within the organization. 47 00:04:32,070 --> 00:04:34,490 [Heather]: And, you know, helping senior leadership with strategic 48 00:04:35,350 --> 00:04:36,410 [Heather]: recommendations as well. 49 00:04:36,830 --> 00:04:38,745 [Heather]: But a lot of organizations, and, 50 00:04:39,204 --> 00:04:53,360 [Heather]: Jason, you probably have seen this quite a bit, where so much of the energy and time, rightfully so, needs to be focused on extracting and getting ahold of that data, and getting it into a usable format for analysts to be able to do those types of, you know, 51 00:04:54,120 --> 00:04:55,840 [Heather]: last mile analytics activities. 52 00:04:56,294 --> 00:05:05,220 [Heather]: But a lot of organizations tend to really stop there. And what I love to be able to do, is to take that last mile and bring it to life. And I think that that really has been a little 53 00:05:05,520 --> 00:05:11,840 [Heather]: underutilized, due to all of the technology and innovation that have been coming into the marketplace and coming into this industry, that 54 00:05:13,654 --> 00:05:18,628 [Heather]: that piece sometimes gets ignored. And I think that it's really important because it really closes the holistic circle. 55 00:05:20,246 --> 00:05:25,320 [Anna]: Absolutely. And I think, kind of, that storytelling aspect is something that you've really highlighted 56 00:05:25,780 --> 00:05:32,970 [Anna]: within your career and kind of made a point to really cultivate that at organizations that you've worked at, it sounds like. So 57 00:05:33,628 --> 00:05:38,377 [Anna]: can you kind of, in your own words, tell us a little bit about yourself and your career journey 58 00:05:39,115 --> 00:05:39,854 [Anna]: for our audience? 59 00:05:41,429 --> 00:05:43,366 [Heather]: Absolutely. Sure. So, 60 00:05:44,422 --> 00:05:47,375 [Heather]: when I say the number of years, it often frighten me a little bit. 61 00:05:48,195 --> 00:05:52,950 [Heather]: When I say that I've been, you know, doing analytics, data analytics, air quoting there, 62 00:05:53,888 --> 00:05:55,907 [Heather]: since 1996, so 63 00:05:56,685 --> 00:05:57,904 [Heather]: quite a long time. 64 00:05:58,363 --> 00:05:59,662 [Heather]: But if you think about 65 00:06:00,121 --> 00:06:06,070 [Heather]: just that 25, 26 year period, what this industry has gone through, it's been quite incredible. 66 00:06:07,168 --> 00:06:10,705 [Heather]: You know, when I first came into data analytics, it was really just taking 67 00:06:11,260 --> 00:06:23,132 [Heather]: any piece of information, whether it's sales data or survey research information, and trying to actually do secondary research on that and kind of turn that around from an insights standpoint. But what we're able to do now 68 00:06:23,910 --> 00:06:27,925 [Heather]: is really measure, obviously, much much more, but the small things. So 69 00:06:28,679 --> 00:06:36,410 [Heather]: way back when, you know, big data was kind of... 10+ years ago, big data was really the phrase and the couple words that everybody really, you know, it was 70 00:06:36,883 --> 00:06:46,812 [Heather]: being talked about everywhere. But what I really think that big data is, it's the ability to measure the small things now. The really minute, small things in each individual person 71 00:06:47,189 --> 00:06:49,283 [Heather]: versus macro groups, versus, 72 00:06:50,138 --> 00:07:07,438 [Heather]: you know, macro types of behaviors and activities. But we're being able to get down to these really, you know, intricate levels. And I think that that's really some of the exciting things that I've seen take place over the last 5-10 years. Things that we kind of dreamt of being able to do a decade ago, we're actually being able to do now. 73 00:07:08,316 --> 00:07:12,265 [Heather]: And being able to see just this natural progression with technology 74 00:07:12,725 --> 00:07:22,154 [Heather]: in the data and analytics space has just been completely fascinating to me. And I've really enjoyed that over the last, you know, couple decades, two and a half decades if you will 75 00:07:23,131 --> 00:07:32,108 [Heather]: within the industry. So, while I've done that and, you know, you kind of mentioned it, I started off in Detroit for a long time, then Chicago, then out to San Francisco, and now here in Nashville, 76 00:07:32,885 --> 00:07:40,064 [Heather]: really driving a lot of those same activities within the teams and the organizations that I belong to. But also being able to put a 77 00:07:41,001 --> 00:07:50,865 [Heather]: lens if you will, on all of that work that is encompassed by having experience across multiple industries, across different types of verticals, and really bringing home best practices 78 00:07:51,245 --> 00:07:56,631 [Heather]: that can be utilized, you know, whether it's a tech company you're working with, a CPG company, a financial services company. 79 00:07:57,327 --> 00:08:01,893 [Heather]: And that's again, really what I've tried to hone in on and what I truly enjoy doing. 80 00:08:04,877 --> 00:08:22,612 [Jason]: Love that. And thank you, Heather, so much for making the time today to talk to us. We really appreciate you being able to share, you know, what you're sharing today and your story I think is, you know, especially given, you know, the different places you've been and worked and experienced, being able to share that now is... 81 00:08:23,191 --> 00:08:24,770 [Jason]: We're really grateful for that. 82 00:08:26,507 --> 00:08:33,780 [Jason]: I think one of the, one of the themes that in our discussions, and what you're sharing so far, we're talking a lot about storytelling. 83 00:08:34,240 --> 00:08:38,755 [Jason]: And I think I would even when we think about that last mile 84 00:08:39,095 --> 00:08:40,235 [Jason]: of analytics 85 00:08:40,655 --> 00:08:54,089 [Jason]: today, one of the things that I really believe in is sort of that human-in-the-loop part of intelligence, or human-in-the-loop here, where there is all this technology. And it's super great and saves a lot of time, but 87 00:08:54,564 --> 00:09:03,117 [Jason]: I think having folks spending time to analyze things and that last, the way you're phrasing it as the last mile, I like that. It really, it 88 00:09:03,548 --> 00:09:07,396 [Jason]: makes a big difference in what actually translates things from a really fancy 89 00:09:08,251 --> 00:09:09,588 [Jason]: data science project 90 00:09:10,044 --> 00:09:21,636 [Jason]: that we can all look at and reflect on how great and complex it is. That's what translates that into something that we actually use to make different decisions on, and that's where that last thing helps. I wonder, 91 00:09:22,774 --> 00:09:28,921 [Jason]: kind of thinking about that through the lens of storytelling, you know, could you maybe talk a little bit about some of that insight driven storytelling 92 00:09:29,500 --> 00:09:31,039 [Jason]: and some of the impacts 93 00:09:31,417 --> 00:09:32,397 [Jason]: that that's had? 94 00:09:33,828 --> 00:09:34,028 [Heather]: Yeah! 95 00:09:35,183 --> 00:09:36,441 [Heather]: Great question and great, 96 00:09:36,977 --> 00:09:37,835 [Heather]: you know, just 97 00:09:39,448 --> 00:09:48,175 [Heather]: discussion points that you just made on all of that. And it's kind of interesting, I still use... And Jason, you and I worked together, gosh 12+ years ago, 14 years ago? 98 00:09:49,175 --> 00:10:03,780 [Heather]: And working... You know, when we worked together, that was the very first data science model that I had seen come really to life, and the success of it that you know, that we were creating at that time. And I still use that story to this day as we're starting to bring in data science models into production 99 00:10:04,678 --> 00:10:07,775 [Heather]: within the organization I'm at right now. So, 100 00:10:08,290 --> 00:10:08,790 [Heather]: absolutely, 101 00:10:09,170 --> 00:10:12,630 [Heather]: you know, I almost feel like I'm coming full circle from 102 00:10:13,010 --> 00:10:14,950 [Heather]: 12+ years ago in terms of 103 00:10:15,370 --> 00:10:17,350 [Heather]: what we started to be able to do. And 104 00:10:17,664 --> 00:10:19,443 [Heather]: what I... What I think, 105 00:10:20,381 --> 00:10:29,245 [Heather]: you know, regarding storytelling, it's really important for... And I teach a couple classes at Michigan State on it. In fact, two weeks ago, I was just up there teaching this as well 106 00:10:29,983 --> 00:10:36,410 [Heather]: to some of the data classes, the data analytics classes within the marketing, within the business school and marketing. 108 00:10:38,022 --> 00:10:37,444 [Heather]: And one of the biggest things that you have to really understand, obviously, is your audience, but you have to also understand what you're putting together. And it's not just... At the end of the day, I always you know, say this, 109 00:10:47,498 --> 00:10:49,715 [Heather]: for probably just as long 10-12 years, 110 00:10:50,614 --> 00:10:53,450 [Heather]: you can't just push something over the fence and say that the number is 8. 111 00:10:54,344 --> 00:11:02,895 [Heather]: What you want to be able to do and provide to your stakeholder, your senior leadership, whomever this... Whatever you're doing is going to, whether it's a report, a presentation, whatever, 112 00:11:03,749 --> 00:11:16,335 [Heather]: is describe everything going on around that number 8. What are all of the things that happen to make that number be 8? And then we can develop those strategic recommendations from there. And I think it's really important too that we 113 00:11:16,835 --> 00:11:19,735 [Heather]: understand as data analytics leaders and professionals, 114 00:11:20,289 --> 00:11:27,270 [Heather]: sometimes what you say isn't gonna be a good story. But you... But that's okay and that's our job to not be... not have a bias. 115 00:11:28,324 --> 00:11:35,675 [Heather]: Everybody wants, you know, x campaign to work really, really well, or this sales strategy to work really well, or the launch of a new product. 116 00:11:36,568 --> 00:11:50,036 [Heather]: But being able to actually have that unbiased opinion and drive the necessary insights that will influence the direction of all of those different things, is really important. And none of us should be afraid to be able... You know, none of us should be afraid to say 117 00:11:50,811 --> 00:12:01,524 [Heather]: that something didn't work because that's actually insight. So that's actually us learning things as well, that's going to shape future decisions and, you know, whether that's feature strategies, product introductions, etc. 118 00:12:01,904 --> 00:12:03,842 [Heather]: So, there's so many different instances 119 00:12:04,381 --> 00:12:17,690 [Heather]: where being able to add context to the numbers is going to really drive impact and influence within your organization. And I think another component of that is also understanding, you know, along with that 'don't just say the number is 8.' 120 00:12:18,884 --> 00:12:34,572 [Heather]: Along with that is understanding that business decisions are made not just from logical sense, but we as humans also have an emotional side of our brain that works unconsciously, that we may not be aware of, is helping us make decisions on a daily basis. So with our insights and data storytelling, 121 00:12:35,244 --> 00:12:37,939 [Heather]: again, utilizing the data, the narratives, the individuals 122 00:12:38,397 --> 00:12:42,329 [Heather]: and playing to both the logical and the emotional side of 123 00:12:42,907 --> 00:12:45,699 [Heather]: any human's brain is actually going to 124 00:12:46,040 --> 00:12:50,660 [Heather]: drive the strategic decision-making that you're aiming to do with your results and your numbers. 125 00:12:51,811 --> 00:12:52,528 [Jason]: I think, yeah. 126 00:12:53,564 --> 00:12:56,415 [Jason]: Reflecting back on our time that we worked together at Organic, 127 00:12:57,150 --> 00:13:02,771 [Jason]: the ad agency. I think that was super transformative. At least... I mean, for me, it sounds like for you as well, 128 00:13:03,109 --> 00:13:04,527 [Jason]: with regards to, 129 00:13:04,865 --> 00:13:17,609 [Jason]: like, consciously thinking about storytelling in a business setting. And one of the most valuable pieces of, there were a lot, but one of the most valuable things I took away from my experience at Organic was the Moth Storytelling 130 00:13:18,188 --> 00:13:20,725 [Jason]: training and those methods of 131 00:13:21,224 --> 00:13:28,920 [Jason]: setting the stakes, starting in the action, you know, landing the spaceship. Like, all of these sort of like, fundamental to storytelling 132 00:13:30,014 --> 00:13:32,108 [Jason]: kinda tools and methods and framing 133 00:13:32,899 --> 00:13:35,357 [Jason]: when it comes to giving a presentation or 134 00:13:35,975 --> 00:13:39,352 [Jason]: helping walk a client through a report. I wonder if there are any 135 00:13:39,970 --> 00:13:45,565 [Jason]: kinda, tips and tricks or methods that, you know, you might be using today that would 136 00:13:45,985 --> 00:13:46,925 [Jason]: help do this. 138 00:13:48,040 --> 00:14:00,552 [Heather]: So these are kind like the four points of data storytelling that I like to teach my teams and teach folks about, but the first is obviously defining your story. What is the main thing in your presentation or your report that you want to get, 139 00:14:00,891 --> 00:14:08,894 [Heather]: you want to get the message across? What is the business decision that's going to be made at the end of the day based on what you're bringing to life? That's going to help you define your story. 140 00:14:09,571 --> 00:14:24,637 [Heather]: The second is display. And you'll kinda notice that each one of these four sections here, you know, has one word and it begins with D. So, we've got define, now we're on to display. And it's, what is the best data visualization to be able to use? Is it an image? Is it an infographic? 141 00:14:24,949 --> 00:14:28,802 [Heather]: Is it a table? Is it a graph? Is it 142 00:14:29,540 --> 00:14:41,796 [Heather]: an impact picture or image, for example? So, there's a lot of different things that you can do based on, not only your audience, but what type of information you're trying to get across as well. So, displaying the best visualization would be the second. 143 00:14:42,449 --> 00:14:51,722 [Heather]: The third is declutter. So that's really removing any distracting or unnecessary information. And I go back to a lot of the days when we would do 144 00:14:52,114 --> 00:14:58,036 [Heather]: market research surveys, for example. There are so many questions in any given survey that 145 00:14:58,453 --> 00:15:00,880 [Heather]: being able to put all of that in a report is just 146 00:15:01,380 --> 00:15:09,680 [Heather]: unthinkable. You're not gonna be... That's just gonna be way too big. Your audience will get lost. What are the main things you want to put into this? And all of that other unnecessary 147 00:15:10,433 --> 00:15:19,805 [Heather]: information and data that you got from additional questions can go in an appendix, or go to, you know, different reports for different people. So, decluttering what you're putting together is really important. 148 00:15:20,578 --> 00:15:41,976 [Heather]: And then the last one is direct. So, directing your audience's attention to the key components of your story. And a lot of that is how you just set up your slides. It's how you set up your presentation. It's how you write your introduction to, whether it's on, you know, the header of a slide or the beginning of a paragraph in a report or in a white paper. So, the, you know, four Ds of storytelling: 149 00:15:42,394 --> 00:15:46,526 [Heather]: define, display, declutter and direct. Those are the big things 150 00:15:46,864 --> 00:15:48,402 [Heather]: that I really try to teach folks. 151 00:15:49,632 --> 00:15:51,765 [Jason]: I love that, and I love 152 00:15:52,102 --> 00:15:56,680 [Jason]: the simplicity of it as a framework and the complexity that it lends itself too. That's a really, 153 00:15:57,219 --> 00:16:04,565 [Jason]: I think that's super useful. Thank you for sharing that. That that... That's fantastic. I think, you know, when I hear things like that, to thinking about 154 00:16:04,985 --> 00:16:11,285 [Jason]: how we learned of them and how we kind of, you know, pick these things up along the way. I would imagine, like mentorship, 155 00:16:11,879 --> 00:16:15,409 [Jason]: has been important for you and it's something that, you know, it sounds like you probably do 156 00:16:16,025 --> 00:16:20,512 [Jason]: some of yourself. I'm curious, you know, from in your experience, like, 157 00:16:20,984 --> 00:16:33,599 [Jason]: have you worked with... Or have you had folks that you considered to be mentors that helped you especially early in your career? And I'd love for you maybe to talk about how you're, you know, currently maybe passing those learnings on to others today? 159 00:16:34,374 --> 00:16:37,761 [Heather]: Yeah, absolutely. So outside of data, this is my other passion point 160 00:16:38,654 --> 00:16:45,880 [Heather]: in leadership which is mentoring junior staff. It absolutely, 100% comes back to the fact that I had a great mentor when I started my 161 00:16:46,258 --> 00:16:48,291 [Heather]: career. So, when I graduated from Michigan State, 162 00:16:49,366 --> 00:16:52,337 [Heather]: and this is obviously way back, you know, in the 1990s, 163 00:16:53,072 --> 00:16:53,431 [Heather]: really 164 00:16:54,004 --> 00:16:57,274 [Heather]: having an internship was just really starting to gain momentum. 165 00:16:58,311 --> 00:17:05,290 [Heather]: And people... A lot of people didn't do that at that time. So, the program I was in actually required it for graduation, which I'm very thankful for. 166 00:17:05,910 --> 00:17:07,490 [Heather]: But where I went to intern 167 00:17:07,910 --> 00:17:15,500 [Heather]: actually became who hired me right after college. And I think we both know him but Paul Ballew was his name, was my boss there. 168 00:17:15,840 --> 00:17:19,835 [Heather]: And then he went to General Motors and I followed him there as well. 169 00:17:20,809 --> 00:17:25,260 [Heather]: So really having that person from, quite honestly, day one of my internship 170 00:17:26,316 --> 00:17:34,089 [Heather]: through to, you know, almost a decade of working together was really, really great. And seeing, you know, what he could teach me through that time period 171 00:17:34,744 --> 00:17:37,330 [Heather]: and the experiences that 172 00:17:37,749 --> 00:17:53,160 [Heather]: were able to honestly come about from the different places that we worked and the clients that we worked on together. So I love, as you, kind of you know, mentioned, I love to bring that back and come full circle with the, you know, the people that I've met. So I... You know, across, 173 00:17:53,579 --> 00:18:04,559 [Heather]: again, that all of the different cities across this country that I've lived in, there's still folks that, you know, that have been on my teams or even people that I've interviewed that I've stayed close with that, you know, maybe didn't accept the job 174 00:18:05,137 --> 00:18:10,045 [Heather]: at one of the companies I was at, who I still talk to, who still call me or text me for career advice, 175 00:18:10,818 --> 00:18:20,231 [Heather]: wanting me to, you know, help them look over their resumes or, you know, any one of these different things. And I just, I really continue to love doing that. We have a really great mentor program here 176 00:18:20,609 --> 00:18:21,389 [Heather]: at Jackson 177 00:18:22,326 --> 00:18:23,084 [Heather]: where I'm at now. 178 00:18:24,002 --> 00:18:30,390 [Heather]: Where it matches... It matches a mentor and mentee up, based on a lot of different criterias and factors, 179 00:18:30,770 --> 00:18:33,110 [Heather]: and a personality and 180 00:18:33,410 --> 00:18:35,310 [Heather]: a mentor-desiring 182 00:18:35,944 --> 00:18:42,036 [Heather]: topic survey that they hand out. So I really engage in that every quarter. So we kind of 183 00:18:42,749 --> 00:18:56,480 [Heather]: have new mentors and mentees every quarter. So that's really, really a good program. And I have actually quite a few mentees that are across different offices, so up in Michigan or in Chicago. So a lot of different, a lot of different places to meet 184 00:18:56,859 --> 00:19:00,955 [Heather]: and to influence the organization that way. In addition to that, I also, 185 00:19:01,749 --> 00:19:12,680 [Heather]: my team runs the intern program for all of Nashville. So, we have... You know, we run and coordinate and really structure an entire eight-week program every summer 186 00:19:13,697 --> 00:19:15,495 [Heather]: for interns across the country, 187 00:19:15,989 --> 00:19:26,732 [Heather]: who spend a couple weeks here in Nashville, go back to wherever they're living, and come back at the end of the program for a couple weeks as well. So that's another place. And we've actually had really good placement from the intern program. 188 00:19:27,251 --> 00:19:32,075 [Heather]: Three people actually from our summer cohort actually have already gotten full time jobs here 189 00:19:32,808 --> 00:19:36,139 [Heather]: in just a few months. A couple of them are graduating in December, 190 00:19:36,477 --> 00:19:44,295 [Heather]: and we have another offer going out to someone graduating in May. So really great opportunities to get involved with the folks that are up and coming throughout, 191 00:19:44,675 --> 00:19:46,575 [Heather]: you know, throughout this industry. 192 00:19:48,315 --> 00:19:56,350 [Jason]: I love that. I mean, it's great to see you really kind of giving back in a way that's meaningful to you and that you had experience with yourself. And certainly, you know, you kinda, 193 00:19:56,730 --> 00:20:05,445 [Jason]: I'm sure, feel like you hit the lottery with Paul, you know, especially having him you know, seeing what he's gone on to in his career too, right? Leading analytics at Ford 194 00:20:06,105 --> 00:20:06,605 [Jason]: after 195 00:20:06,960 --> 00:20:10,659 [Jason]: his role, I think he went right from GM to Ford if memory serves. 196 00:20:11,600 --> 00:20:21,105 [Jason]: I may not have his career path exactly right, but I worked with him a bit at Ford and he's certainly a big visionary-type person, like, definitely big-picture thinking. 198 00:20:21,699 --> 00:20:26,993 [Jason]: And now he's at the NFL. So, yeah, not a bad, not a bad path. And certainly not someone, 199 00:20:28,290 --> 00:20:45,840 [Jason]: you know, certainly someone that you'd... great to learn from. So kudos to you to find that too. I think part of it too is being willing to take on, you know, like, being open and enough and, like, understanding enough the importance of learning from someone in that position. And so I think, like, you know, kudos to you for 200 00:20:46,274 --> 00:20:49,329 [Jason]: seeing and recognizing that at a young point in your career. 201 00:20:49,866 --> 00:20:57,524 [Jason]: Because not everybody does that, you know, it's a take, it takes both sides, I think, to come to the table, to really get meaningful things out of it. So... 202 00:20:58,379 --> 00:20:59,257 [Jason]: Kudos. 203 00:21:00,333 --> 00:21:02,747 [Jason]: So I think, you know, one of the, 204 00:21:03,124 --> 00:21:04,142 [Jason]: kinda circling 205 00:21:04,694 --> 00:21:12,306 [Jason]: back sort of, we talked a little bit about this, but sort of like, one of the questions that people, you know, in this field that we get asked a lot is, sort of, 206 00:21:12,685 --> 00:21:12,805 [Jason]: you know, 207 00:21:13,538 --> 00:21:15,950 [Jason]: prove that my marketing is working, 208 00:21:16,526 --> 00:21:19,871 [Jason]: right. So like, prove to me that this is working like, you know, 209 00:21:21,244 --> 00:21:21,943 [Jason]: show you 210 00:21:22,323 --> 00:21:25,879 [Jason]: along those lines. Like, so what maybe in your experience, you know, 211 00:21:26,958 --> 00:21:33,462 [Jason]: how has it been challenging for you to answer that question, which I'm sure you've been asked a thousand times? 212 00:21:35,196 --> 00:21:37,430 [Heather]: And will continue to be asked a thousand more. 213 00:21:38,323 --> 00:21:44,369 [Heather]: Absolutely right. And, you know, that kinda comes back to, it's okay if you... If if the data isn't saying something 214 00:21:44,787 --> 00:21:45,505 [Heather]: worked really well. 216 00:21:47,315 --> 00:21:46,738 [Heather]: And being able to have that trusted role within your organization 217 00:21:50,703 --> 00:21:52,479 [Heather]: so that you can feel comfortable 218 00:21:53,015 --> 00:21:54,711 [Heather]: bringing those results to life 219 00:21:55,144 --> 00:22:17,215 [Heather]: regardless of the outcome is really, really important. That's not to say that everything's gonna be negative in terms of data and what you're gonna be reporting on, and presenting out on, but we do have to... Everyone has to really embrace the fact that we actually learn. And insights come from something not working, and that's how we optimize and that's how we get better campaigns. That's how we get better 220 00:22:17,855 --> 00:22:19,455 [Heather]: activations, all of the above. 221 00:22:19,989 --> 00:22:21,846 [Heather]: As well as understanding that, 222 00:22:22,304 --> 00:22:39,245 [Heather]: you know, kind of statement about what somebody... Tell me something... Tell me this, you know, campaign or tell me this strategy is working really well. We also have to make sure that we're doing this over time. We're measuring and tracking over time versus a snapshot in time, versus one moment, that's, I'm 223 00:22:39,861 --> 00:22:47,634 [Heather]: making this up, ten days after the launch of the campaign. Sure, we want to know what happening. We want to understand in the first few days, in first few weeks, 224 00:22:48,489 --> 00:22:55,804 [Heather]: the success or trends in terms of what's happening. But being able to gauge that over time is really important too. So while we... You know, 225 00:22:56,602 --> 00:23:09,705 [Heather]: innovation and technologies given us data at our fingertips, there's also things that we still need to be able to be patient for, and to wait to be able to collect enough data and information to draw the appropriate conclusions on, is also really important as well. 226 00:23:11,320 --> 00:23:15,900 [Jason]: I like that, I mean I think that, yeah... What you're describing there, those methods and things, those, 227 00:23:16,440 --> 00:23:22,475 [Jason]: you know, even transcend that question a bit and just really apply to a lot of different scenario. So I think that's very useful. 228 00:23:23,375 --> 00:23:24,115 [Jason]: Thank you. 229 00:23:25,575 --> 00:23:26,635 [Jason]: And sort of one other, 230 00:23:27,335 --> 00:23:31,286 [Jason]: one last question I would just pose based on thinking about your career 231 00:23:31,665 --> 00:23:33,244 [Jason]: journey, right? So you actually... 232 00:23:34,262 --> 00:23:39,295 [Jason]: I think it's very unique, the path that you've traveled. So between Detroit, Chicago, San Francisco, 233 00:23:39,875 --> 00:23:40,775 [Jason]: now Nashville, 234 00:23:41,155 --> 00:23:42,335 [Jason]: these are very different 235 00:23:42,875 --> 00:24:01,998 [Jason]: regions. These are very different, I think, kind of cultures. I'm just curious to know, this is from my perspective. You know, I'm curious to know, like, maybe just... Do you see these differences? And if so pros and cons? Like, what do you see kind of different in the different areas, and what do you, maybe, what do you love most about Nashville? Because I'm jealous about Nashville, quite frankly. 236 00:24:03,150 --> 00:24:04,570 [Heather]: So much wrapped up in that 237 00:24:05,270 --> 00:24:07,930 [Heather]: for questions there. So first and foremost, 238 00:24:08,750 --> 00:24:19,785 [Heather]: my mom, even from when I was a little kid, would tell me that I was born with a suitcase in my hand. So I always was very, you know, wanting to travel, wanting to live in different places. 239 00:24:20,538 --> 00:24:28,431 [Heather]: And I've certainly been able to do a lot of that, which I'm very grateful for, and obviously grateful for my husband for, you know, sticking with me through all of that too. 240 00:24:29,522 --> 00:24:31,178 [Heather]: But yes, there are differences. 241 00:24:31,954 --> 00:24:36,817 [Heather]: There's differences East Coast, West Coast. West Coast would always say they're the best coast. 242 00:24:37,750 --> 00:24:40,450 [Heather]: Then you have Chicago saying that they're the best third coast. 243 00:24:41,230 --> 00:24:49,330 [Heather]: Then, you know, you've got the Great Lakes, which I've lived in, all can compete from a water standpoint, and now land-locked in Nashville. So very different terrains, very different, 244 00:24:49,906 --> 00:24:50,845 [Heather]: you know, climates 245 00:24:51,302 --> 00:25:01,005 [Heather]: from both a weather standpoint as well as you know, the types of organizations and companies that are in those cities. So it's been fascinating going from, you know, Detroit, that was 246 00:25:01,783 --> 00:25:03,599 [Heather]: historically, and now, obviously 247 00:25:04,056 --> 00:25:06,311 [Heather]: diversifying has been awesome for the city. But 248 00:25:07,087 --> 00:25:10,796 [Heather]: having it have such a central historical hub with automotive, 249 00:25:11,410 --> 00:25:11,569 [Heather]: you know. 250 00:25:12,368 --> 00:25:17,702 [Heather]: What I would notice and, you know, I'm know sure if you noticed this too, Jason, but people would just kind of go from 251 00:25:18,041 --> 00:25:22,951 [Heather]: GM to Chrysler to Ford, maybe not all three of them, to a supplier, to an ad agency, 252 00:25:23,290 --> 00:25:32,156 [Heather]: and it's the same kind of group of, you know, group of folks. You know, moving around from company to company. And then you go to Chicago and it's very CPG focused, 253 00:25:32,695 --> 00:25:33,714 [Heather]: very, very 254 00:25:34,053 --> 00:25:37,805 [Heather]: consumer package goods focused, very tactical with their with, 255 00:25:38,342 --> 00:25:42,015 [Heather]: you know, their activations or their marketing campaigns and marketing strategies. 256 00:25:42,414 --> 00:25:43,632 [Heather]: And then I, 257 00:25:43,971 --> 00:26:11,764 [Heather]: moving out to California and working in the Bay Area was you know, when I moved out there, I thought, well, for sure I'm gonna be working with, like, the Adobes and the Citrix's and, you know, Oracles of the world, Salesforces of the world. And we did, I did at the companies I worked for. I thought going in, that they would have it all figured out. Like, they are the world's top companies from a tech standpoint, they have for sure got all of their data roadmaps and strategies figured out, they're making sense of their data. 258 00:26:13,075 --> 00:26:14,335 [Heather]: We're all in the same boat. 259 00:26:14,715 --> 00:26:16,055 [Heather]: None of us have done it perfectly. 260 00:26:16,395 --> 00:26:19,595 [Heather]: None of us, you know, we're all still learning. They don't have it perfect either. 261 00:26:20,089 --> 00:26:23,421 [Heather]: So, you know, it's a little comforting to know you're in good company 262 00:26:23,958 --> 00:26:25,234 [Heather]: with some of those firms too. 263 00:26:26,351 --> 00:26:31,735 [Heather]: And then coming to Nashville, obviously, a very, very rapidly growing city, and moving here only, 264 00:26:32,235 --> 00:26:47,590 [Heather]: gosh, 4 months before Covid lockdowns was very interesting, because we didn't even really get to get, you know, our feet on the ground or anything before we were basically home bound. But I do... What I have noticed, to kind of round out and conclude your question is, 265 00:26:48,702 --> 00:26:51,594 [Heather]: before Covid, I noticed a significant amount of differences 266 00:26:51,931 --> 00:26:56,156 [Heather]: in just moving between two cities, or when I would go back and visit Chicago or Detroit. 267 00:26:56,770 --> 00:26:59,710 [Heather]: What I've found though since Covid is that, 268 00:27:00,370 --> 00:27:11,836 [Heather]: as people become more mobile, they can live wherever they want to, for the most part. They can work from home more often than they could. They can travel more, overseas even. I feel like it's become much more... 269 00:27:13,069 --> 00:27:15,087 [Heather]: We're becoming much more... 270 00:27:18,303 --> 00:27:22,975 [Heather]: I well, I just... I don't even know the word to say, we're becoming much more... 271 00:27:24,155 --> 00:27:28,575 [Heather]: Homogeneous isn't the right word because that's not what I really mean. 272 00:27:29,210 --> 00:27:37,221 [Jason]: That's what was coming to my mind, the cultures are sort of blending more now is what it feels like. And so with that yeah. That movement in that, I get it. 273 00:27:37,895 --> 00:27:41,635 [Heather]: Yeah. It it... It's not just the blending, but it's... 274 00:27:42,495 --> 00:27:51,596 [Heather]: We're morphing. And, you know, people, some, you know, people I know in California were like, "why, I can't believe you're moving to Tennessee." And it's so much more 275 00:27:52,852 --> 00:27:53,352 [Heather]: diverse 276 00:27:54,105 --> 00:27:54,605 [Heather]: and 277 00:27:55,385 --> 00:27:55,885 [Heather]: populated 278 00:27:56,425 --> 00:27:56,925 [Heather]: and 279 00:27:58,745 --> 00:28:05,098 [Heather]: urban-centric than you would ever, you know, think if you had never visited here or have lived here or anything. So, 280 00:28:05,557 --> 00:28:17,014 [Heather]: again, I think it's just everything coming together post-Covid is allowing us to really get and work with the brightest and best people regardless of where they are, and I think that's one of the best things that's come out of, 281 00:28:18,230 --> 00:28:19,688 [Heather]: come out of the last few years. 282 00:28:23,160 --> 00:28:33,755 [Anna]: Absolutely. And thank you, Heather, again, so much for kind of walking us through your career path, and kind of learnings from that and sharing some of that advice for 283 00:28:34,375 --> 00:28:49,672 [Anna]: our listeners who might be looking to get into the data space or move around within the data space. I think it's really important to hear from someone who has had, you know, a lot of different experiences in a lot of different areas. So, thank you again so much for taking the time to speak with us today. 284 00:28:50,447 --> 00:28:55,189 [Anna]: Is there anything else that you would like to leave our listeners with before we close out the recording? 285 00:28:57,518 --> 00:29:01,974 [Heather]: I think that just to, kind of conclude, I would say that if you're interested, 286 00:29:02,512 --> 00:29:06,225 [Heather]: or are already a part of, you know, the data, business intelligence 287 00:29:06,565 --> 00:29:07,545 [Heather]: analytics space, 288 00:29:07,845 --> 00:29:25,105 [Heather]: continue to have that natural curiosity with your work. And never let go of that, regardless of what level you're at or the type of role that you're in. Always have that curiosity to understand the business, understand the business decisions that will be made and understand 289 00:29:26,205 --> 00:29:26,705 [Heather]: really 290 00:29:27,365 --> 00:29:33,673 [Heather]: what your organization is trying to do holistically from a data roadmapping and a data strategy standpoint. 291 00:29:35,247 --> 00:29:40,745 [Anna]: The Real Intelligence podcast is presented by RXA, a leading data science consulting company. 292 00:29:41,485 --> 00:29:47,065 [Anna]: RXA provides project-based consulting, staff augmentation, and direct hire staffing services 293 00:29:47,365 --> 00:29:48,125 [Anna]: for data science, 294 00:29:48,660 --> 00:29:50,840 [Anna]: data engineering and business intelligence, 295 00:29:51,220 --> 00:29:53,980 [Anna]: to help our clients unlock the value in their data faster. 296 00:29:54,620 --> 00:30:00,528 [Anna]: Learn more by visiting our website at www.rxa.io 297 00:30:01,064 --> 00:30:05,369 [Anna]: or contacting our team at [email protected] today.

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