Seven Principles for a Data-Driven Workplace

EPISODE 98 | Guest: Sean Matthews, president & CEO of Visix, Inc.

Many of our clients use their digital signage software to show data and data visualizations on their screens. It’s a great motivator for your workforce and helps create a data-driven workplace for better decision making and higher productivity.

But how do you know what data to show and how to display it? Are you sure your employees understand it? Is it relevant and actionable? If not, you might need to rethink your strategy. Sean Matthews walks us through seven steps and real world examples that can help.

  • Understand what we mean by data-driven workplace and why data matters
  • Hear some surprising (and disappointing) workplace data literacy stats
  • Discover how data affects everyday decisions and workloads
  • Walk through seven principles of data literacy
  • Find out about our digital signage data integration features

Subscribe to this podcast: Podbean | Spotify | Apple Podcasts | YouTube | RSS

Get content tips to motivate employees in our Masterclass Guide 3: Digital Signage Content Guide


Derek DeWitt: In a data-driven workplace, decisions are made based on, well, data, not your feelings or opinions or anecdotal evidence. But how well are we collecting and using data? Well, to talk about that, I’m joined today by Sean Matthews, CEO and president of Visix. Hi, Sean.

Sean Matthews: How are you, Derek?

Derek DeWitt: Good. I’m quite good.

Sean Matthews: Well, good. Glad you’re having me on today. And, this is actually a pretty interesting topic, so thanks for having me.

Derek DeWitt: Unlike all the other ones!

Sean Matthews: Yeah. Yeah. We’ll, I’ll make this one quote unquote “interesting”.

Derek DeWitt: All right. I’d like to thank Sean for joining me today and all of you for listening. Don’t forget you can subscribe to this podcast, and you can follow along with a full transcript on the Visix website. Just go to, Resources, Podcasts.

Okay, Sean, so what does it mean to have a data-driven workplace? What does that mean?

Sean Matthews: Well, Derek, I mean, it means people value facts and data, and then they act and react to shifts in that data. It also means that, you know, they’re checking the data before they make any significant decisions or changes to policies or activities, you name it. It means data is readily available. It means that it’s updated frequently, and it’s accessible to everyone in the organization, so that they can all react to that data. It means that everyone at every level is data literate, which most of us aren’t; not only using and understanding the data, but how to work with it. Those statements really encompass what a data-driven workplace is.

Derek DeWitt: Gotcha. I think that last point, especially about data literacy, a lot of people feel that they, oh, I understand the data, and yet, do you, you know? Data literacy’s kind of a big topic these days, not just in the business community, but, just as we live more and more of our social lives online, you know.

I know that there was a study by Qlik (Q-L-I-K, I guess that’s how you pronounce it) that said that only 10% of relevant data that businesses collect is used for analysis, which I thought was surprising. You would think it would be more. They further go on to say 24% of business decision makers feel data literate. Only 24% of them. So that means the rest of them aren’t, or don’t feel they are. And yet 32% of executives say, oh, yeah, I can create value from data.

So it, those are not great numbers. But is it, I mean, is it important? Clearly businesses are succeeding and even thriving, so why does data literacy and, and making decisions data-driven wise, why is this important?

Sean Matthews: Well, I mean, we’re all living in a digital economy. I mean, the faster we can respond to internal and external events, the better we can compete. And, you know, the more productive the company is, and the more satisfied our customers and employees are, that just makes for a better, more successful company.

And if you aren’t looking at data to see trends and compare this kind of performance over time, or survey the people that you deal with, then how are you going to adapt to those people and what they need and what the market wants? I mean, without seeing those trends, then it becomes difficult to be competitive.

You know, digital literacy means choosing the right data, you know, for whatever purpose and how to parse it, and knowing, you know, how that data should inform a decision, or even whether or not it should impact a decision.

Derek DeWitt: Ah, yeah, that’s an excellent point is, I think sometimes, you know, it’s like, go generate me a report. Okay. And then either it gets ignored or somebody looks at it and goes, I don’t really understand what to do with this, and they do nothing with it. Or the other, they see, they pull one thing out and then they create a whole action plan based on that one piece of isolated data, and they don’t look at it with context and all that kind of stuff. So, yeah, problematic.

Which is why I’m glad, there’s this association called the Data Literacy Project. And they have tons of great resources, and I want to kind of touch upon some of what they have. They say, and this is a quote, “only one-third of us can confidently understand, analyze, and argue with data.”

Sean Matthews: Yeah, I mean, I think, Derek, if you really dig into it, like most of us, whether we were self-taught, high school graduates, college graduates, you name it, spent very little time understanding statistics. And I don’t wanna completely weave statistics into the conversation, but you know, the better you understand how statistics are used, how they’re calculated and how they’re manipulated, it makes it difficult for you to really understand data and or data visualizations. Even though those data visualizations companies will tell you that seeing charts and graphs and whatever, you know, really help you understand what’s being delivered. And I’m somewhat against that philosophy.

Derek DeWitt: Yeah. I know a lot of people are sort of distrustful of statistics and data and all this stuff, because they have this idea of like, well, you know, you can make the numbers show you whatever you want them to. And there’s a little bit of truth to that, but not really, and especially not if you’re doing it in a sort of good faith way, You know what I mean?

Sean Matthews: Yeah. I agree a hundred percent.

Derek DeWitt: It’s a skill and it takes experience, and it takes education. And it’s improving all the time. They’re always finding new kinds of data sets, new ways to group the data. Oh, it turns out if we look at these things together, which are disparate… You know, I think about Freakonomics and things like that, which is taking things that you wouldn’t normally put together and seeing a trend suddenly, and you go, oh, that is interesting!

Sean Matthews: Yeah, and I do think, Derek, there’s a lot of just data overload. You know, we have products in our own business that we use to, you know, evaluate performance in sales or customer satisfaction or client churn. And, you know, we can look at all this data and compare previous years to one another, that sort of thing. But, you know, often I think that people are asking for data and data visualizations and dashboards and products that really, they show well, right, in a sales process or in a pilot process, but in the grand scheme of things, how often do these clients go back and actually look at some of the data that we provide them, even from our own product as it relates to utilization and the number of people that are contributing to the digital signage process,

Derek DeWitt: Right. And, you know, you go out there and grab all this data and then it’s like, well, which particular piece do I need? Do I have a business goal in mind when I’m gathering this and analyzing this information? Really, it’s what you should be measuring. I mean, we say this constantly on this podcast, and again it’s counterintuitive, but you care about what you measure, though we would think that it’s the other way around. But however, we define success, that’s what we’re gonna go for, you know? And I think that sometimes there’s just so much data now available, especially with AI and things like this that can really just pull in an astonishing amount of information.

Sean Matthews: I’ll say this in our own business, you know, between our sales and marketing teams, you know, a great example for us is just website analytics. And, you know, everyone looks at how many visitors their website gets, but if you’re not looking at what the bounce rate is, you know, that’s the number of people who land and then instantly leave again because they were looking for something else, then you don’t have the right info.

And, you know, I used to subscribe to this theory, quite frankly, where almost like a sales funnel, the more leads you put it, the more opportunities you put at the top of the funnel, you know, the more you’re gonna get out at the bottom of the funnel, right? And so, you know, my theory was that, hey, you know, if we had 10,000 visitors to our site a day, you know, that’s great. But you know, the more you understand the reality of 10,000 show up, but 9,000 leave instantly, then, you know, I’m not really addressing the problem with what’s wrong with our website, which is, you know, maybe it’s not capturing people, it’s not intriguing, it’s not sticky, right? And so, you know, as a result, I’m really measuring the wrong thing.

And, just to add sort of to that story, I had a sales guy one time, we were at a trade show, and, you know, I’m pushing to get card swipes. We paid, you know, $100,000 for this booth, and, you know, I want every card swipe I can get because, you know, I paid a lot of money to be there. And this guy looks at me and he says, you know, man, I get that you want a thousand card swipes, but quite frankly, I’d be happy with five quality card swipes, and every one of them closed, you know? And I was like, huh, pretty valid point there.

So, you know, I do think that, you know, another piece that’s affecting all this is just the sheer volume of data that’s out there. I mean, it’s hard to be digitally literate when, you know, there’s just such a volume of information out there, and the amount of types of data that are being created are just simply outpacing, you know, people’s ability to absorb what’s being delivered.

Derek DeWitt: Yeah, exactly. There’s a lot of the feeling of running to stand still, you know?

Sean Matthews: Yeah, for sure.

Derek DeWitt: To the point of how much information there is out there, I think the Digital literacy project website says something like, every second of the day, 24 hours a day, 1.7 megabytes of data is created for every single human on earth. Now, keep in mind, we’re not saying that it’s all worthwhile. When we talk about information, when we’re talking about information science and analytics, what we mean is ordered signal as opposed to disordered or chaotic signal. So, you know, a bunch of random words is not information, but a bunch of words that are put together that still is a lie, or that doesn’t make sense, is still technically information, you know? The statement “the moon is made out of Cool Whip” is information. It’s just not accurate information.

Sean Matthews: Right, right. Yeah. And I do believe that it’s really not a surprise that there’s a gap between C-suite guys that think they have, you know, a great understanding, between those guys and everybody else in terms of employees. But it’s disappointing.

And I think some other interesting stuff that you will find on the Digital Literacy Project site is, you know, things like, despite that over half of the C-suite executives, that’s 52% in this particular case, being fully confident in their own data literacy skills, 45% say they frequently make decisions just based on a gut feeling, you know, rather than data-led insights. So, it’s more along the lines of informed intuition than actually black and white data-driven decisions.

And, so meanwhile, on the same site, you know, it says 42% don’t always trust that the data available to inform their decisions is up to date and accurate. So, there’s like a lack of confidence in the data, so they default to their, you know, sort of gut decision.

Derek DeWitt: And that’s kind of their job. I mean, to be fair, C-suite people, especially CEOs, like that’s their job is they’re, you know, a force of nature and personalities. Thomas Wolfe called them, in his book Bonfire of the Vanities, you know, “masters of the universe”. I mean, that’s kind of what they’re supposed to do, to a certain extent. And yet, when we start bringing in something that is quote unquote “boring” like data, they’re still using the same methodology to achieve results that they would use to, say, bend someone to their will or implement a new product line or something like that.

Sean Matthews: Yeah. And, you know, without getting overly complex, you know, you could use data just even from simple surveys, you know, to sort of clear up maybe the decision making process. Because, you know, sometimes making those assumptions, particularly with educational gaps or separators or, you know, life experience separators, whether it be age or global experience or whatever, you know, you can make some decisions that really have no data-driven value, and they have no data-driven, you know, influence. In order to get the rest of your team to buy into some data-driven decisions, you gotta lead by example and reference that data when you’re making those types of decisions.

Derek DeWitt: You know, it’s interesting that you said that 42% of C-suite execs don’t even trust that the data that they have is up to date or accurate. It’s funny, ’cause there is this huge disconnect in expectations, right? So, the website says 89% of C-suite executives expect employees to be able to explain to them how data informed their decisions. And yet only 11% of employees, 11%, say that they are fully confident in their own ability to read, analyze, work with, and communicate with data. So right there, we’ve got, well, my boss wants me to, you know, lift up this building and I’m pretty sure I can’t do it.

Sean Matthews: Yeah. And I think, you know, I’ll give you an example of one of our clients because, you know, we haven’t really talked about this, but a lot of our clients want to put data-driven information on screens to affect employee performance, whether it’s information that’s generated from within our application, or they’re using some third-party thing, like a data analytics platform with dashboards, whatever, they’re wanting to put that on screen.

And so, you know, in their minds, you know, they’re thinking they’re creating this data-driven workplace, and that they’re, you know, educating everyone on what that means. But the reality is you’re putting the burden on a workforce who really can’t meet your expectations, right? They just can’t. They’re not trained to do that. They have a day job that they’re working on.

And I’ll just use one example of a client who, at first, I didn’t really think this was the appropriate thing to do. But it’s a manufacturing plant, and English is the second language of most of the employees that work at this particular manufacturing plant. So, they came up with an emoji scheme to basically represent the performance of a particular shift. And, you know, they were educating that team of people that, you know, if it was the frowning emoji, then we were way behind productivity objectives for that particular shift, you know? And so, the smiling emoji was everybody’s doing a great job, keep it up. But they, you know, made such a simplistic data visualization. And again, at first, I thought it was kind of like elementary and maybe even insulting.

Derek DeWitt: Right, exactly. What am I a child?

Sean Matthews: Yeah. And, but again, with the language barrier separation, that was how they were being able to address the fact that, you know, most employees want their employers to offer some sort of training to improve their data literacy. Which, you know, I think that if you looked at that particular environment, that would be almost an impossible task.

There’s other data out there that indicates 45% of employees that are anxious that their employer isn’t taking responsibility for, you know, nurturing the skills that they’re gonna need to be successful in the future workplace. And I think it’s, you know, it’s critical to business operations; it’s not really a buzzword. You know, if you don’t help your team become data literate, you’re gonna lose people, because they don’t even know how to use the data to improve their own performance, you know, just at the individual level.

And I will say this, we first started seeing, you know, data visualization way back in like 2005, right? And it was in call centers. Call centers were a big deal in that era, right? And, you know, hundreds of people in these call centers, and there were all kinds of reader boards mounted to the wall, suspended from the ceilings and everything. And the data that was on these screens, it was actually pretty straightforward. It’s a call center. It would indicate the number of calls that are inbound, you know, what the average call volume was per, whatever increment, 30 minutes, hour, whatever; number of calls on hold. You know, it was very specific information. And call center employees were taught that, hey, you know, you can take breaks if you need to go to the restroom, vending machine, you know, whatever it is that you need to do, but analyze what’s on screen before you make those decisions. Meaning if the call volume was very high, it’s probably not a good time to go to the restroom or go to lunch or….

Derek DeWitt: Just hold it in, pal! Hold it in for another 15 minutes.

Sean Matthews: Right. And you know, at some point, obviously, you know, employees have to, you know, feel free to sort of buck the trend in terms of what’s on screen, but I think, you get the point of how logical data can affect employee performance.

Derek DeWitt: So, let’s take a look…I think those examples are super interesting and illustrative.

There’s a thing called the Seven Principles of Data Literacy, which is from the Data Literacy Project website (and this is where a lot of these statistics are coming from). So, we’re gonna look at these seven different principles on what we can specifically do to help create a data-driven workplace. Because if it is a critical business operation, then if you ain’t doing it, you should probably do it. So here’s how.

Sean Matthews: Well, I mean, again, seven different principles. And number one is you have to foster a culture of humility and curiosity, right? This isn’t all some crazy technical skill (remember my emoji example). But you do have to grow a culture of curiosity, critical thinking, creativity and collaboration, so that people can talk about what they’re seeing on screen.

You need to start small. Choose one metric that people care about. But you have to understand and really know how that metric affects the entire performance. So that way you can start changing the minds and culture of those employees, because they see the value of understanding the metric, and when they respond to it, positive things happen, right?

Derek DeWitt: Right. That makes sense. And then you can scale it up after that. I mean, that actually makes a lot of sense. I think it’s maybe not (especially with a workforce that maybe isn’t especially data literate), you don’t want to say, okay, here are the 15 things that we’re gonna be doing, and we want you to monitor all of these things, and also here’s a series of processes and all these other ways for us to understand it, analyze it, write reports and so on.

I think it’s a smart idea to start with just one thing. Let them get used to the concept and the system, and then add a couple more and add a couple more, and before you know it, they’ve got 30 balls in the air, and they didn’t even know that they could do that.

Sean Matthews: Yeah. And, you know, to the point of, you know, limiting the data set, even over time, it can’t become so overwhelming. I mean, we all know that when NASA launches a rocket into space, you know, there’s, I mean, who knows, a million data points, right, so that they can ensure this thing you gets into space and gets to the moon, etc. But, you know, it’s not one person monitoring that data. You know, it’s an entire team monitoring various data points and, you know, being able to communicate with one another about what’s happening in their data set, right, certainly to avoid problems.

And, you know, in that model, number two on our list would be, you have to encourage employees to put training into practice, right? You have to train people to understand their analytics, how the data’s collected, how it’s collated, how it’s turned into usable information. Or otherwise, they can’t really choose the right things to measure or pivot when the data says they need to.

And you need to make sure they see to pay off and the benefits to this new skillset. You know, what does it mean for their roles in the organization? What does it mean for how much influence they have in the decision making process. What it does for their career progression, whether it’s with this company or, you know, some other company.

Here we are at a period of time, you and I are doing this podcast, and in the United States, the rail line workers were, you know, forcing a deadline to go on strike. You know, here we are coming out of the pandemic, these guys have worked like crazy, ensuring that goods were getting to every endpoint. It affects, the rail system affects like 30 some odd percent of our intermodal movement around the United States, right? So it’d be a crippling thing. But here’s what’s fascinating to me. So, the unions were gonna go on strike, and it wasn’t really just about money, right? Here these guys are in negotiations with a corporation or a series of corporations or these rail lines) and what the employees were really put out about was this points system for it measuring how many days you could have off, right? And so, if you ended up with negative points, the only way you could get the points back was to work more. Yet they’re already working like at their limits.

And so, I guess, where I go to this is, you know, number three on our list would be bring everyone on the journey. You know, when you’re starting talking about, you know, data, here the rail line owners maybe are thinking that this is just a money grab. Well, it’s not a money grab. In this case, everyone has goals, and every goal has a metric that’s meaningful to some people working towards that goal. And so, in this case, all these employees wanted to do was have some better way to work towards time off and, you know, recoup if for some reason they were penalized, right? It’s just an interesting misunderstanding, in my opinion, of probably what survey data was really indicating.

And, so again, you have to celebrate the successes in this process. Employees might, you know, just not trust the data or their decisions. And when a data-driven campaign or decision is successful, you make a big deal about it. Like, and it would’ve been interesting if they could have used this data to come to terms to an agreement much earlier without the threat of a strike or something like that, and really tout that as a success versus just some know last-minute hoorah type of thing.

Derek DeWitt: Right. And I would say the inverse is equally true from the organization’s perspective. Like, look, if you tried something and it didn’t work, just fess up. Don’t keep pushing it through. It didn’t work. It’s no big deal. If everybody has a sense of sort of ownership and buy-in to the whole process, then they’re aware that this is an experiment. And sometimes things don’t go well. That’s just how it goes.

Sean Matthews: Yeah. And I mean, you know, ties right into our fourth one here, you know, focus on the desired outcomes. So, the fourth principle is you have to be focused on the desired outcomes. You gotta clarify the problem you wanna solve or the insights you want to gain so that you have the context to explore, analyze, interrogate the right pools of information, right? And so, again, we care about what we measure, so measure the right things. And if you don’t do that, then you’re not gonna be focused on the desired outcomes.

Derek DeWitt: Yeah. You gotta really look at just how the system that you’re putting in place impacts people.

Sean Matthews: Yeah. I mean, and that, you know, really is the fifth of the seven principles is measuring the impact of your efforts to become a data-driven workplace. I mean, how many teams are using data for decision making? How much of the data we collect are we even analyzing? Are we underutilizing our data? Are employees more confident in their data literacy? And how satisfied are our employees with our efforts. And, you know, this should be an ongoing thing that includes lots of voices. We do it in our own business. You know, even looking at anything from website analytics to our client health, using the data tool sets that are available to measure what client health might look like.

And, you know, the sixth one in our list here, we should really be focused on adopting a systemic perspective. You know, we need to look at the organization as a whole, understand how the different parts work together and support each other in this process. We need to make sure that people creating data and those that are consuming data, that they’re on the same page, right? You know, we’re not measuring your individual performance on this thing; we’re measuring how our processes perform, so that we can improve those processes. And you don’t want people getting lost in that perspective and sort of diverging in terms of what they believe the data is being used for.

Speaking of a technology thing, our seventh principle here would be you need to decide what technology can meet your business need. But you know, you want to determine what it is that you’re trying to achieve, right? The data that you need to make decisions towards that goal, and then, you know, choose the technology that best drives that, right? You know, it also has to be scalable. It has to be user friendly or no one’s gonna adopt it. You know, a lot of technology is that way; if it’s just not friendly, then people just are gonna avoid it. And that’s just a fact with technology.

Derek DeWitt: Well, that’s for sure. And you know, also if it’s cumbersome and clunky, it’s like, man, this is taking up way too much of my time. You know, like I understand the perspective that sometimes people will get in their heads like, oh, so on top of everything else, for the same pay, I now have these additional tasks that are required of me, yet I also still have to get my other stuff done. So, you know, you’re not doing me any favors.

And I think one way that really we can – managers mid-level managers and C-suite – can kind of help alleviate that idea is if they lead by example. If people see them doing it and gaining benefit from it, then hey, maybe this is a good idea. You know, you share those results and all this. Hey, this is what we did in our efforts to become a data-driven workplace. We did this for us (so you know that no one’s being sped on), and we found these benefits from it. So, have at you employees.

Sean Matthews: Yeah, and I mean, of course you need to make sure it’s relevant to your audience. You know, I’m the CEO of this business, and I sometimes have to check myself to not overload people, for example, in town hall. You know, I believe that fiscal performance or financial performance is relevant to everyone. But I think if I were to ask specific employees, some would probably say, yeah, I kind of zone out when I’m looking at your charts and graphs related to financial success or, whatever, financial performance. So, you know, I think overloading people with that information is also problematic.

Derek DeWitt: Right. I mean, really, I think that means you gotta know your people. And if you, if the CEO is the one behind like, hey, let’s become data driven, it’s vital for our success in the next decades to come, if they don’t know the people, if it’s a big company, okay, no one expects you to know all 7,000 employees’ names and hobbies, but their immediate managers sure know them, you know?

Sean Matthews: Sure, sure. Yeah.

Derek DeWitt: And you know, I think too, you can, whenever possible, like it doesn’t have to be that big a headache. Like you said at NASA; I love this idea of the NASA control room, you know? I’ve been watching that alternate history show For All Mankind, and there’s a lot of activity in that NASA room. And I think that’s a very nice sort of metaphor.

Each desk, each department has their expertise. There’s a person in each one that has a team under them, but they’re the ones who are ultimately responsible. And the CEO is kind of like the person, the controller, who stands there and just makes sure that everything goes right. And then if something should, God forbid, go amiss, they suddenly have to make that decision.

And in many ways, you can sort of democratize this. I mean, I know it sounds like these are silos, but they’re only silos up to a point. It’s a way of spreading the work out, so that nobody gets hit too hard with it. And people know their departments. I mean, they know. People in sales know sales. They’re the ones who can actually help supply and understand that data.

Sean Matthews: Yeah. And I think that, you know, Derek, data sharing can inspire knowledge sharing and, you know, other cultural shifts. Because, you know, if in my skill set, in the NASA control room, I’m seeing a lot of, I don’t know, dissatisfaction with a new feature set that we released, then I can talk to other teams and really say, hey, it’s not that people don’t like this feature, they just don’t like the way the feature is either implemented or how it’s affecting other parts of our offering.

And so now, instead of just me saying, I don’t like it, or I have one client that doesn’t like it, I can share data that indicates that there appears to be some dissatisfaction with this new feature that we just released.

Derek DeWitt: So, okay. Those are the seven items. Do those things. To recap, try and create a culture of curiosity. Encourage people to not only get the training but put that training into practice, so they can see that it is practical. Include everyone, not just the managers; you know, don’t just shout instructions down to the lowly peons. Focus on the outcomes that you want, and you may find that you maybe need to adjust, or can adjust, how you achieve those outcomes. Measure the impacts of your efforts to become data driven. Be systematic. And decide which technologies are best, in fact, to achieve those goals and your business needs.

That all makes a lot of sense. Pretty generalized stuff. It can apply to all sorts of different kinds of organizations. But we also, because of what Visix does, focus on digital signage. So how does digital signage tie into a data-driven workplace? I know you mentioned data visualizations, and we talk a lot about KPIs and things like this. Are there other data integration features that are significant in this effort?

Sean Matthews: Yeah, I mean, there are, Derek. I mean, first I’ll say that the one advantage to having digital signs within a physical organization is that viewers, passersby, they don’t have to do anything to go get the data, right? They’re on their way to a meeting, they’re at a hallway intersection, and the data’s just presented in front of them as they pass by, right? And certainly, a well-designed, well-deployed, data-driven digital signage solution would do just that. It would inform people without them having to do anything. To best accomplish that, we have lots of data integration types, whether it be schedules and weather, you know, webpages, Excel, JSON data sources, XML, SharePoint; you know, there’s all kinds of things that you can connect to so that it ends up on screen.

And then of course we could add in, you know, data-mapped artwork with conditional logic; so, if this, then show this. So if positive, it’s green, if negative, it’s red. And then we can add interactivity to this as well, so that you can make data more engaging. So, I pass by this screen, and I see that, I don’t know, call volume’s up. I can actually touch that part of the screen and maybe get a better understanding of what might be contributing to that increase in call volume, right? And so, you know, by doing that, you’re just making the user feel more empowered to engage with this data, right?

And of course, one of the most important things is auto-updating data, you know, it refreshes every few minutes, and you just set it up once and let it run, so that you don’t have this person who has a day job also tagged with creating new data sets visualized on screen. So.

Derek DeWitt: Right. What do you do for the company? I’m the data wrangler.

Sean Matthews: Yes. I’m the person that makes sure when you’re on your way to the restroom, you know whether call volume is up or not.

Derek DeWitt: Right, yeah. I think that’s, I think that’s key. And, you know, as computers get faster and faster and are able to handle more simultaneous tasks, we’re gonna see this kind of automation really take off in the next five to six years. It’s gonna become so commonplace that anybody can enter the information and the system, the software, actually knows what to do. It goes, yeah, I know what to do – you told me months ago what to do with it; I’m gonna do that. And then boom, and it’s all up there and it’s accurate and it’s up to date and it’s not old information. So that if, you know, for some reason, we do need to react very quickly, we can.

Sean Matthews: Yep. I agree. So, you know the idea behind, behind having, you know, relevant, timely informational on screen has always been important in the digital signage environment, and certainly in an environment that is being driven by changes in data and the metrics that we’re measuring by. You know, I think it’s of utmost importance that that data be in real time so that it can have a realtime effect on the decisions people make.

Derek DeWitt: We have certainly come a long way from those old, you know, those old paper stock tickers you see in the old movies, like in the 1920s. You know, the kind of the glass dome with the coded punched paper coming out. We’ve come a long way from that.

Sean Matthews: Oh, for sure. Yeah. And I, you know, I like you see a future filled with auto-updating, data-driven information to positively affect the morale and performance of those people on your teams.

Derek DeWitt: Right, Exactly. And I’d also say, you know, back to the whole thing of gut feelings, and CEOs kind of operating by their feeling because they’re processing information in a certain way and a certain speed, and so even though it might in fact be an intellectual exercise, they’re so good at it, ’cause they’ve done it so many times, that it feels more like intuition. As these kinds of data-driven workplace systems get implemented, it kind of spreads that capability out to more people. So, more people kind of have that ability to, oh, I understand what’s going on, you know? The data literally becomes part of the environment, especially when you’re looking at something like digital signs, which are physically right there.

Sean Matthews: Yes. And you know, I’m hoping, Derek, that we made this entertaining subject for our listeners. Because it’s not something that, you know, everybody would just run out and want to go do, you know.

Derek DeWitt: Right. But you should. You should absolutely do it.

As I said, you can follow along – there’s a lot of information here –  you can follow along on the Visix website with a transcript of the conversation that Sean Matthews and I have just had. Sean Matthews, CEO and president of Visix. Thanks for talking to me today, Sean. You know, who knew data is such an interesting topic?

Sean Matthews: Yes, Derek, thank you for having me on again. I always enjoy it.

Derek DeWitt: All right, thank you everybody out there for listening to this episode of Digital Signage Done Right.