MindSpeaking Podcast Episode 23 - Brent Dykes , Author, Founder and Chief Data Storyteller at AnalyticsHero
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Timestamps:
0:00 - Data presentation challenges
0:45 - Podcast introduction
2:55 - Common data presentation issues
4:11 - Preventing information overload
6:35 - Addressing mixed audiences
10:49 - Strategies for diverse audiences
11:46 - Handling data misuse
14:53 - Fostering a data culture
17:23 - Delivering bad news
23:37 - Empathy in data storytelling
26:55 - Preparing executives for surprises
27:33 - Handling executive summary requests
33:41 - Benefits of data stories
35:00 - Closing remarks and invitation
Summary:
Brent Dykes and Gilbert Eijkelenboom discussed common challenges in data presentations, such as overwhelming audiences with too much information and making data more understandable and persuasive. They shared insights on how to address these challenges in real-life scenarios, including presenting data to non-technical audiences and creating engaging presentations that drive business impact. They emphasized the importance of empathy and sensitivity when conveying bad news through data storytelling, and discussed the need for effective data storytelling in today's fast-paced business environment. They highlighted the importance of establishing a shared understanding, avoiding surprising or cornering executives with negative information, and using data trailers to provide a glimpse into the main insights and findings of a data analysis.
Introduction
Gilbert Eijkelenboom:
Today's guest on the MindSpeaking podcast is Brent Dykes and yes, this is the second time that Brent is coming on the show. And it will be a special episode because it will be a bit shorter, with a very clear focus on what are common challenges within data analytics when we present insights to our audience. We're going to dive into some common challenges like how to give bad news with data if people don't like the outcome of what you're presenting, how to deal with executives who say you have only two minutes to produce your presentation, or how to present with a mixed audience. So again, Brent Dykes is not a newcomer here. But he is the author of Effective Data Storytelling, a book I recommend to everyone. And he has his own company, Tell Stories with Data and gives workshops around data storytelling. So I'm excited to dive into today's episode, which is focused on the common challenges of presenting data. Enjoy. Alright, welcome, Brent.
Brent Dykes:
Hey, good to be here, Gilbert.
Common data presentation issues
Gilbert Eijkelenboom:
Absolutely. I'm happy to speak again. We've recorded a podcast episode before. We talked about a different, a lot of different topics, of course about data presentation, data storytelling, mainly, we talked about your book, Effective Data Storytelling, I think it's a great book. I recommend it to everyone. In this short podcast episode, I would love to talk about common challenges that people have in data presentations. And maybe the first one, what do you see as common challenges in data presentation? What have you faced yourself or what have you heard from other people in your training and the workshops that you do?
Preventing information overload
Brent Dykes:
Yeah, I mean, there's lots of challenges that people have. I mean, one of the common ones that comes up a lot is just or one of the complaints I hear from maybe managers or people that are concerned about what their teams are presenting is just overwhelming their audiences with too much information. I think that's one of the main issues that I see a lot. Just there's just a ton, you know, people are packing in multiple charts into one slide. You know, if there's any whitespace, let's fill that with text. Let's you know, let's pack it in as good as we can and and then that, you know, that becomes a problem on the receiving end because just too much information, too much going on. And then it just gets harder for the audience to kind of really understand what your key message is, what your key insights are and what to make, you know, make. It makes it harder for them to make sense of what you're really communicating to them and what they need to pay attention to.
Gilbert Eijkelenboom:
100%. This is also the one I hear the most, I think, and what would you say is one tip or two tips to prevent that from happening apart from not putting so much content on one slide. But do you have any other thoughts how to prevent that?
Addressing mixed audiences
Brent Dykes:
I mean, I think one thing I try to focus on with each slide, you know, I talk about slides being data scenes in a data story. And so, one simple thing would be to just make sure that every scene or slide is focused on conveying one key point. I think the danger is when you're trying to use one slide to kind of communicate three different things or four different things, then that's where you get into trouble. Right? So I think it's it's about focusing on your each slide. What is the purpose of the slide what is it trying to do? Is it you know, hopefully, you have some kind of narrative structure that you've worked on and developed in the background, but, but then that becomes the question, what is the purpose of each slide and in some cases, you might even realize, you know, one of the challenges is we when we're doing analysis when we're doing all this complex modeling and everything we want to show our work, right, we want to we want to show what we did that we've been working hard to concise that we are sharing and, and sometimes we need to step back and say, What is the purpose of sharing this chart or sharing the slide is, is it strengthening the data story? Or is it potentially just being tangential or extraneous, you know, it's not really contributing anything to the story, and sometimes that's hard for us as individuals to do that. And that might be where we need to rely on a coworker or partner, friends to kind of come in and say, yeah, I don't, I don't think you need to include this information. It's not necessary to your story. It actually detracts.
Gilbert Eijkelenboom:
I love that. I think when we do that, we also have less a smaller chance that people in the audience have different understandings of each slide and that they go a different direction along with these different directions. Exactly. It's very hard to manage the attention if people are all over the place, right? If every slide has this one key message one purpose, it's way easier to synchronize those brains, right and get them in a certain direction. Yeah. And maybe related to that. But how do you deal with it makes audience where this might happen more frequently, where people see a slide and then process that in a different way or they have a different perspective. How do you deal with mixed audiences where you're present? Your day tell your story. to different kinds of roles or personalities in the room?
Brent Dykes:
Yeah, I think I mean, mixed audience is always a challenge. Because people are going to have different interests, they're going to have maybe different levels of knowledge or understanding of the topic that you're talking about. And so I think going into each kind of scenario where you're presenting information you need to decide upfront, who needs to do something with this information? You know, maybe there's a if you are familiar with the RACI model, right? So there's What is it now spacing on it? There's responsibility responsible, accountable, consultative, and warm or something like that? Yeah. So I think that's a good model to kind of think about, like who actually needs to be accountable for this who's going to need to take action on this who's going to need to make a decision. There may be other people in the room that you know, obviously are interested in the topic. They need to learn about it. But at the end of the day, it's going to be marketing that's going to be making the decision finance can influence the decision but they're not the ones making the ultimate decision. The CMO is going to be the one that's going to be making the decision. And so in some cases, you may need to prioritize your focus towards that key decision maker, that stakeholder that's going to be making the decision. In other cases, maybe there isn't one group that needs to make maybe it's a collective we need to get everybody on board. And in some cases, I might even say, you know, like, say you're presenting to technical people and business people there if there's an opportunity to break that meeting into two meetings. Maybe you cover off the technical details with the technical audience to not lose the business audience in the minutia of detail. And on the other side, you know, the business side, it talking to them about the business terms, the ramifications, the key things that they need to do and have control and influence over. So that's another strategy that you could split the meeting into two audiences if you can't prioritize. And then a third option would be just, again, maybe you can't do two separate meetings. Maybe you can't prioritize one audience over another. Then I think the third option would be to kind of indicate to people that you're going to address each of their areas of concern, but it's going to be in this order. You know, so people don't start jumping and well, what about the details of this? What about? No, no, I'm gonna get to that. But first, we're going to talk about the business side of things. And then in the second half of the presentation, we're going to talk about the detail of how we're gonna do that. So I think it's just communicating upfront, like everybody's going to, you know, understand the audience, you understand there's a mixed audience. There's going to be people from different camps who want to hear different things. And you acknowledge that at the outset that okay, here's what we're going to talk about. First we're going to cover this, then we're going to get into this. So that's, that's an indicator to the people who are, you know, otherwise, they're gonna be raising their hand. What about this, whatever? No, no, like, we're gonna get there, but you need to hold those questions until we get through this initial setup. So the business people understand what what's going to happen. So those are three three ideas that I have for a mixed audience.
Gilbert Eijkelenboom:
Yeah, I liked that. I liked the last one. I liked the last one where you proactively communicate that you will address each group's concerns, right so that people will stay patient and not interrupt you when they see you're addressing another group. Right. So nice. Three things. So the prioritizing certain stakeholders, decision makers, maybe using the RACI model, split up the meeting, and indicating that you're going to address everything step by step before you start to dive in. One thing that makes many people angry, especially in data, data analytics, is when people don't try don't use the data to make better decisions, but actually to justify decisions that they have already made. Right and to show that they made the right decision and to even push you as a data professional to get the data so that they are right. How do you deal with that?
Brent Dykes:
I mean, ultimately, you may not have a say and whether or not you do that if you want to stay employed, you may not have that ability to kind of push back, you know, and so I you know, I like to have integrity as a analyst and as a data professional. So, you know, I might look at the other side, you know, and might try and get a sense for what's really going on here. You could go into that situation and just cherry pick the details that are gonna support the case. You know, I just don't feel like that's the best for long term success when we are just looking at justifying past decisions. So I would kind of say, you know, like, I know there's situations where that's needed, and that's the request that you get from high up. If that's all I'm doing all the time at the company I'm working for. I would start to be concerned that this is not really a data culture. This is this is just a culture that's using data selectively when it and when it supports their, their agendas. And so, for me, I don't know how happy I would be working in that kind of environment for a long time. I probably want to move to a culture where they're looking at the data first and making decisions based on what the data is telling them as opposed to coming back around and justifying everything based on you know, what, what they've already made. So it's a tricky situation. I mean, it's human nature, you know, you're going to run into those situations where that's going to happen. Again, if it's occasional ad hoc, and, you know, and maybe the intention of the decision makers, not malicious just trying to support his or her decision and kind of get buy in or support for it. You know, it's harmless, but I would also be if I weren't getting the right culture, I think they would also you know, yeah, let's try and support your decision but can I also evaluate this and see what really happened, you know, and if it you know, maybe that would give me comfort to like, if I go back and check and, you know, I tried to find if there's any data that doesn't support the decision, and I can't find any that's great. You know, I've I've paid this this was a good decision, you know. But again, you have to have a manager who's open to that, who's open minded is willing to listen to the data. And there are many managers out there that No, that's no, we're, we need to support you know, I need this justify my decision and that's, that's your goal. That's your mission. And yeah, that's that's a tricky situation. I, I don't know if I could endure that. If it was that was the consistent focus of what I was doing and analytics.
Gilbert Eijkelenboom:
To which extent do we have the ability to influence this data culture to make it happen? Maybe not borrowed by ourselves, but maybe being the start of that change within an organization? Yeah.
Brent Dykes:
I mean, I think it's working with your manager or working with your boss. You know, if there's interest in kind of fostering that kind of culture. I think that it's a good example that I ran into was I was co presenting at a at a an a breakout session many years ago. With it. He was the VP at A E commerce, luxury brands ecommerce site. And, and I was presenting with him and his his direct report who was over analytics. And they shared a story with me that he had always been very data driven, and he encouraged his team and heard this direct report to always be data driven. And there was a situation where, at this luxury ecommerce site, they're actually introducing a clearance section to their website. Now, his previous experience he had worked in a clearance only focused kind of clothing brand. And so he had a lot of experience in this area. And so he was like, Okay, this is how we're going to do this. This is what it should look like on the website. And he kind of reverted to his you know, his experience which he had extensive years of experience and, and his direct report held him accountable and said, you know, we always test everything we do. That's, that's what you've taught our team to do that we always test. We never go with just our gut. And he, you know, he trained his team well, and he respected her and her push back and he said, okay, yeah, okay, let's let's do the test. I'm betting my money on option A. And the funny thing is, when they did the tests is Option A was the weakest performing of the options. So, again, data one, and I think that's, you know, that's part where he instilled this culture of test and learn and being data driven, and his team held him accountable. And ultimately, it prevented him from making a bad decision. So I think there's some, you know, as a team if we can collectively build that accountability, supporting data driven decision making, even when our gut tells us that it's, you know, this is the only way to do things. I think, no, we've got we've now got this mentality. We're always going to look to the data. We're going to test ideas we're going to optimize. And, you know, I think that's how you build a data culture.
Gilbert Eijkelenboom:
This great, thank you for sharing those steps. And so far, we talked about, you know, how to deal with people that will justify their decisions based on the data. Sometimes we also need to give bad news with data. So we know that you know, the insights that we're about the present, that people in the audience will not like seeing them. What can we do to still make that message land and be embraced even though it's negative or bad news?
Brent Dykes:
Right? Yeah, I mean, I think definitely instilling a data driven culture is a key part of it, you know, and that's obviously a process. It takes many it's not something in a week, you can quickly develop, it's going to take weeks and months and years, perhaps to kind of get that culture in place. Obviously, that I think that makes it easier when we have bad news to share. Because people are looking at the data to not just justify or in some cases, you know, I bet I've seen companies where they don't like sharing bad news. And so they creep in general. Yeah, like they it's always got to be positive nobody's you know, no accountability. No, you know, and again, it's there's some cultures where bad results are used as a form like a stick, like as punishment, to you know, vendettas against other teams or to push somebody down so they can get ahead. That's obviously bad. But then again, there's also cultures where they do not want to share bad news. They do not want to make anybody feel bad or look bad. And then you create, you know, almost this alternate alternate reality, where everything is good, everything is awesome. You know, it's like The Lego Movie. Everything is awesome. You know, now you can't make any mistakes. And then you basically do not correct actions decisions over time that that get more and more progressively palpably. The impact of not making changes at some point in the future gets really really bad to the point where you know, entire teams can be fired because you know, you've completely messed up on on something or something like that. So, I think it's, you're obviously going to have an openness and open culture to receiving bad news. And then as I'm on our side, you know, as the data storytellers or the people presenting bad news, I think we have to show empathy, right? We have to understand that you know, there's subtle ways that we can position this that it's not about so and so making a bad decision. But, you know, ultimately, we're not serving the interests of our company. We're not helping our customers, we're not helping our or our shareholders or whatever. You know, it may be not direct to the blame at anybody. And that's, that's an interesting thing. You know, I've seen some cultures at organizations where it's about blaming people. And I think that's not what we should be doing with data. It's really about holding people accountable, so that we can make better decisions, better actions in the future. And so I think that might be a shift, you know, that may need to come at some companies where it's, it's not about placing blame, and creating a scapegoat for some bad decision or some outcome that we didn't like. It's how do we learn from this and move forward? What can we what lessons can we learn from this? How can we improve how do we avoid making the same mistake in the future? How do we make sure you know, what processes what things you know? And so I think you could turn it from a negative into a positive or at least, maybe not entirely 100% positive, but at least, hey, there's, you know, we can get better from this. This, this was a mistake. There's some things that were out of our control, and then oh, look, there were some things that were in our control that you know, going forward, we can change and introduce new processes or new steps that help us to avoid you know, this problem going forward. So it's, it's delicate, you know, I, one of the one of the things people asked me is how long should my data story be? You know, that there may be a question, how long should my data story be and one of the criterias that I look at is, what's the nature of the topic? You know, if it's a sensitive topic, where we're going back to a team and telling them that their program sucks, you know, or basically has basically failed and a multiple levels. That may be a longer data story, because we need to be sensitive around what we share and really kind of help them understand. You know, we don't want to be just, you know, you suck. here's the, here's the metrics that show that you suck, and then leave it at that know that I think, you know, we might have to weave in, here's the positive, but also, here's the negative, you know, and here's, here's something we can learn from this. So I think, in those situations, you know, the, the situation and how sensitive the the audience may be to the bad news is a factor in how we craft our data story that we have to be sensitive and empathetic to them. And ultimately, we want them to understand what we're communicating. You know, because one of the things one of the natural reactions of audiences when they're hearing bad news is they will attack the data. They'll say, oh, that data, you can't trust that data, or you don't have all the data you need or, or, you know, then they start criticizing your analysis and, and stuff and, and then people get on defensive and they're not going to, they're not going to listen to what the information can teach them, you know, and that's how we can improve how we can get better how we can make better decisions going forward. Yeah, so those are some initial thoughts. I mean, I'd love to get your take, what are your what do you think? How do you what do you tell your people when they bring up you know, the bad news? What would you recommend to them?
Empathy in data storytelling
Gilbert Eijkelenboom:
There's a thing I've read like a year ago which was not about data analytics at all, but there was this article which had this headline, intent before content, and it's something that stuck with me. So what it means is that first you show your intent, so it's another exactly the all those things you're saying it's not about blaming people. It's not about making any any negative about this, but we're here to just get the data on the table so that we can all learn and benefit so that we create a better organization get more sales or improve customer loyalty, whatever the shared goals are, or the shared goals of the of the audience and emphasize that you're on the same team emphasize the common ground and and also mentioned Hey, I have some data to share that you might not like. So then they are kind of prepared instead of receiving a punch out of out of nowhere. So now they can hide a little bit and prepare mentally right there to get less defensive. So so intent before content, so intent, saying hey, it's not my intention to give a big punch. Yeah, but it might hurt a little bit, but I'm on your team. So here's the here's the data.
Preparing executives for surprises
Brent Dykes:
And here's another thought to like with your punch analogy. I think one of the other things too, that many executives do not like are surprises, right? And sometimes when we're sharing data stories, we have surprises. So in some cases where you feel like oh, I've got a, a negative surprise here. And I know that maybe the a particular executive or decision maker may be embarrassed by this being shared with a wider audience. It may be where you need to do a pre meeting with them. Give them a heads up. So that coming into that meeting, you know, and maybe it's them kind of think oh, okay, good to know. Have you also looked at this and this was another thing that we were trying to achieve with this initiative and and maybe there's some positives from that, but anyways, not to spin it in a different way. But I think putting executives in a situation where they're very defensive, can ruin the positive outcomes or changes that can occur and so you'd never want to corner somebody, you know, where they feel like, oh, my gosh, you know, you've you're attacking me. I feel like you're, you're exposing me, you know, making me look bad. I think, you know, the good thing to do in that situation may be to do a pre meeting with them to give them a heads up and maybe take their feedback on how this can be positioned in a way that you know, is positive rather than only negative or, or centered around. You know, maybe people could view it as making them look really bad when you know, the path forward can can lead to some good as well. So,
Gilbert Eijkelenboom:
Right, and it gives them time to prepare and think, you know, what might be next steps. So to OSHA maybe exactly fix this, what
Brent Dykes:
Can we you know, so they can be part of the solution going forward, rather than just, you know, them being surprised and then like, getting defensive.
Handling executive summary requests
Gilbert Eijkelenboom:
Exactly. Yeah. All right, then. The last question is also a very frequent one that I get in my trainings and you probably as well. How do you deal with people who say Hey, you, you have two minutes, just give me the insights. Or often this is a complaint people get when they talk to executives who look on the watch before the meeting started? Even right. So how do you how do you do that? Just give me the data.
Brent Dykes:
I mean, one of the main ways that I talk about it in my book in my training is just something I call the data trailer. So you know, when you look at a data story, you can go walk them through the whole all of I mean, the magic of a data story is you give them a very comprehensive understanding of everything behind the aha moment and you know, the information they need to kind of make a decision and including recommendations and things like that. And if you only have two minutes, it's hard to build out the entire story and really give them a deep understanding of what's going on. And so that's always a concern to me that that, in some cases, when we just they just want to hear the insight. And that's it. They don't want to hear anything else. That they're not getting the whole story. They're not going to really understand this at the same level. So I use the data trailer. And so my my model of the narrative arc, there's something I call a hook, which is kind of like something in the data that maybe there's a spike in a particular metric or a decrease in a particular metric, something that would catch the audience's attention, you know, oh, our sales went up 200% Our sales went down to 100%, and this quarter, or for this product, or whatever. And that's gonna get the attention of the audience. And then we skip all of the build up to our aha moment, which is our main insight. And so we share the hook with the aha moment. And so we might say, you know, we, our sales jumped to under 200%. And we found that we can actually generate an additional $20 million in sales this next quarter, because we found that there's a supplier, a competitor that is having supply chain issues or something like that. And so we you know, and then that decision maker at that point can get basically the two minute version of the data story. It's not a story. I'm not going to call it a story. It's a it's basically a data trailer like a movie trailer. That's kind of like the worst movie trailer you've ever seen, because it's giving away the climax of the movie. But what what we're trying to do with that data trailer is trying to pique their interest, get them to say, Hmm, this is really interesting. Can you tell me more about this supplier? You know, what's going on with them? Or how would we address this? You know, what's, what's the opportunity, like, you know, if they start to have questions about it, now, I think we have an opportunity to tell them the rest of the story and, you know, we have their buy in and and they're going to want to hear the rest of the story. So that's in that situation. I think, you know, I when I look at the executives, they've been kind of programmed to expect executive summaries there. They're obviously context switching throughout the day between different meetings and, and oftenly. I think, I think with with data and how it's been misused in the past you've had many people presenting data in inefficient ways in which they're sharing their overload. You know, they're doing data dumps, and executives got tired of being like, where's this going? You're not I don't have a
clear direction of where you're taking me. You know, why am I sitting here my time, you know, I've got you know, you're wasting my time. And I think that's because a lot of us on the data side haven't been communicating very well. And so now, the, the fallback position for them is just give me the executive summary because I don't I don't trust you to use my time effectively. That's basically what they're saying. Right? Like, you are efficiently. You're, you're you're going to waste my time. And so I think that's where data storytelling comes in that it's not going to be a waste of your time, but we still have this old culture of the executive summary. So I see the data trailer is a way to kind of get people to get interested in hearing the rest of the data story and, and let's say we share a data data trailer with somebody in there and they're like, Okay, that's, you know, not interested. Okay, well, that's great too, because I'm not going to waste their time. I'm not gonna waste my time. And if, if, if that data trailer didn't get them interested in hearing the rest of the story, then clearly, you know, it's not worth their time or my time to, to, you know, spend any more time doing that. So, you know, I think that's that's kind of my way of addressing it. And, you know, I think there's still a need for a quick summary. But I also want to tell people, you know, that is not a story. You know, like, think about the, you know, the example I use in my trainings today. I talked about Empire Strikes Back, right. So, if you were to do the data trailer of The Empire Strikes Back and executive summary of it, you know, okay, Darth Vader is Luke, or Luke Skywalker is Darth Vader's son. That's the executive summary of that movie, and for the main takeaway, and does that does that build up does that you know, have the same impact as watching the entire movie and getting to that dramatic scene and no, there's no tension there's no build up there's no connection with the characters. It's, you know, it's completely devoid of that. So it's, it's efficient. executive summaries are efficient, but are they always effective? You know, and that's, that's the thing where I think a data story can outperform in that area by by really looking at something comprehensively helping people to kind of understand the topic that we're talking about, at a deep level and also understand the significance and the urgency of taking action. You know, those are some of the benefits I think we lose when we just go with the executive summary that you know, again, doesn't have the full context doesn't have the full depth of understanding that you would get in a data story.
Benefits of data stories
Gilbert Eijkelenboom:
That makes that makes a lot of sense. And because I think a data story helps to make it more understandable and and also more persuasive. Right. There's ways that people need to act as you mentioned, they understand the urgency more ad, it's not just Huh, that's interesting, but they actually want to do something with it. And that's what we all data professionals want, right? Not just to do the analysis and then see that people ignore what we're doing, but actually have the stuff implemented. And also like the point about the old culture, right, the executive summary culture, because so many data professionals have presented it best, too much information. So now people don't have enough trust. And I think there's of course, some ways to build trust with executives or people who might be more impatient before that session so that they don't assume that you're going to waste it waste their time. Yeah, so great insights. Alright, so that that was it for for today. It was really fun to have this brief conversation about you know, key challenges in data presentation what we might face in the in real life when we do a presentation in person or virtually. And what are some of the challenges. So thanks a lot for sharing your insights, Brent, and, yeah, hope to see you soon again.
Brent Dykes:
Thanks, Gilbert. Have a good one.
Gilbert Eijkelenboom:
Alright, thank you
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