SDS 726: Seven Factors for Successful Data Leadership

This is episode number 726 with Ben Jones, co-founder and CEO at Data Literacy.

00:19

Welcome back to the Super Data Science Podcast. Today I’m joined on the show by the jovial Ben Jones. Ben is the CEO of Data Literacy, a firm that specializes in training and coaching professionals on data-related topics like visualization and statistics. He’s published eight books, including bestsellers like Communicating Data with Tableau, which was published by O’Reilly, and Avoiding Data Pitfalls, which was published by Wiley. He’s been teaching data visualization at the University of Washington for nine years, and previously worked for six years as a director at Tableau. 

00:52

Today’s episode should be broadly accessible. In it, Ben details the seven key factors of successful data-savvy leaders, which he details in his brand new book Leading in the Age of Data, in which he devised by administering his proprietary data literacy assessment to thousands of people. All right, nice. Let’s jump right into our conversation. 

01:12

Ben, welcome to the Super Data Science Podcast, where are you calling in from today? 

Ben Jones: 01:15

Hey, Jon. Thanks. I’m in the Seattle area, actually in Bellevue right now, Downtown Bellevue. 

Jon Krohn: 01:20

Nice. And so you are a listener to the show is my understanding, and maybe you can fill us in a bit on that. But as a listener, you found how to make a guest request, which I guess for anyone out there … I probably don’t talk about this on air very often. So, our podcast manager, her name is Ivana, and so you can email ivana@superdatascience.com and you can propose yourself or someone else as a guest, and we get dozens of them a month. So, from those dozens of suggested guests, recommended guests that came through, there were three in September that Ivana recommended to me to actually consider, and you were number one, and then I thought it was a no-brainer because you have this great new book out Leading in the Age of Data, which in the YouTube version you’ll be able to see me holding it up here. Ben also has it on the desk behind him.

Ben Jones: 02:20

There it is. 

Jon Krohn: 02:21

So, you get it in stereo, except in the audio version, video stereo. And yeah, you have this really clear idea for a podcast episode based on your book and the experience that you have. And so yeah, tell us, Ben, about the seven key factors of successful data savvy leaders. 

Ben Jones: 02:41

Yeah, you bet. We’ll get into that. Yeah, but as you said, longtime listener, first time caller. I have a trick though. So, a person on my team, Alli Torban, she’s also a podcaster, her podcast is Data Viz Today. So, she said to me, “Ben, if you really want to get on their radar, here’s what you have to do.” So, she gave me the pro tips and luckily, thankfully, we squeezed our way through. So, here we are. But yes, in Leading in the Age of Data, I lay out seven factors. So, trying to be an effective leader in today’s world, you really just can’t get around this question of data. How are you going to help set the stage for your team’s success? And I think a lot of people assume it’s just an IT problem, but really, we’re working with organizations that have leaders in every department you can imagine, marketing, HR, finance, and they’re all trying to become more effective leaders.

03:29

So, we do this little assessment with organizations. We have for about four years now. And in that assessment, it’s called the Data Literacy Score. We survey their team members, and it’s an interesting lens, it’s a subjective lens, but we break up 50 questions into these seven categories. So, what we’re trying to do is help leaders realize they need to think about some very specific things in each of those categories in order to avoid some of the problems and pitfalls that teams fall into. So, the first, we start with ethics, right? Because it’s got to start there. Is your team using data for helpful ends and means rather than harmful ones? So, ethics is such an important foundational piece for every leader. We go onto purpose. The purpose category, to me, I designed because I’d heard so many times data scientists saying, “Well, I got hired for this job, but I’m just working on little side projects that mean nothing.” So, I’m trying to help leaders assess are they using data to actually move the needle and achieve their goal and objective? So, that’s in the purpose category. 

04:36

There’s a section where we just talk about data. That’s the third category. So, its quality, its timeliness and freshness, whether it’s sufficient or not. So, the data piece is so important. That’s the third category. Category number four, we get into the technology. So, do the tools work together? Are they interoperable? Are you adopting new tools? And so, the technology category, actually that’s the lowest scoring one, which is understandable because there are a lot of pain points there, right? So, that’s the fourth. The fifth is people. So, what are the knowledge and skills that the people on your team have? And is it enough to actually make use of all of those tools you’ve paid a lot of money to implement? So, people.

05:18

I’m a big believer in the sixth category, which is process, because I spent a lot of time earlier in my career in Lean Sigma thinking about processes, mapping out processes and improving them. So, a lot of the times people don’t realize that you’ve got all these great tools, but your processes aren’t allowing you to use them. There’s actual decisions that are happening for your everyday, even weekly processes are not even taking data as an input. So, how can you find ways to change that as a leader? Thinking about things from the point of view of the steps in a process. And then we end with the one that I think my former employer, Tableau Software, really put at the top of the list for everyone, which is culture. So, do you have a team that is building in rewards and recognitions around data? Are you tapping into different kinds of data communities? And just is the overall culture in your organization one that’s reinforcing data as a key asset? So yeah, those are our seven categories. We go through those. Each has its own chapter in the book, but we’re trying to give practical advice on how to do well in each one of them. 

Jon Krohn: 06:24

Nice. Yeah, let’s make sure I’ve got those seven right. So, the seven factors that you have for successful data-savvy leaders are one, ethics, two, purpose, three, data, four, technology, five, people, six, process, and seven, culture. Yeah? 

Ben Jones: 06:42

Nailed it. You got it. 

Jon Krohn: 06:44

Nice. All right. Well, Ben, why don’t you talk us through an example of each one?

Ben Jones: 06:50

Yeah. Sure. Okay. So, let’s start off with the ethics category. So, within that one, actually it’s a lot of case studies in chapter one. In fact, chapter one is for free because we think the ethics piece is just important for everyone, so we just give it out. And actually, we’re making a little landing page for your listeners at dataliteracy.com/superdatascience. They can just go get that chapter in PDF form. So, basically with that one, lots of case studies. So, one example that’s a famous one is how Amazon had this machine learning algorithm back in, let’s say 2015, that timeframe, that would rank resumes. So, they wanted to have a five-star system for their hiring managers, just like you and I have to go onto amazon.com and look for books. Right? “Hey, here are the resumes that are five-star resumes for a particular job description.”

07:42

But of course, what they found is that when it came to programming, it was basically filtering out anything that was for a woman. So, if your resume said, “I was the woman chess club captain in my college.” It would drop it. It would push that resume to the bottom, because it would say, “Well, this doesn’t look like resumes of people that have been successful in that role here at Amazon in the past.” Which, of course, that’s because it was and still is to a large degree a male-dominated discipline. So, they realized that to their credit and they basically sunset the tool. In fact, they say they never even really used it, it was just for evaluation. But we’re showing lots of examples like that of how even in that case, an HR leader would need to be very savvy to principles of data ethics, how data can actually be used in biased and unfair ways. So, that’s a great example, one of many there within the ethics chapter, to get the ball rolling, just to help people realize, “Okay, I need to make sure I don’t do any harm here as I go about trying to leverage data.” 

Jon Krohn: 08:44

Nice. That’s like the … What is it the doctors have to say? The Hippocratic Oath.

Ben Jones: 08:49

Yeah, right. Do no harm. Yeah. 

Jon Krohn: 08:51

First, do no harm. So, chapter one-

Ben Jones: 08:52

First, do no harm. Or, as we’ve heard it say sometimes applied to data, first, do not hoard. So, that’s ethics. And of course, that also deals with those topics like data privacy and data security. Really important topics you just got to know before you hit the gas. I mean, I honestly wouldn’t want a leader to be very data savvy if they didn’t have those principles in place from the get go. So, that’s the ethics category. Yeah. In purpose … Okay, let me think of a good example that we cover in the purpose chapter. Well, again, with that one, I’m trying to make sure that they’re thinking big and also not getting stuck in these pet projects, these side projects. One thing we do is we try to teach them this topic or this technique called the goal tree, where you take this KPI, right? So, you’re thinking about that. You’re looking at that as a leader all day maybe, and really interested and concerned. But let’s say even if you have a medium-sized team, a lot of the people on the team may not have any way to relate to your KPI. It’s just so far away. They don’t feel like anything they’re doing today or this week has anything to do with it. So, it’s very demotivating a lot of times, and leaders don’t always understand that.

10:06

So, we teach them this technique to break it down and flow it down into more tangible critical success factors for each of their teams. And then also to show them the tree so they can see how different teams are contributing to the same overall goal just in different ways. And so, they can see the big picture, but they can also see where they’re having a lot of impact. And it’ll help them understand maybe some trade-offs that inevitably have to happen, let’s say, with budget or resources. So yeah, the goal tree can be a good way to really get your purpose and get data to touch different levels of the purpose as you go through your year and try to achieve your goals. So, there’s a good example there from the purpose chapter. 

Jon Krohn: 10:45

Nice. The goal tree. 

Ben Jones: 10:47

Goal tree, yeah. I’ve used that technique a lot. It’s just a simple little flow down, right? And they can be very mathematically precise where, let’s say you have, I don’t know, a sales number. Then below that you’ve got a number of visitors to a website, then a click-through rate or something. So, it can be really mathematically precise, but doesn’t have to be. Let’s say you have a weight loss goal, you may not be able to literally do the math on weight loss, but you can break it up into sub-goals like diet versus exercise. 

Jon Krohn: 11:15

I see. Yeah, it makes perfect sense. Yeah. So, your ultimate goal is profit, and so you’re like, “How do we do that?” Well, we increase revenue, but then another leg of the tree is reducing costs. And then-

Ben Jones: 11:27

Exactly. Right. 

Jon Krohn: 11:28

Okay. Yeah. 

Ben Jones: 11:28

Yep. So, you have cost-cutting measures and you also have promotional measures. And in an organization, that’s totally different teams and they have nothing to do with each other, but what they don’t know all the time is, okay, if I achieve my part of it and that organization or team over there achieves their goals, then this is why those two things go so well together. So yeah, as a leader, you need to see that entire picture, but then also I think it’s good for your team to be able to see it too. 

Jon Krohn: 11:57

Nice. Cool. I like that. All right, and then category three is data. Now, that’s a funny one to me. 

Ben Jones: 12:04

Right. 

Jon Krohn: 12:04

But I think I know what you’re getting at here. In our seven factors for successful data savvy leaders, data is one of them. 

Ben Jones: 12:12

Yeah. I know what you mean. It’s like, okay, well, yeah, isn’t that the whole thing? So, it’s interesting, right? Because let’s say you’re not in IT, so you may not really feel like data is your domain and I think that there are a lot of people who have worked their way up within organizations over the last decade or two or even three. And again, the fact that data is so relevant and so important is a little new really, I mean, at least to the degree that it is now. Right? So, within that category, we’re trying to help people understand some different aspects that really hit home. So, thinking about data from the point of view, for example, assessing of all the data in the world, like a Venn diagram, so there’s the data you maybe have but you don’t need. Okay, so then we want to ignore that. There’s also maybe data we both need and have, and we want to make sure we’ve got people working on that.

13:11

But then as a leader, you really have to spend time thinking about the data you need but don’t have. So, what are you doing to make sure your team gets that data? It’s your job to advocate for your team, find ways to have the data becoming more and more relevant and sufficient. So, that’s the first little segment there. Each one has seven guiding principles. So, that’s the first one. 

Jon Krohn: 13:35

Each one of the seven has seven? 

Ben Jones: 13:36

Seven, yup. So, 49. It’s a weird number for a guy from Seattle to be all about 49-ers. But yeah, we got seven times seven, so there are 49 little … I actually made it really easy to write the book. This is my eighth book and I just had to every day, think about one of those 49 [inaudible 00:13:54]. 

Jon Krohn: 13:54

Oh yeah, that is nice. 

Ben Jones: 13:55

Yeah. We went through it- 

Jon Krohn: 13:56

I think it’s having the experience that allows you to distill it into that seven by seven that would’ve taken [inaudible 00:14:02]. 

Ben Jones: 14:02

Yeah. So, that’s the data category. Yeah. With that one too, we’re teaching the concepts of metadata and documentation. I think a lot of people aren’t sure what’s in the data, they don’t know what the acronyms are, but they’re afraid to say anything. So, as a leader, can you make sure they have something at their fingertips to learn what the data means? If you have a data catalog, great, learn about it, learn how to use it. Maybe you’re in an organization that hasn’t rolled out that technology yet, so how do you make little simple explainers or lists? How do you have people on your team available if someone has a question, so that no one has to feel stupid? So, as a leader, I think that that’s an important thing. And that’s as applicable if you’re in marketing as it is if you’re in IT. You need to make sure your team members are familiar with the most important attributes and variables that they need to do their job. And you have to understand that right now they may not be, especially for someone who’s new to the team, this is all brand new to them, right?

Jon Krohn: 15:01 Yeah. 

Ben Jones: 15:01

So, [inaudible 00:15:02] the way you create that-

Jon Krohn: 15:03

As a quick hack for people who maybe you’re worried about wasting someone’s time, you have some variable names you don’t understand. You and I were talking about, just before we started recording, about the ChatGPT Code Interpreter, now called Advanced Data Analysis. 

Ben Jones: 15:19

Yeah. 

Jon Krohn: 15:19

Which is annoying because they made that name change right after I released the Code Interpreter episode of this. So, I had episode number 708, I did an episode on what was at that time called the ChatGPT Code Interpreter. I had five hacks for data scientists. And one of those hacks was just understanding what your variables are, and it’ll even take a crack at guessing based on the name and the structure, obviously it has the power of GPT-4 in behind it. So, it can make a lot of educated guesses really quickly and give you a good start. 

Ben Jones: 15:53

I think AI chatbots in the near future here are going to really help a lot with this question of understanding the data and what it is and what it means. Of course, they’re not perfect in that regard and probably never will be, but it can be a good starting point to ask a chatbot to explain a specific concept or metric. 

Jon Krohn: 16:12

Yeah. I obviously won’t … If you pulled the data from a SQL database or something where all of the codes are specific to the organization … But anybody who’s designing that kind of database in the first place should be trying to put in column headings that make some sense. So, it should be possible for a machine that even has no awareness of your particular business, your particular SQL database structure, to be able to take some guesses. So anyway, so we probably belabored data enough here. Let’s move on to tech. Number- 

Ben Jones: 16:45

Yeah. So technology. Again, in our Data Literacy Score, this is the lowest scoring category. By that, I mean the average score people assign to the questions in this category comes in at the very bottom end. And I think that’s because even in small organizations, technology can be where you feel the friction and the pain points. So, we’re asking leaders to think about those points of friction and can they identify them, can they be in tune so when those technologies might not be as interoperable or performant as their team would like them to be? And then how can they be judicious but also proactive about reaching out to their peers in IT to try to find ways to resolve that? So, there’s lots of ways in which these systems we’re implementing can cause a lot of headache for people. 

17:40

We try to create systems that are empowering, but a lot of times they’re imprisoning. So, if their team members are in prison because of tools, they need to know that and they need to know that there’s something they can do about that. They may not be able to solve it themselves, but they need to play a role in raising the issue and trying to find a path forward, finding workarounds, putting together a roadmap, communicating that to their team members so they feel like there’s a way that things are going to get better. And I think it’s easy to just, if you’re a non-IT leader, let’s say whatever in, I don’t know, operations, to just throw up your hands and say, “Oh, well.” And then even to complain with your team members about some of the problems that are surfacing with legacy systems or vendor lock-in, all these different problems. 

18:27

And so, that can be therapeutic to some degree, but I think the team members also get the idea, “Well, this leader isn’t trying to solve my problem, they’re just griping about it.” And eventually I think that leads to even more frustration. So yeah, I mean, how can you be part of the solution? How can you help IT know about some of the problems that are surfacing? And then, if it’s going to prevent your team from achieving their goals, can you get it fixed? How can you find a way to get that problem resolved? And I think that’s the role of a great leader in the age of data, is to find that path forward. So, that’s one of the seven guiding principles in the technology category would be about alleviating points of friction. 

Jon Krohn: 19:08

That’s a great one. Makes a huge amount of sense to me. And then, number five is people. 

Ben Jones: 19:15

Yeah. So, actually, the lowest scoring statement of all is about having great data training available. So, out of 50 questions, there’s 49, seven times seven. 

Jon Krohn: 19:25

Yeah. Yeah. 

Ben Jones: 19:25

Those are what we call overarching question that essentially tries to gauge someone’s opinion overall about data and its effectiveness within the team. But number 50 out of 50, the lowest scoring overall is about people having access to great data training. 

Jon Krohn: 19:38

Gotcha. So, the lowest scoring of the granular 50 questions is in this people category, the one that you’re about to say, but it was the previous category, tech, where on average, across- 

Ben Jones: 19:51

The category overall. Yep, exactly. 

Jon Krohn: 19:52

Yeah. Okay. I gotcha.

Ben Jones: 19:53

Yeah. Thanks for clarifying. So, each one has seven. So, the technology category itself is the lowest, but of all the 50 individually, the lowest scoring one is this. Actually, this is second to last. “My organization provides valuable training opportunities to help me and my teammates develop the knowledge and skills necessary to effectively work with data in our roles.” So, it’s 49 out of 50, the 50th is coming in the next section, but that’s 49 out of 50. 

Jon Krohn: 20:21

I’m on the edge of my seat. Come on, let’s hurry up and get through this people crap. What do people need? Come on, get past the people.

Ben Jones: 20:27

They need training, they need good training, but they need good training. So, what we try to teach them in the book is, okay, what does a good training look like? Right? So, I think, first of all, they need probably training that’s just on their own systems and it’s hard to get a vendor to do that. So, this statement, the fact that it’s so low scoring is self-serving for us in a way, right? Because that’s primarily what my company does is offer data training. So, we’re always available to help, but a lot of times we have an open response question at the end, and what we find is many times they want training on their own systems, and that’s a little difficult for us as a vendor to provide. We have great training programs, but the leader is probably going to have to put together something around training their own tools and systems to make sure someone is familiar with this labyrinth that they’re in and how to make their way through it. 

21:15

So, we teach them what we think are principles that differentiate or separate between poor data training and good data training. And this can probably apply to training overall, not just data, but there’s eight factors for great training. So, clear, relevant learning objectives. It has to be customizable to your specific situation. That’s number two. Three is soliciting active participation from the trainees. Four is, it needs to be delivered in an engaging way because as we all know, it’s really easy to make data boring. Five is, it needs to be taught by deeply qualified trainers. Six, including practical examples and real world case studies. Seven, giving trainees a chance to be assessed and receive feedback. And then eight, featuring follow-up touch points where learnings are reinforced. So, in other words, we’re not just telling- 

Jon Krohn: 22:04

Ben, I’m deeply disappointed that you can’t distill that down to seven.

Ben Jones: 22:09

I had a ninth, but it got blended with another one. Yeah, eight’s a round number. That was my first soccer jersey number, so I like the number eight. 

Jon Krohn: 22:17

Okay. I’ll stop interrupting you. Okay. 

Ben Jones: 22:19

And then last but not least, so in the final category, right? Which is the culture category. Again, this is the lowest scoring question of all 50, which is this, “My team has access to one or more thriving data communities with members that connect around data and its value as a resource.” 

Jon Krohn: 22:38

Just read it through one more time. 

Ben Jones: 22:39

Yeah, sure. So, culture question, “My team has access to one or more thriving data communities with members that connect around data and its value as a resource.”

Jon Krohn: 22:50

Right. Right. Right. Yeah, I mean, it does sound like a rare thing. It doesn’t surprise me that that’s- 

Ben Jones: 22:53

Yeah. People are like, “Nope, I don’t have a data community. Nope.” And maybe that’s simply a reflection of them saying, “No, we don’t have that at all.” And it isn’t always saying that they need, that isn’t necessarily the first problem for a leader to solve, but it is probably good to know that people often don’t feel that that’s something that they have at their disposal. And now the leader may say, “Well, I can’t go create a community. I mean, I have enough on my plate.” 

23:18

But this is where there are really great resources and communities out there that you can, if you have some awareness of them, user groups, book clubs, things like that. You can get them tapped into different communities online that help them, but also then building those potentially within your own organization as well. So, can I find ways to connect with someone else who’s also using the same tool here at the same company? People love that because now they feel like they can share their experiences, they can learn from other people. They’re not stuck in their own little box. They get to branch out and they get to talk and connect. And I think like we talked about before, Jon, right? Prior to recording the show, that whole connection piece is really important in our culture. 

Jon Krohn: 24:01

Yeah. And pre-pandemic in New York, and I’m sure in Seattle as well, out by you, Meetups with a capital M, were great for this kind of thing. And in New York it’s never really taken off again post-pandemic and a lot of Discord channels out there. But yeah, as tying into a conversation that you and I were having before we started recording, yeah, I don’t think the digital experience provides anywhere near the same kind of community. 

Ben Jones: 24:29

Yep. 

Jon Krohn: 24:30

Yes, you get the advantage of people from all over the world, but you don’t build that same kind of … When you go to a meetup once a month or whatever, you see the same people. You very quickly start to figure out, “Okay, this is somebody that’s trustworthy. This is somebody who’s honest. This is somebody who has great ideas. Maybe I’d like to work with them, or maybe I’d like to collaborate on a project with them or tackle some Stanford lectures together.” Or whatever, in a way that I’m sure absolutely happens in a Discord channel, but I can’t imagine it happens as often per unit of time spent. 

Ben Jones: 25:06

Yeah. It’s just a richer interaction face-to-face. I think we need to get back to that. People are getting back into the office now, of course. And so, maybe there are opportunities as a leader that you have, to tap into some different kinds of groups within the company. A lot of it maybe you have to be careful with data communities that you’re not placing more burden on people and filling up their already full calendars. So, much of it can be optional, but high value too. Like, “Hey, I can join this hackathon or I can listen into this guest speaker, or someone’s got a skill pill that maybe I can take my lunch and go through.” So, those sorts of opportunities and providing those, I think those can be very helpful. And like I said, that’s definitely the one out of all 50 right now that ranks all the way at the bottom. So, that’s an interesting data point already. Whether you’re going to do it or not and put a data community in place, it’s good to know that that’s a potential avenue that is many times not taken advantage of. 

Jon Krohn: 26:05

Totally. Yeah. That’s a really great takeaway from this whole message that, across these seven factors for successful data savvy leaders, if there’s one thing you take away from this whole conversation, it sounds like this opportunity to create a data culture is the most likely one that you need to work on in your business. Awesome. Thank you so much, Ben. Before I let you go, do you have a book recommendation other than your own book? 

Ben Jones: 26:30

Yeah. So, I’m reading a lot of books. Okay. I had one I really loved. It’s by Melanie Mitchell, so she’s at the Santa Fe Institute, so it’s called Artificial Intelligence: A Guide for Thinking Humans. So, it’s a really great book. I just finished reading it. And the reason why I like it is because it’s helpful for people that aren’t already super AI savvy. And so, I really admire what she did there. She has deep experience in the field going back decades, so I really love that book. I would recommend that to your readers, especially for those who are in a role where they have to explain AI to people that don’t get it and maybe are afraid of it.

Jon Krohn: 27:05

Awesome. 

Ben Jones: 27:06

So, that would definitely be a recommendation I would put out there. 

Jon Krohn: 27:08

Great recommendation. 

Ben Jones: 27:09

Oh, one other one too. 

Jon Krohn: 27:10

Yeah. Oh, yeah. 

Ben Jones: 27:11

Chart Spark is coming soon. It’s a book, it’s available today for pre-order, but this is by Alli Torban. She’s the host of the podcast Data Viz Today. I mentioned her earlier, she’s the one who gave me the pro tip on how to reach out. 

Jon Krohn: 27:24

Yeah. 

Ben Jones: 27:25

But yeah, I’m just reading through it right now as a beta reader and it’ll probably hit the shelves in early December and it’s amazing. It’s all about creativity and data. Two things people don’t think should go together or certainly they haven’t often been put together, but she did a great job of doing just that. So, how do you really tap into your own creative energy when you are working with data? 

Jon Krohn: 27:45

Yeah. We expect Ivana will be getting a podcast episode proposal from Alli soon. 

Ben Jones: 27:51

And Alli knows how to do it. 

Jon Krohn: 27:52

And so, maybe we’ll have Alli on the show soon. And I also thought to give, I mean, everyone their best shot here, for people who are listening and you want to make that great … I mean, so Alli, who submitted Ben’s suggestion here, I guess, very clear. So, obviously your name and then typically a link to your LinkedIn profile or some other social media platform or something online that clearly gives me a sense of your profile and maybe the scale of your following or something like that. It definitely doesn’t hurt to already have an existing following, though that isn’t the only criterion for being on the show. If you have a huge following, it’s going to help. 

28:28

Yeah. And then, it very quickly provides underneath, co-founder and CEO of Data Literacy, link to Data Literacy, author of Avoiding Data Pitfalls, and Data Literacy Fundamentals. Links to the Amazon pages for those. So, you can see book reviews and that kind of thing right away. A nice short bio, just a couple sentences that’s directly relevant to what would be covered on the show. And then, actually provides a link to the PDF of the new book, so Leading in the Age of Data. A digital version, a PDF, so that we can peruse that. And then, critically, some proposed topics. So, in this case, it was exactly what we did, the seven factors of successful data savvy leaders and a story that illustrates why it’s important and an actionable takeaway from each of those. 

29:21

So, that just made it so easy for me to see all this and be like, “Okay, this is an episode already packaged up with somebody who knows what they’re doing. Let’s go.” And to be absolutely sure, some previous podcast appearances, there were three provided to me. And that’s a good number because obviously that’s something critical, we need our guests to be excellent oral communicators. It’s a podcast. We don’t have a choice, unfortunately. That’s got to be something that our guests have. And so yeah, thanks so much to Alli for making that so easy. And I haven’t been letting you speak enough, Ben, but I don’t know if I’m holding you back from saying something- 

Ben Jones: 30:06

No, not at all. Not at all. No, I think I spoke a lot. I feel like I had a lot to say. So, thanks for giving me the chance. 

Jon Krohn: 30:10

No, for sure.

Ben Jones: 30:11

And yeah, with regard to Alli, to me it’s all about being a shortcut. Can you be a shortcut for someone? Can you make their job easier? Can you put yourself in their shoes and think about what they need to get done and then make that as easy as possible, like Alli did with that description that I filled out and sent over to Ivana. And that’s the same thing with data. Can you find a way for this resource to serve as a shortcut to make better, faster decisions? In general, I think as you grow in your career into a leadership role, be a shortcut for people. That’s an important lesson. Also, yeah, I wanted to make sure your listeners know dataliteracy.com/superdatascience, they can get the first chapter. We got a discount code for them there, as well as just a place for them to find out more courses we’ve got. 

Jon Krohn: 30:58

Perfect. 

Ben Jones: 30:58

Yeah. Yeah, thanks. [inaudible 00:31:01]. 

Jon Krohn: 31:00

Yeah. And other than that, how else should people follow you? 

Ben Jones: 31:02

So, you can find me on LinkedIn, so it’s just /BenRJones. I used to be on Twitter a lot more than I am today, or I guess X, and that’s @DataRemixed. But last but not least, so we are starting a new solutions page, so they can just email me directly. And so, if they reach out to bjones@dataliteracy.com, we’re actually going to be giving away to five listeners that can reach out to me with a data leadership challenge they have, a free hour of time just connecting and chatting about it. So, feel free to reach out at anytime. We’ll put a little form in the landing page too- 

Jon Krohn: 31:38

Awesome. 

Ben Jones: 31:38

… that helps them figure that out and get their little challenge into my inbox. And like I said, I’ll reach out and we’ll set something up. 

Jon Krohn: 31:46

Perfect. Yeah. So, I’ll make a note of that as well in my LinkedIn post that comes out when this episode is published, so that people can do that, for sure. All right, Ben, thanks so much for taking the time today. It’s been awesome having you on the show with this nice, clear episode, very actionable. And we’ll catch you again.

Ben Jones: 32:04

All right. Thanks, Jon. Appreciate it. Take care. 

Jon Krohn: 32:07

Sweet and easy, that interview was. In today’s episode, Ben covered his seven factors for successful data savvy leaders, namely ethics, purpose, data, technology, people, process, and culture. With that final and seventh category containing the single biggest opportunity for data savviness improvement, in many organizations, that is providing access to internal or external data communities. 

32:32

All right. That’s it for today’s episode. I hope you found it insightful and helpful. If you enjoyed it, consider supporting the show by sharing, reviewing, or subscribing, but most importantly, just keep listening. And until next time, keep on rocking it out there. I’m looking forward to enjoying another round of the Super Data Science Podcast with you very soon.