In this episode we are joined by Brent Dykes, the bestselling author of Effective Data Storytelling (Wiley, 2020), founder of Analytics Hero, and Forbes contributor. He has helped thousands of finance leaders at companies including Microsoft, IKEA, Nike, Sony, and Pfizer, enhance their data storytelling, visualization, and interpretation skills. “I strive to ensure analytics is always aligned with business priorities and maximizing business value.“
In this episode Brent Dykes says: “One of the things that I saw quickly was that a lot of people struggled to communicate their findings and, and their insights around data. I would go to conferences and I’d see people present data, and I’d be horrified by what I’d see-just overwhelmin data dumps. And that really flagged for me in my mind that there’s a real opportunity to do better.”
- Early college days choosing between marketing vs accounting
- The aspects of humans that AI and machines can’t replicate
- Business consultant at Adobe for 12 years and Domo
- Effective Data Storytelling
- The evolution of data
- Evolving perspective of finance and analytics
- Value AI vs Humans
- Lessons from coaching and mentoring companies
- Challenges of data storytelling in large and small companies
- The biggest challenges in data storytelling
- What do you do when your story is contradictory to what their bosses want to hear
- Recommendations for tools and systems to up your data maturity
- Why Excel’s a great starting point
https://www.effectivedatastorytelling.com/contact
https://www.linkedin.com/in/brentdykes
Effective Data Storytelling (Wiley, 2020)
Glenn Hopper:
Welcome to FP&A Today, I’m your host, Glenn Hopper. I’m excited to welcome Brent Dykes to the podcast today. Brent is a renowned expert in data storytelling, the, the founder of Analytics Hero, and the author of Effective Data Storytelling. With over two decades of experience in the analytics industry, he’s worked with top brands like Microsoft, Nike, and Ikea to help transform data into compelling stories that drive business impact. He’s also a Forbes contributor and a LinkedIn top voice, regularly speaking at conferences worldwide. Brent, we’re thrilled to have you with us.
Brent Dykes:
Thanks, Glenn. Great to be here.
Glenn Hopper:
I love, uh, you know, that’s one of my favorite things about this podcast is I talk to, uh, people who have shared interests with me. And when you combine data and storytelling, then you are, uh, you know, you I’m in your choir. Yes. <laugh>, that is, I love, I I love talking to people, um, uh, about those two. And I think that you’ve really done an incredible job of marrying both and, and your lessons around them are very important for FP&A. And I think it’s, it’s so interesting. And, you know, I mentioned before the, uh, before we started recording that you’re the third author I’ve talked to in the, in the past few weeks, and it’s amazing to me, or at first it used to be that you have all these numbers people, and it seems like it’s a right brain, left brain thing, but who are compelled to write books because you would think like numbers, you know, is, you know, it’s qualitative and quantitative. How do you get to writing the books? So, I’m curious, uh, from your perspective, take me through kind of your career journey and how that working with, with data and numbers and how that turned in storytelling and how that turned into, uh, writing about it and now teaching about it as well.
Brent Dykes:
Okay. Yeah. So my, uh, going back to college, we’ll start there. I was deciding between accounting and marketing. Okay. And I was excelling in both classes. And, and I was like, well, which, which one should I go for? Most people would tell me, oh, accounting, you know, you, you’re set for life. If you get into accounting, you’re good marketing, eh, not so much, you know, a little bit more risky. So I, what did I do? I went into marketing. Uh, at, at the time there was no such thing as analytics. Analytics didn’t exist. Data was not a big focus yet. Then I got my MBA after working a few years in a marketing role, I went back to school to get my MBA, and then that brought me into marketing analytics. And so then that’s where I started to work on the data side, even though I came from the business side, getting exposure to measurement and data and, and all of that, working with Fortune 500 companies at that point.
And one of the things that I saw quickly was that a lot of people struggled to communicate their findings and, and their insights, uh, around data. And I would go to conferences and I’d see people present data, and I’d be like, horrified of what I’d see. It’s just an overwhelming, you know, just data dumps. And, and that really flagged for me in my mind that there’s a real opportunity here to do better. And, and so I had worked as a business consultant, uh, at Adobe for 12 years. And while I was there, I started to think, you know, maybe I’d already written one book about analytics, digital or web analytics. Uh, and then I, I really was passionate about the communication aspect. Uh, and so then when I left there and joined another company called Domo, I, I started writing my second book, uh, effective Data Storytelling. And part of that was because I felt that there was some problems in the industry. I felt like the term was becoming used as like a buzzword. I saw vendors using it maybe in ways that weren’t appropriate. I saw people talking about it in terms of mainly data visualization, but not really understanding the power of narrative. And so that kind of really inspired me to want to write the book and get it out there and hopefully correct some of these misconceptions that I was seeing creeping in.
Glenn Hopper:
You’re so spot on with that. And I think, you know, with our audience of FP&A Professionals, I think about my first financial, actually my first corporate role, I started in marketing as well. I was a product manager for a, uh, um, a web design tool. And ended up, in the process of trying to get more money for my product, I started diving into the, uh, the budget and eventually got moved from, well, if you’re so concerned about the budget, why don’t you manage this for, I sort of got pulled, like, I, I just woke up the morning and I was no longer in marketing. I was just doing budget and numbers. You’re two number, you see <laugh>.
But, um, I think about when I rolled into finance, our first CFO, and maybe you’ve seen this too, where the roles have evolved as we’ve had more data and have had to work with them. I think about our CFO back when I was in telecom, we’d have our monthly or quarterly meetings and, you know, the CEO would come and give his big presentation and get everybody all fired up, and then immediately be followed by the CFO, who would basically just read down the financial statement, revenue cogs, gross margin, <laugh>, right? You ebitda, net income, you know, going all the way down. And it just, he would lose everyone, even if the numbers were good. So obviously back then, you know, storytelling wasn’t a thing. And it’s like, when you were talking about analytics wasn’t a thing, but as we got more and more data and as the expectation of what, you know, the value that you had to data as it changed, um, it became a more important part of our, our job.
And I think especially now in finance and in in other groups, in a leadership, you have to be able to be the one to add value to the data. So I think all that preamble to lead up to my my next question where, so your two books, web Analytics, action Hero, and then Effective Data Storytelling were written at points in time. And I think you’ve seen, you know, sort of evolution since you’ve written those and as you’ve moved on. But how, how would you say each book reflected your evolving perspective on analytics and, and sort of where we were then and where we are today, and how you’ve seen storytelling become more important over the years?
Brent Dykes:
Yeah, I mean, I, I think you can see some overlap between the two books. Uh, one, the first book was definitely focused more on the how do you, how do you drive value with web analytics and, and drive action. And, and so that’s a key part. I mean, both books, I talk about driving action, and in the first book, I didn’t really dig into the communication too heavily, whereas the second book, a hundred percent on communication and obviously taking those insights that we have and how do we share them in a meaningful way that’s gonna drive change in action. But I think in the first book, I think I might’ve been a little bit, or at least in that stage of my early stage, earlier stage of my career, I might’ve been a little bit more data first and not really valuing maybe some of the human aspects of what goes into decision making.
What goes into us as analysts or as, uh, financial professionals working with the data. What do we bring to the table? And I think I have a new, I think I’ve matured in, in a sense that, yeah, data’s important. I will always look at data, but is there some value that, that we bring to the table as human beings now? I think having AI on the table now where we can automate a lot of stuff, the, the modeling and the algorithms and the tools that we now have are getting more and more powerful. So what does that mean for us as human beings? Are we just now out of the picture? Are we not relevant? Are we just inefficient meat machines and, and really we need to have, you know, computers doing everything? And I’m like, no, no. There are actually aspects that we bring to the table that will be very hard for computers and machines to replicate.
That’s empathy, creativity, human judgment, contextual awareness. These are all things that we bring to the table. And so I think I’ve shifted a little bit further away from, from data being the end all be all of everything to maybe being a little bit more, sometimes people say a data humanist where we’re, we’re seeing the role of the human being as a part of this data ecosystem and, you know, data based environment, you know, there is a place for human beings. And, and that’s kind of how I see it. I definitely see our future augmented by technology and AI, but I still s I’m still an optimist in believing that we play an important role in shaping the future.
Glenn Hopper:
Yeah. And that storytelling is so interesting because I think, you know, people use generative AI for, for writing ideas. And I even, I like going back and forth and kind of workshopping with AI to write something, but if you ever try to get generative AI to actually write something for you, I I would never like try to pass off something written by, uh, generative AI as my own, because it’s full of cliches, hyperbolic statements, you know, overly superlative, uh, comments. And just, it’s so, it gets, you know, it’s, it’s <laugh> because it’s the entirety of the digital world, um, all kind of jammed together. I mean, it’s gonna pull the cliches out, and it just, it ends up, uh, as sort of this watered down and anodyne, um, you know, just a, a very basic clear style of writing. So that storytelling is one of those, it’s like LLMs, you know, they don’t have a sense of humor. They don’t understand sarcasm. There is, there are these subtleties that come with storytelling. Like it could, you could, you know, train it on tufty and, uh, you know, <laugh> all, you know, and great writers and, and give it an idea of, of what to mimic. But to really do that storytelling, you need that, that human in the loop. And are you, so as you experiment with generative ai, like what are you, are you finding ways, things that it can help with in the storytelling or things that it’s particularly good or bad at?
Brent Dykes:
Yeah, I mean, I, I kind of look at it as obviously when there’s like the, there’s all the work that goes into before you do the story. So that’s like cleansing the data, preparing the data, analyzing the data. I think AI can play a role there, definitely, especially those things that are more laborious or just take time for, for to, to do manually and can be error prone as well, right? So I see a role on the analysis and the prep data prep and analysis side. I also see a role for maybe bouncing your ideas off of ai, getting feedback on your visualizations, getting feedback on your thought process into how you connect the dots. You know, so I definitely see a feedback mechanism there in terms of that, of, of an AI crafting the entire data story. I think the areas where it can fall apart is on the contextual understanding of the business and the situation around the data.
’cause if that data is not collected, and it often isn’t, right? Oh, that’s when our CEO spoke at that conference. That’s why we’re seeing this lift or, or this, this happened because of this, you know, uh, we didn’t log our hours correctly that week or whatever, you know, all this kinda like stuff that doesn’t go into the databases or into the data sets is out there in the human world that we can then bring in to help craft the story, shape the story. And yet a algorithm or an AI solution doesn’t have that context that’s lacking that. And then, and then the other factor is our understanding of the audience and understanding what’s important to them and how to communicate the information to them. So it, I think there’s, there’s ways in which we can brainstorm data stories, we can get feedback on our data stories.
It can help us with the analysis part of forming a data story. And then obviously it can generate, uh, visualizations as well. And, and I think that’s gonna be an interesting area because in two, three years, do we just verbally tell, you know, the software what we want? You know, I want a bar chart this way. I want you to highlight these three values, and I wanna make sure that this and this and this, you know, all these best practices are a part of that chart, and then it builds the chart that we want and then we modify it based on voice, you know, or, or, or prompt based, you know. So it’s, it’s an interesting space. I, I, I definitely feel like data storytelling is really close to where, you know, AI is operating and, and how we leverage it. But I still feel like it’s gonna be human led. It’s still a human led activity and not something that we can just outsource and automate completely.
Glenn Hopper:
Yeah. And that, so that brings me to analytics hero, your company. So you’re going now and you’re talking to, to companies about their data strategy and about the storytelling. And then I guess you’re, and you’re doing coaching and, and mentoring to the analytics professionals. Let’s take that part first and then I’ve got another, a follow up question to it. So, <laugh>,
Brent Dykes:
For about three years now, I’ve been offering training workshops. So I do virtual and in-person training workshops, coaching. I’ve also done more like makeovers. So I’ve had different companies that have had, you know, here’s some reports that we’re delivering. These are not working for our audiences. How can we improve them? And so then I’m coming with, well, have you thought of doing it this way or this way or this way? And in providing recommendations that way. So yeah, just helping companies wherever we can. And then in my past life, I’ve worked in data strategy. I’ve worked in, you know, helping to build companies to build data cultures and studying companies. And I really see that data storytelling is a, a way in which we can instill a data culture in companies. ’cause you know, I, the, the metaphor I like to use is, you know, when we’re, when we have young kids and, and they can’t read yet, what do we do with them?
Well, we read to them, right? We share stories with them. We get them excited about hearing these adventures and these stories from the storybooks, and then what does that do that, and then inspires them later on to then want to read and, and explore their own adventures through these books. And so I, I think the same, I see data storytelling as a catalyst for developing a data culture. Not sometimes people think, oh, the data culture is gonna happen once we have these skills in place. Or, or sorry, when, when we have a data culture in place, then we can start telling data stories. And I’m like, no, no, no, no, no. If you only have a handful of people who are capable of telling data stories, get them to start telling stories to the tens or hundreds of other people. ’cause then they’re gonna inspire other people to want to get interested in the data, to want to explore the data. They’re gonna ask questions now about the data and, and you’re gonna start to see data storytellers develop a data culture at your company. So that’s kind of, you know, and I see finance playing a wonderful role. I mean, you have so much data that’s critical to the business that really sheds a light on where your customers are, where your expenses, where your operations, how they’re, you know, there’s so many opportunities for telling really interesting powerful data stories from
Glenn Hopper:
A finance perspective. Yeah. And it was so interesting to me from a finance perspective, because I’m, I’m an analytics guy from way back, my first finance role actually, it’s sort of evolved into, I had the company’s first, uh, BI team. I don’t even, I don’t remember, I don’t know if that’s what we were calling it back then. This would’ve been the mid two thousands. So whatever it was, we were, you know, we were using Crystal Reports and doing <laugh>, right? Uh, you know, doing reporting on, on metrics across the company. But it was interesting to me that that rolled up under finance. But I thought it made sense because as finance professionals, we were the impartial reporters of the company’s finances. So if we could also be that impartial reporter of, we’re not gonna tweak KPIs to make ’em, you know, look, be different, <laugh> better one quarter over another, because we’re not trying to protect anyone.
So if there’s, you know, operations as KPIs, we’re always gonna report the same way sales does and whatever. So it made sense for us to kind of be the, the reporters of, of the data too. But then as machine learning came along and big data came along, I was, it, all the companies where I worked, marketing was light years ahead of what finance was doing. And I thought, but we’re the OG analysts, how <laugh>, how are we getting past that marketing? But at the time, especially if it’s an e-commerce business or what, they had so much more data than we did. And now though, as things equalize in, in finance is sort of branching out of its silo of just, you know, general ledger and financial numbers and being able to report based on correlations between, you know, and, and predict churn and everything it is rolling into, into finance Yeah.
A a little more these days. And we are, it feels like we’re catching up now a little bit. Good, good. You’ve, you’ve worked with some very big companies, Lego, Honeywell, fidelity, and, and the ones I mentioned earlier, I’ve worked primarily with companies in the SMB space, say under 50 million in, in revenue. And these companies always struggle, you know, they don’t have, they’re not big enough to have a, a true data science team. You know, maybe they’ve got a couple of bi people or whatever, but they’re, you know, we, we’ve been talking about digital transformation for 30 years now, it feels like, are we ever gonna be transformed? And especially in this space. But I’m, I’m wondering, and, and maybe it’s different, I have had a couple of, I, I won’t throw any, any large companies under the bus ’cause I’d like ’em to hire me again. But I have seen large companies that you would think would be further along <laugh> with their, uh, data, um, maturity than, than they are. But what, as you go in and talk to these companies, and maybe it’s a different answer for the big companies in the small, but what do you see as the biggest barriers to kind of improving that data maturity and data literacy in organizations? Or are you seeing that companies are getting better now? What’s, what’s kind of your read there?
Brent Dykes:
Yeah, I, I recently talked about this in a Forbes article that I wrote. And I think one of the problems I’m seeing with data literacy and in kind of developing a data maturity, uh, one interesting thing that I spotted was, is that a lot of the focus is on tools and techniques as opposed to thinking and cultural focus. So when it comes to data literacy, they’ll train people up on certain tools and technologies. Uh, they’ll teach ’em certain techniques on how to maybe visualize the data. But that’s the extent of it. There. There’s really no focus on the, how do I approach a decision and use data effectively, you know, what’s that mindset? And so in that article I talked about, and, and it was interesting, I was like, looking at a lot of different, we talk about, uh, data-driven decision making. I’m sure you see, you hear that in finance as well, but on the analytics space, we, we talk about that a lot.
Like, oh yeah, everything’s about data-driven decision making. And it was interesting when I looked into some of those articles that had been published by different authors and vendors, there was almost no discussion around the actual decision making. It was like everything, like to collect the data, to prepare the data, to visualize the data and reports, and, and kind of, and then analyze the data. You know, all of that was the heavy lifting in the minds of these people that were, you know, advocating for data-driven decision making. And then it was almost treated like a given that once you got to the decision that it would just be straightforward. Like, people would take that information, just make the right decision, and then boom, you know, then we’re, we’re off to the races. And, and I’m like, if anything, working in analytics for almost two decades has taught me is that no, that is not, it’s not straightforward.
Like even when you get the insight, like there’s a chance that it’s misinterpreted, misunderstood, not understood, and, and nothing goes nowhere, a lot of people are still making decisions based on their gut and, and a hundred percent on intuition and not leaning on the data at all. Uh, and, and so for me, one of the barriers is really how do we instill that data driven mindset, if you will, in more people across the company at the top and at in the middle, middle management, and then down to the frontline workers if they’re making, you know, if we’re empowering people, we’re, we talk about democratizing the data, getting data into the hands of everybody, but if people are not trained on how to use that information to make decisions, what value is it gonna provide? It’s gonna be either ignored or misused. And so I really feel like we’ve still got a long ways to go, you know, and some people talk about, oh yeah, it’s gonna fix everything.
It’s secure, all no, no, no, this is a human problem. This is not a technology problem. This is a human problem. And as long as human beings are still operating in companies, which I, I think we will, until someday when maybe we can all retire and let the, the robots run everything until they decide, you know, we’re no longer, they don’t wanna serve us anymore. But anyways, we, we won’t go there, <laugh>. But, you know, I, I feel like this is this, that’s a key part for me. I, I think that’s a barrier that we really haven’t developed a data-driven mindset across the board at companies. You know, there’s still some hesitancy, there’s still some resistance. And so that for me is, is a key focus area that, and, and how do you overcome that? I think one of the key things that I think is really important is executive sponsorship.
You know, I’ll say that over and over again. If you’ve, I’ve seen the difference. I’ve worked in, you know, one of the benefits in working consulting is I’ve had the opportunity to work with hundreds of different organizations, large organizations, and I can, I can walk into buildings, massive corporate buildings that look on this look the same on the outside. But when you go in and you start talking to people and you see how they’re using data and how they’re not using data, it’s, it’s shocking how much the difference, one or, or a handful of executives who get it make to an organization as opposed to others that seem to be floundering. They, they really have no clue, they have no executive sponsorship on how to leverage the data.
Glenn Hopper:
And, you know, better than I, I do here, but th this is why storytelling is such an important part of data. So I was at a company years ago that we were fortunate enough to have DataRobot access super cool tool. I don’t know if, if you’ve had much experience with it, but you, it just drag and drop machine learning. It’ll, you know, you put your data in, it’ll run whatever model you want. But we had all these people with no data science background using DataRobot dropping stuff in, and, you know, it, it would select, it would say, you could use K means clustering or whatever, you know, whatever it was gonna pick. And people would present the output of whatever they dropped in the DataRobot. And if you ask them, okay, why did it, why did it predict this? Why, why is it saying that this is gonna be our churn rate or, or whatever the case is?
And people would say, well, ’cause that’s what it spit out. And if you don’t know, you know, what the, if you don’t know how to do model selection, you don’t know how to, if you don’t have that basic sort of data science analytics background, and you just use the tools and, and I think we’re the, we have the danger there of, of seeing that with generative AI right now too, of Oh yeah. But at least with generative ai, you can interact with the data in a way that hope <laugh>, you can get the Gen AI to explain it to you maybe better than, than these people were that were using it. But to, to move to that data driven decisionmaking for the executives to buy in and believe it works, they have to believe the data. And not only the data, but what was done with it.
So if, if someone is gonna give you a report, they need to be able to explain, you know, th this is what we did. These are the problems we saw, this is what we cleaned this data, we excluded these, we had to, uh, in interpolate here, and you know, this is what we did. And based on this analysis, we think this is the likely scenario, this is the probability it happens, or whatever. And you can build that trust and then move to it. But if you don’t, if you don’t have the ability one to understand what you’re doing, two, to tell the story about it, then you’re not gonna get that trust from, from senior management. And I guess that begs the question as, and I think that there’s, we’re gonna see more education going this way where if you’re studying finance and now you have to study, you know, you study statistics and some BI stuff, but I think it’s gonna be, from an education perspective, analytics is gonna be tied in there a lot more going forward, just as, as we’re using all this data. But for people who are in the workplace now, and they want to enhance their data literacy and they want to help their companies transform into this sort of data-driven decision making, I mean, how do you advise people to lean in and, and, and start learning and expanding their expertise to include those ANA analytics capabilities?
Brent Dykes:
Yeah, so if we’re talking to a, a finance, you know, FP&A kind of audience, I would assume you have the numeracy skills. You know, you’re comfortable with numbers. So if I was advising somebody how to, in the finance world, how to become more data literate, I would say, I think one of the big challenges that I’ve seen in my analytics consulting career, especially with executives and and mid-level leadership, is sometimes they don’t understand how the data is collected, how it’s processed and calculated. And so I’ve seen a nu on a number of situa <laugh>. One funny example, maybe not so funny, was I was working with a company and I was like, they were talking about these numbers that they had pulled from like customers, like, oh, our customers, you know, they, they like this or they like that, or, you know, they’re like, quoting this.
And I was like, where are you getting these numbers from? Because I don’t, I don’t know where you’re collecting this data. And I was coming from our tool, the, the Adobe analytics tool. And then I went into the tool and I found that it was like an old like survey that somebody had set up and it had been discontinued like months or years ago. And people were still, because it was a report that was still being emailed to them, or it was where they, they could access it. They were, they were trusting that this was like, oh, this is our customers, you know, like, this is all our customers saying this. And I was like, no. Like this is a fraction of like whoever was cookie at the time when we had cookies and we could cookie people. These are just the people that are cookied and, and you’re seeing the data from this survey is just for those people, which is like 0.001%, you know, and you’re making decisions based on this data representing what your, how your customers are behaving and how they’re feeling.
And, uh, I had to break it to them, like, that information is garbage. Like I, I don’t even know if I trust it was implemented two years ago or whatever. And it’s now probably in disrepair and probably not reflective of anyway. So it was one of those situations where they didn’t understand how the numbers are being collected behind the hood. It just, somebody had given a, you know, had named the, the variables and the data so that it resonated with this, with these business decision makers. ’cause they’re like, oh yeah, I wanna know this, or I wanna know that, oh, that looks like good information. Oh, great. And I’ve seen that on countless times where people are making decisions thinking, I think this means this, so we’re gonna make decisions based on that. And it’s like, no, it doesn’t mean that it means this. And it could be entirely different.
And then once, you know, you see them not only like realizing, oh crap, you know, like, I can’t use this. And then they think back, oh crap, I’ve been using it to make these decisions based on this misconception. And, and then they, you know, you almost like, you start to see them like, oh, I hope I didn’t, uh, you know, do anything too Dan or dam, you know, damaging to the company. But anyway, I, you know, I think one recommendation I would be for finance professionals to dig into the analytics, see how the data’s collected, where is it coming from, what does it mean? And don’t just assume because some, somebody in analytics or on the data side has labeled certain things a certain way that that’s what they mean. ’cause there’s countless opportun countless occasions I’ve seen people misinterpret numbers or assume that one metric that they saw in one report means the same thing as it means in another report. And then they don’t realize, oh, that version of revenue is excluding returns, whereas this other one has it, it hasn’t excluded the returns. So you’re, you’re actually looking at, you know, that number represents something different. Um, so that, that would be one recommendation that I would have.
Glenn Hopper:
Yeah. And that having that data lexicon where everybody’s, you know, singing from the same sheet of music and where this term means this and it’s consistent across, I mean, that’s foundational to that having a data-driven organization, I think back to, you know, it’s been a few years since I’ve, uh, been the one building, uh, models on a regular basis. But I think about, uh, you know, we’re in budget season right now for, for finance folks and we’re, we get very proud of the models that we’re building and we, you know, we’ll do our scenario analysis and we’ll have, we’ll do Sima crazy, you know, complicated forecasting, and we have all these assumptions and drivers and, you know, you have this massive spreadsheet or whatever tool you’re using to Mm-hmm. To build your models. And when you go to present this model to senior leadership or to the department heads, whoever it is, they’re never gonna, they don’t want to take the time to dive in and, and they don’t wanna follow, you know, do the trace formulas through Excel, right?
And all that. You have to give them the confidence and you, you have to, uh, you know, get them comfortable with the forecast that you’ve built. And I think for finance people, and, and I think of a, a classical education, you’re not trained on how to tell the story. You’re, you’re trained on how to build the model. You know, you can do the statistical modeling and all that. So if, if I’m a, a finance team, or if I’m leading a finance team, how can I help my, my people learn how to leverage storytelling to sort of translate these complex models and, and whatever data insights in that we’re, we’re sharing into these actionable business decisions. Like they’re, if, if you go in and you talk to a company, what are the key things you’re talking to them about to help move them along to where they can use storytelling to get buy-in and, and get this trust with their numbers?
Brent Dykes:
I believe that a lot of storytelling data stories is really around finding an insight. And, and the insight for me, how I define an insight is based on, uh, author Gary Klein, who’s a psychologist and author. And, and he said that an insight is an unexpected shift in the way we understand things. So for me, that is a critical thing that we’re looking to share whenever we have an insight, meaning, Hey, we, we thought that our customers liked this about us, and we’ve, you know, we’ve, we’ve surveyed them, we’ve done some studies, and we found actually they don’t like us for that. They actually like us for this other reason. You know, so maybe it’s, maybe we thought it was the compatibility of our products with other vendors, but no, it’s actually our, I’ll just make something up. User interface. And, and so we’ve been a lot of, we, maybe we’ve, as a company, we’ve spent a lot of time promoting our compatibility with other vendors.
We’ve developed, we’ve invested a lot of resources in developing new connections and integrations with other vendors. And yet the thing that our customers actually value about us is our user interface. And so from a marketing perspective, we’re probably not touting our user interface, we’re touting our network of, of integrations that we have. And, and so from a marketing perspective, that insight that we’ve now uncovered means we need to change our focus. We need to promote how great our user interface is and maybe how much better it is than our competitors in terms of product development. You know, we’re, again, we’re probably over, you know, oriented towards, um, developers working on new integrations, and we’re not maybe spending as many cycles on improving and enhancing the user interface because we didn’t realize it was so compelling in, in, in a selling feature of our product. So when we have these, uh, insights that we uncover, uh, the, the storytelling is, is really important because as I’ve talked to many people all the time, I’m, I’m sure there’s many finance teams out there that have uncovered really powerful insights about the market, about, uh, competitive opportunities, about operations, the different aspects of the business where expenses and different assets, you know, how they can be taken advantage of.
And, and yet when we go to communicate that to executives, we’re missing the mark. We’re providing, you know, some of the common mistakes we make is we provide too much information, right? So we, instead of putting things in the appendix and referencing them as needed, we say, oh, they might ask a question about this. So it’s going into the deck, you know, and so we go to a hundred slides, dense data tables, you know, very complicated models and all of this information that’s overwhelming for most business audiences and most stakeholders. And so we need to have that empathy and have that focus on the audience and figure out how can we communicate this information in a more clear manner. And the power of storytelling is, you know, if we think about how our brains work, it, our brains are trying to make sense of the world around us, right?
So they’re constantly putting information into narratives where, you know, our, our, our brain unconsciously is forming narratives constantly about the information. And so that’s where the data story comes in, because we take that facts, we take that insight, we wrap it in a package of supporting context and, and connecting the dots for the audience and, and sharing that in a way that then in the audience, they can, you know, unconsciously it’s almost like, oh wow, there’s the narrative. Oh, I’ll just grab that whole thing and, and embrace that as, you know, the new narrative that I’m gonna go move forward with. So storytelling is super powerful. It’s really, um, critical that we, we take advantage of, of the power of stories. Uh, ’cause they’re memorable, they’re engaging, and they’re persuasive. And, and I think sometimes I, I think I see this on the analytics side, probably on the finance side too, you might see this as that we wanna be objective. We don’t wanna persuade the audience one way or the other. Uh, but sometimes when we’re making recommendations, there’s a decision to be made. And, and we need to be persuasive with our insights. That, and an action needs to occur that we need to do something here. And we can’t miss fixing this problem and seizing this opportunity that we have in front of us.
Glenn Hopper:
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That storytelling it, it’s easy when everybody’s kind of on the same page and, you know, everything’s rosy and your trends all look great and everything <laugh>. But where it gets difficult is, and I think a, I see this a lot where, you know, the analyst is the person doing most of the work is, uh, you know, usually lower on the, um, on the management. Um, totem pole. Totem pole. So if you’re, if you’re getting pressure from, whether it’s the CEO or the division head or, or whomever who has an aggressive, and, and I’m just, I’m my brain’s around budget season right now, so I’m thinking <laugh>, you know, that the, the company goal is we’re gonna grow top line by 12% this year, and we’re gonna reduce these expenses and we’re gonna reduce churn and all this. And if you’re looking at historical data that says, well, actually our top line has been pretty flat for the last two quarters.
Our churn is higher than it has been for the past six quarters or whatever. And our expenses are going up, our new products aren’t doing, I mean, you have all these data lines, but you have the, uh, but if the narrative that’s coming from the top is, we’re gonna do these, do you have any guidance or, uh, maybe anecdotes about when the data is telling you something that’s contradictory to what the company is, is, is wanting to do, how do you communicate, how do you use that storytelling to say, Hey, this, you know, I understand I might be the junior person in the room, but I’m the one with all the data. And it’s saying this, I mean, how, how can, um, analysts use this data to, to convey a story that is maybe contrary to what the, uh, their bosses are are telling what they
Brent Dykes:
Wanna hear?
Glenn Hopper:
<laugh>? Yeah. Yeah, exactly. <laugh>,
Brent Dykes:
Yeah, no, those are the situations where I think data storytelling is important because it’s gonna be very hard for them to accept the numbers. You know, basically just here’s the numbers raw without that packaging and maybe that context to help them appreciate and fully realize what’s going on. That’s gonna be a very hard conversation to have though with an executive. ’cause they’ve kind of given you a mission. They’ve given you, Hey, here’s the story I wanna tell. Go find me some numbers that support this story. Right? That’s the hard challenge, I think with data storytelling. And there’s always the possibility that you may cherry pick the numbers to support the agenda that you want. Uh, and and that’s a real challenge that many of us face when we work in data. ’cause if I tell them the real story, they don’t want to hear that story.
You know, that’s not the, that’s not the story they wanna hear. So I think for me it’s, if I was in an environment where I was constantly having to cherry pick the numbers to tell certain stories, I would reevaluate where I’m working. Um, ’cause I just wouldn’t feel comfortable working in that environment. I would hope to work in an environment where I could share what the data’s really saying and obviously tell a story around it so that it’s, it’s approachable, it’s engaging that it connects with people and hopefully that people would wanna hear that story and would, you know, listen to the, what the numbers are really saying and, and then maybe come back around, okay, well that, that maybe was too aggressive. Let’s step it back a bit. Let’s change our estimates. Let’s you know, if we don’t have the data to support it.
And that goes back to building a data-driven culture, a data-driven culture. It’s not just about using data, right? Because I think there’s lots of cultures out there that have data, but don’t use it in the right ways. They use it to manipulate to, uh, forward their personal agendas that they have. And I wouldn’t consider that a data-driven culture. I think a data-driven culture is where leaders are willing to listen to the numbers. They’re, they’re realistic. They’re grounded in reality. And, and if, and if, and they’re, they’re also not in a shoot the messenger environment, right? That’s another is sometimes. Yeah. We, we come with a, uh, an insight from the data. That’s not what people want to hear. And then we get end up getting shot. ’cause you know, we told the executive a story or a, you know, we shared facts they didn’t wanna hear, and then that becomes our fault.
And that, again, is not a data-driven culture. So I think it’s, it’s really important to have a, a safe environment where financial analysts can with information that really is the reality of the business. You know? ’cause I think what happens, and I’ve worked in those companies, I’ve seen cases where executives didn’t wanna listen to the numbers and they start constructing this bubble, and it’s a bubble that’s based on this alternate narrative or view of the world that’s not real. And at some point it’s gonna pop, and when it pops, you know, if it pops in three years, that’s gonna do a lot more damage than if it popped in a couple weeks.
Glenn Hopper:
Yeah. And I think, and we’ve all kind of been there too, where and when, you know, we’re talking about data lexicon earlier, and I think, um, you know, that whole lies, dam lies and statistics comes about where it’s like, well, this metric doesn’t look that good. Let’s calculate it differently this quarter before we report to the, uh, border investors. And we’ll, we’ll put a footnote note in there. So we’re, we’re being, you know, fine, but we’re just gonna report this slightly differently. You can also fall into that kind of p hacking sort of thing where you’re <laugh>, um, you know, or the Malcolm Gladwell approach where you have a premise and then you just go try to cherry pick the data so that you prove your argument rather than you. So that’s, I guess that’s not data driven, that’s data pulled along to <laugh> to make the sale the lawyer approach or whatever. <laugh>,
You know, we haven’t talked systems and I, I, I suspect that in your, your teachings, it’s your, you’re kind of system agnostic. It’s whatever, whether you’re Power BI or Tableau or whatever, it’s, we still have to do the same kind of reporting and storytelling. I mean, what do you like for companies who are getting a handle on their data, they’re trying to move up in, in sort of that, that data maturity scale. Um, mm-hmm. <affirmative>, what are your recommendations for, um, you know, how to sort of wrangle it? And maybe if it starts, if you are starting sort of a, a data transformation process in finance and accounting, like how do you get buy-in from the other departments to get operations and sales and marketing and all brought in, like what are the steps that people need to start taking to improve their, not just data literacy, but just improve the, the culture towards, um, data-driven decision making?
Brent Dykes:
I looked at it as there’s, so one of the, I published a white paper on how do you build a data culture using data storytelling? And in there I have four kind of foundational layers that are needed to kind of build the kind of environment where data storytelling can flourish. And one of, obviously the basic bottom layer tier is having quality data that is, well actually no, let me start with the first. And that’s not, that’s the second one. The first one is having relevant data. Are we capturing data that’s aligned with the business goals and priorities of, of the organization? That’s the first thing. So we do, we have relevant data that that is gonna help us to make decisions. That’s the first thing. Second thing is quality, making sure that that data is trustworthy. And then from there, I think the next layer is to get, have the reporting infrastructure so that people have access to the information they need when they need it.
Uh, and so then that fosters data literacy in my mind. ’cause people have access to the numbers. They can start to share things, and then they’re gonna have questions on the reports. And, and that’s where you need to have that analytics infrastructure in place so that people can go in. Whether it, and that could be not just from a technology perspective, but that could also be from a personnel perspective, that there, you know, I’ve seen some organizations that invest heavily on the technology and then have almost no analyst capability to support the, you know, to drive the bus. Essentially. They, they bought the bus, but they didn’t buy any dr, you know, bus drivers to kind of drive it and having those four layers there. So you have the relevant data that you know, which, which is aligned with the strategy. You have the quality data, which means you probably have governance in place. You have the, the reporting infrastructure and business intelligence, and then also that additional analytics infrastructure for answering the questions and diving deeper with the help of analysts or AI or whatever. If you have all of those things in place, I think you have the ability to really leverage the data in meaningful ways and to tell stories and to share stories. And I, I think, you know, those are some
Glenn Hopper:
Critical things that are needed to kind of expand on that. And maybe it’s, you know, maybe it’s mindset, maybe it’s budget, maybe it’s like you said, ex executive buy-in. But as you work with these companies, what would you say distinguishes the data-driven organizations that successfully cultivate both the data literacy and storytelling from those that struggle. So you could come in and, you know, give them this whole kind of roadmap. And you may have a sense when you leave, these people aren’t gonna be able to do this. And I, I wonder what it is that sets the successes and, and failures apart.
Brent Dykes:
Well, it, it’s, if I looked at those different areas, so the first area, it’s when, often when I’ve worked with companies and I find that people just don’t find the data relevant. You know, there’s, there may be dashboards, reports going out, and they’re just like, I don’t even look at my dashboard ’cause it doesn’t have any of the information I need to hit my goals this, this quarter. And then I’m like, okay, well that’s a problem. You know, we’re not even capturing the data you need to make decisions. That’s, that’s gonna impede their ability to become more data literate, to share stories. Uh, if there’s any issues with the quality of the data now data’s never gonna be perfect. I also think that’s important that people, I think data, data savvy people get it that the data’s not always gonna be a hundred percent perfect.
I’ve seen some executives react to errors in the data and, and they react like, oh, well I can’t trust any of it. And I feel like that’s, that would, to me, would be a, uh, that would highlight that they’re maybe, maybe not as data savvy as they’re maybe putting out to the world. Because often there may be, in my oatmeal, there might be, you know, a, a hair and that doesn’t mean I’m gonna throw away the whole bowl of oatmeal. I’m gonna just pull that piece of hair out, say, okay, I’m not need that, but <laugh> the rest of this is useful and nourishing and, and I can get value from it. And, and I think unfortunately data is messy and, and it’s constantly, you know, causing issues. I could create a beautiful data story based on your data, but if nobody at your company trusts the numbers, like if I, if I said, oh yeah, I got the data from this data source, and everybody’s like, oh, well that, you know, if it came from that source, it’s garbage.
So it doesn’t matter how pretty I’ve made my data story or how compelling the narrative is, if the foundation of it is is bad. So that’s, those are a couple of areas. And then obviously access is another problem. I see. Whereas people either don’t know where to go to get the information they need, or they’re so overwhelmed by the, the volume of information out there and it’s so much noise compared to actual metrics or data that signaled to them, uh, that can be overwhelming. I, I, I once talked to a guy and he was bragging that he had 20,000 power BI reports at his company, <laugh>. And I was like, I think he’s bragging. And I was like, that to me is not a brag that that means that probably a lot of people are trying to get information and are struggling ’cause just so many iterations of reports being created. And I mean, yeah, I guess it could be a, it could be a sign that you’ve got a data-driven culture, but I I, my experience would tell me that no, it’s probably a, a cry for help, really that’s not really being answered. Uh, ’cause so many people are struggling to get the information they need and having to iterate on so many different Power BI reports. And I had one client who said, we, we’ve gone from death
Glenn Hopper:
By PowerPoint to death by Power bi. You see managers who they want data and they’ve got all got ideas on, okay, if I could just see it this way. And I, and anyone who’s in an analytic shop, it’s kind of, you’ve sort of, uh, you know, you make your living on ad hoc reports, so the backlog never, <laugh> never goes away. And it’s always some different manager wants to see a, a report a different way and you know, a slight tweak or whatever to it. But I also think that you, you, you mentioned dashboards in there too, and I think about sort of the canned dashboards that are in ERP systems or something that, you know, it’s so easy to throw out a dashboard and, and Tableau or, or Power BI and, and tweak it. But, you know, if you’re just looking at lagging indicators and, and like you said, it’s all this noise.
If you’re just seeing these, all these pie charts and graphs and, and everything, but they don’t, they’re either lagging indicator or they don’t mean anything. It’s, it’s, you have to narrow in and find where are the levers, what are the, what are the key, the key metrics that I can look at that I, that tell me something and that I can do something about. Right. That’s, you know, that’s the, the signal that you’ve gotta pull out of there. And it’s, it’s hard to get, I mean, I, I think you have to be pretty evolved in your data literacy to be able to, to get to that. But that’s, that’s the holy grail. I mean, that’s where you, you want to get with your analytics.
Brent Dykes:
And one of the things that, you know, sometimes people will look at like, well, AI is gonna solve these gaps in data literacy. I think they’re gonna exacerbate the issue because what’s, you know, I’ve noticed in some situations where being an expert on a topic, I’ve used gen AI to come up with some different options and, and recently this happened where I was generating some different options and, uh, at first blush I was like, oh, this is great. Wow, what so powerful. It, it, it generated the this information really quickly and gave me some different, uh, examples. And, and I was super happy with it. And then I was like, wait a second. And when I looked at it, I was like, this calculation is completely off. Like, and the same issue was being made the same, you know, jump in logic, um, that the, that the Gen AI tool was using was being, the same mistake was being made across all these examples.
Now, if I was not an expert in this area, I might not have realized that this mistake was being made and I might have ran with those, uh, calculations and, and yet I was, because I had expertise in this area, I could spot the problems. Now it was still a still, it was still a huge time saver because I didn’t have to do all of the calculations myself, but I did have to catch a critical calculation that basically ruined all of <laugh>, all of the answers it gave. So, you know, that’s the, you know, I, I think that’s important that when we look at Gen AI and the power of ai, if we don’t have people trained to a certain level, I think it can be problematic empowering them with gen AI to do things if they don’t have some knowledge, some basic understanding of whether that’s data principles or how calculations are being made or statistics at a basic level, I think there you, you will run into problems and exacerbate the problem significantly.
Glenn Hopper:
Yeah. And you know, you really, you’re singing the, uh, song of my people here, <laugh>. And I’m, this is so important to me and it’s, it’s weird when I’m talking to finance people who are they, you know, they say, I am a domain expert on finance and this is, you know, this is what I went to school for. This is what I wanna do. And I’m telling them, yeah, but the world is changing quickly. You need to expand your area of expertise and you need to know more about AI because you’re gonna be using it. And if you’re gonna be turning over decisions and number crunching to it, and, and I say if you are going to be to some extent, we all are going to be, it is, it’s like the internet. I mean, it’s the wave of ai. We’re early, you know, we’re a OL dial up phases now and it’s the hallucinations and there are problems, but we’re, the AI we’re using today is the worst AI we’re ever gonna use. So it’s gonna happen. And so if you are gonna use it, if you don’t understand the fundamentals of, I’m not saying you have to become a machine learning engineer, but you need to know the difference between I, I’m classification, clustering, and regression. Right. And you need to know the basics of how these models are working so that you know what to ask it and when to trust it and um, yeah.
Brent Dykes:
When to push back.
Glenn Hopper:
Yeah, exactly. So it’s, um, it’s, it’s very important and it’s, it’s just, it’s kind of the nature of it know human in the loop. A lot of things we can do, but as the AI gets more powerful, we need to also get more powerful so that we can become, you know, better users of it and not replaced by it.
Brent Dykes:
Right. Yeah.
Glenn Hopper:
As we, we’ve talked about a lot about gen ai and there are a lot of tools out there, and maybe it’s, maybe it’s all about generative ai, but it, as you look at data and analytics, are there trends? It, it, it could just be gen ai, maybe there’s more, but that you’re excited about over that you see over the next two or five or 10 years? What, what’s going on in the space?
Brent Dykes:
Yeah, I mean, I, I think it’s, it’s interesting how AI will augment our abilities to find insights. So that’s the key area. I think that’s always one of the big challenges. How do we find these insights and combing as the data sets get larger and larger? I think it’s getting harder and harder for us to manually, you know, even putting all this data in a tool to kind of process and go through, you know, we’re gonna lead, we’re gonna need these large learning models to really process the information and go through it. So I’m excited about how it’s gonna help us to find insights and extract meaningful anomalies, patterns and trends from the data. And then obviously on the storytelling side, I, I obviously still feel like we will play a role in that, but it’ll be interesting to see how, you know, I, I kinda like what you’re saying.
I mean, where a OL level internet, you know, with our AI capabilities and how will that evolve? How much will we almost have like an agent or an assistant who’s helping us to craft these data stories and, and it, and, and we’re becoming, you know, they’re learning from us, we’re learning from them, and, and then we become a team where maybe down the road we have like these, and maybe that, I’m just think, I’m just thinking out loud now, but maybe we have some kind of personalized personal agent that comes with us. So it’s almost like, you know, when we go from job to job, I don’t, you probably can’t take the data with you, but maybe you can take this personal AI that you’ve developed over time, you know, working with it for two or three years. You’ve now built a, an agent that knows you, works well with you, knows your language, has, you know, kind of that prompting and back and forth feedback.
It compliments you in areas where you’re weak and then you compliment it in areas where it’s weak. I think that could be really interesting, you know, and, and in that combination, uh, I don’t think we’re there yet, but yeah, I see all kinds of ways in which we need to adjust the way we work to this new tool set that we get from ai. And I think it’s gonna have a massive impact on, on how we work today. I mean, if we, in two, three years in the future, looking back, I think we’re, uh, we’re gonna be surprised at how much AI is a part of how we work going forward.
Glenn Hopper:
Yeah. And I’m, I’m totally with you on, on the idea of an agent and, you know, the, there’s sort of this cross between a, a digital twin slash understudy slash coach slash intern that works for you. Yeah. You know, it’s, and just a way to augment and, and kind of fill in those, uh, the gaps of what you can’t do or, or, or, or someone you can, you know, someone I’m anthropomorphizing here, but <laugh> an entity that you can, you know, it’s that whole co intelligence idea, right? Yeah. This is my custom agent or my team of custom agents, you
Brent Dykes:
Know? Right. Yeah. Maybe have a team of them for different purposes. I mean, you know, if we go from job to job or role to role, does that come with us? You know, can we, I, I would wanna, I, it would be, it would be sad if you build up, you work at one company for two or three years, build up this agent that’s really good, and then you have to leave and you can’t, and probably there might be a corporate thing where they don’t want you to take that with you. Probably they don’t ’cause if IP and stuff, but that would be a sad day. You’d have to break up with your agent and then start over with a new agent and you’re like, oh man, I miss Ted, you know, <laugh>, I wish I had Ted again, but now I’ve got, I’ve got Dan and Dan as dumb as a stump and <laugh>. I’m gonna have to train them up again. But, but yeah, no, that’s, that’s an interesting, um, premise. And who know,
Glenn Hopper:
Think we’re, I think we’re writing a sci-fi movie right now, but, or I think we’re, the question is, will the technology get there first to make it science, not science fiction, or will we finish our screenplay first? I dunno, <laugh>, I dunno, I dunno. Let’s start writing.
Brent Dykes:
Yeah, let’s put that into Gen AI and see what it comes up with. A
Glenn Hopper:
Script. Perfect. <laugh>. Well, we are running up on time and there’s, there’s two questions that we ask all of our guests. We, um, you know, we dove straight into all the work. So we do have, uh, we, we like to get some piece of personal information about all of our guests sets, uh, before they go. So what’s something that, um, maybe not many people know about you? Something we can’t find just by Googling you? Uh, any, uh, interesting tidbits you could share with our audience? <laugh>?
Brent Dykes:
Yeah, I’m a big comic book collector. I grew up on Marvel, uh, comic books. And so after being a teenager, my, my collection was stolen. Parts of my collection were stolen from me, and so I abandoned the hobby. And then about three or four years ago, right around the pandemic, I started to, to get back or just before the pandemic, I started to get back into collecting comic books again and had a little bit more money than I did as a teenager. So, uh, maybe my wife would say that was not a good thing, but <laugh>, um,
Glenn Hopper:
Definitely fun. Right. So were, so were you going back and buying, uh, issues that you had already bought before that had had been stolen or lost over the years? I got some And you’re now paying a premium for ’em, but you
Brent Dykes:
<laugh> Yeah, no, I got, I got some of the ones that were stolen from me, and then I, I, I got some of them graded. So you, you can get them certified graded and, and see what their, and then the, their value becomes more clear. So I had the first appearance of Wolverine. Uh, I have the first appearance of the Green Goblin in Spider-Man. Um, so I have some comic books that I, that weren’t stolen luckily, and I was able to get those graded and see what they’re worth. So it’s fun. That’s
Glenn Hopper:
Great. I, uh, so similarly, I’ve collected comic books for years and I, um, got rid of a bunch of ’em when I moved outta my, my house. But, um, I had the original Watchmen when they were coming out Oh, nice. Monthly. Yeah. So one, you know, went through the 12 issues and all, and I, and I had stuck them in, you know, they weren’t in perfect shape, but I put ’em board the cardboard in and put ’em in the bag and all that. And so found those. I, I’ve haven’t gotten ’em valued. I just love saying, see, I knew <laugh>, I had the original watch <laugh>. Yeah. And I, you know, I didn’t keep a whole lot but the, the Watchman ones, for whatever reason I did <laugh>. Yeah. Um, alright. In this question we ask everyone, what is your favorite Excel function and why?
Brent Dykes:
I would say this is a tough one. ’cause I would say the index match before, but I don’t use that as much right now. And so I would say my favorite right now is the count if, or SUMIF, and the reason why is because I, I do a fair amount of aggregating of, uh, survey data for my workshops. And so count if and some if are great, uh, great functions for just getting that quick number I need and works like magic
Glenn Hopper:
An add-on question. You know, as an analytics person, I know, you know, we always, everybody loves to hate Excel and we, we knock it all the time, but it’s, you know, even when I talk to data scientists, there’s still so much we do in Excel. Would you say is Excel kind of where you start? And if, and I know if you’re doing surveys, you’re not talking about big data and all that and excel’s perfectly fine for it. Would you say when you’re doing an actual analytics problem that you generally start with Excel and, and do a lot of work there, or not necessarily?
Brent Dykes:
Yeah, I would say I, I think Excel’s a great starting point, you know, and there’s, there’s people out there that, that have shown me you can do a lot more with it. Even advanced kind of data science stuff related. If you, if you heard of David Langer, he does a lot of Python and R in Excel and so I, I see it as a ubiquitous tool. I like it ’cause it’s ubiquitous and when I talk, I don’t really, I don’t focus on Power BI or Tableau or any of the vendors, but usually I have to pick one. And so I’ll, I’ll do my visualizations in Excel and use that, that to kind of show, you know, like, Hey, you can do a lot of stuff later. A lot of data storytelling using Chart space from Excel. And then usually what I find is often there are limitations in even a tool like Excel. And so when I bring it into PowerPoint, you know, maybe I don’t want to keep the, the chart titles in in Excel, I’ll, I’ll just, I’ll write them in the slide itself in PowerPoint or direct labeling is sometimes challenging in Excel. So I’ll do that in PowerPoint. I would say my go-to combination is Excel plus PowerPoint. I’ll, I’ll use them both as a mash, a mashup and usually I can create very powerful data scenes for my data stories using those two tools.
Glenn Hopper:
And I guess finally, um, how can our listeners connect with you? How can they, to learn more about your work, to find your books and, and all that?
Brent Dykes:
Yeah, I mean, uh, my book Effective Data Storytelling is on Amazon, but if you wanna dig more into the book or, or look at workshop services or training options I have, you can go to effective data storytelling.com. Uh, I also post a lot of content on LinkedIn and so if you look for me, Brent Dykes on LinkedIn, I’m constantly sharing different posts there on, on analytics or data storytelling or data visualization, data culture, lots of different posts there. So always excited to make new connections on LinkedIn.
Glenn Hopper:
Great. And we’ll be sure and put the URL for your website and your profile and in the show notes here too. So. Well, Brent really, really enjoyed the conversation. Thank you so much for coming on. Thanks
