Breaking the Language Barrier Between Data and Business with Joyce Myers of Modern Technology Solutions

Eric Dodds 00:06
Welcome to the Data Stack Show.

John Wessel 00:07
The Data Stack Show is a podcast where we talk about the technical, business and human challenges involved in data

Eric Dodds 00:13
work. Join our casual conversations with innovators and data professionals to learn about new data technologies and how data teams are run at top companies. Welcome back to the show. We are here with a very special guest, Joyce. Myers. Joyce, welcome to the show.

Joyce Myers 00:35
Thank you. It’s nice to be here. I appreciate it.

Eric Dodds 00:38
All right. Well, you are with MTSI, who is a defense contractor, but you have had a long history of working for being inside and interacting with the military. So give us just a brief overview of your background.

Joyce Myers 00:54
Yeah, absolutely. So I am an Air Force grad. I am retired from the United States Army as a soldier and also retired as an army with several little clips of being an army contractor, and now I’m fortunate to be the chief data officer on the corporate side for a defense country. So the whole

John Wessel 01:15
Yeah, that’s great. So Joyce, one of the topics I’m excited about digging into is logistics and data and how closely those things truly are tied together. Give us something that you’re excited to talk about. Well,

Joyce Myers 01:29
I’m actually excited about that. I spent my time in the army and as an Army civilian, as a logistician, so I’m an accidental data person, just coming to it from the business, so I’m excited about that great, awesome.

Eric Dodds 01:43
Well, let’s dig in. We have plenty to talk about. Let’s do it. Joyce, I am really excited because I don’t think we’ve had anyone on the show who has done the type of work that you’ve done within and for the military, and that is going to be, I’m just, I’m so excited, and I have so many questions, but would love for you to just give the listeners an overview of your just an overview of your career. You gave us a little, you know, a short one in the intro. But what types of work did you do? You were a soldier, but then you did different types of work in the army and for the army. So I would just love to hear more of those details.

Joyce Myers 02:27
So I’ve actually been very blessed to experience technological growth and process growth through my career, right? So I joined the army in the early 80s. 2% of the soldiers were female. So it was a unique experience. Wow. It was a logistician. I was a unit supply clerk. So the person that you went and got your paper and your pens and your toilet paper from the person who made sure that all of the equipment was on hand or on order. So from the very beginning, we’re doing logistics. We call it supply, and nobody really thinks of it as logistics. But you know, how many boxes of toilet paper do I need to order for this many people? And do I have the right budget to do that and how do I order it? We have our expendable supplies like that, but the army says we’re authorized for 10 trucks. Do we have all 10 trucks? So from the very beginning, right into that manually, all of that was tracked on paper, wow, in the early 80s. So fast forward a couple of years, we get our first computers. We’ve got word processing. I personally used my first spreadsheet. My first Excel was a graph template that I would draw the lines on. Wow. And, you know, we didn’t have the internet. I know I’m very much aging myself, but we bought a copy of D base three and created our first database to track and move equipment and manage that property. And so I really started getting into the data, and our business process is right? What do we require? What do we authorize? What are we? Where is it? What is the status? All of that’s data, but nobody really called it data, right? I mean, it’s just fields that they were tracking. And then we had army computers, right? Again, not connected. Each organization had its own computer. Our first one was, you know, sitting on a picnic table, sized table, Mm, hmm. And we continued to grow and learn. And then we started connecting those computers, and now this unit could talk to that unit, and this commander could see the organizations below, and we could start to put that information together. And then those became connected at an army level. And as time progressed and technology progressed. Just so we could see ourselves better, right? We could see the logistics, not just from a small sliver, but from our left and our right and our up and down. And it was, it’s just really exciting to do one. I’ve been able to watch that journey and still participate in, yeah, and be on the forefront of some of those systems being developed and providing some of those business requirements as a soldier, as a consumer, of the end result, it was kind of nice to be on the back end. And sure

Eric Dodds 05:36
one thing, we were chatting before the show a little bit, but I told you that I went to the 80th D day anniversary, anniversary with my father in law. Both of us have, you know, families who participated in the D Day operation. So, you know, very meaningful. I did a ton of reading about that entire operation. And one of the most fascinating, I mean, you know, sort of inexhaustible, you know, topics that are, you know, going to be studied, you know, for decades and decades and but one of the big ones is logistics, actually. And it is mind boggling. How many people, pieces of equipment, everything they moved. I mean, there were, you know, considerations around secrecy. There were fake military operations being executed, you know, ultimately, to sort of achieve one of the largest, you know, in, you know, in some respects, the largest invasion, you know, sea invasion in the history of the world. So can you just reflect on that a little bit, having managed logistics in the army and seen that from paper all the way through to developing the systems and having been a soldier, can you just reflect on that a little

Joyce Myers 06:54
A bit? It’s boggling, mind boggling, exactly, but I think it boils down to a couple of things, right? Leadership, communication, trust. So whether you’re using computer systems or whether you’re in a room with darkened windows, planning the largest innovation in the world, the leadership has to have the vision. They have to understand what is the final outcome we’re trying to achieve. Who are the stakeholders? Who needs to know? Whose feedback do I need? You have to trust that the information you have is the right information. And so we know there have been, I can’t even begin to imagine, the number of leadership lessons that have come out of right, mm hmm, communication styles from the leaders, from our general officers that were there, the communication coordination between the different forces, between the Air Force and then the army, right, all of the forces. Those key concepts that made that so successful are still the foundations of the key concepts. Today, we’re using computers, but the leadership still has to have a vision. We still have to have that goal, that understanding what that end goal is, because if you just throw technology at it, you don’t know what problem you’re trying to solve. You just have the expensive technology of communication. Our leaders have to share what they want, but we have an inherent responsibility as data, as employees, as whether an engineer or an analyst or a chief data officer or a logistician, we have a responsibility to say this information doesn’t make sense. Or did you consider that? My purview, my viewpoint, shows that. So I think I’ve thought of this a lot too. D Day is one of my favorite topics. And so regardless of what the data was, and think about it was all on paper. Yeah, it was all on paper and handwritten notes. And the fact that you could bring that all together comes down to the human element, right? Yeah, the communication, the connection, yep.

John Wessel 09:13
So I’m curious, do you think we’re talking all on paper? Do you think because you shared a story before the show about kind of the almost the opposite of this, of where the data quality was low fidelity. Do you think that there’s maybe some aspect of I don’t know discipline, or I don’t know what the right word is. When it was on paper, it had to be so regimented and structured and like, the hand offs had to be really clean, whereas, like, when it becomes electronic, some of that’s automated for you, but there’s still components that are human components that are really important. Do you think that transition has potentially resulted in less high quality data?

Joyce Myers 09:54
I think in some instances, that is absolutely the case. Right when it was. Is paper oriented, and I am not in no way advocating for going back to Mary. Yeah, of course,

Eric Dodds 10:07
you say that with personal connection.

Joyce Myers 10:10
Yes, as the person who had multiple vanilla folders on my desk having to manually review them, I will say that, no, we don’t want to go there. But I think there’s always been people who are going to slack off. There’s always been people who don’t pay attention to detail. I think we magnify it some in our systems, because now so many more connections are out there right before I would fill out my paper, and I hand it to a person, and it had multiple checkpoints where it had things. A lot of people call it human in the loop, I like to call it a reliable human review, right? So the understanding, the context, the experience or wisdom, theoretically, yeah, yeah. So I think maybe you know, then it was a very controlled environment, very trusted leaders, very just you will do this right again, the military has a hierarchy and a leadership team, sure, important, and men’s lives are on the line, right? Soldiers’ lives are on the line. So when we as people know what’s on the line, based on our results, I think the quality will be there. But if we don’t understand what we’re putting in the computer and how it impacts the end goal, if we don’t understand in marketing that our marketing command campaign grows our business, if we in HR, don’t understand that hiring and tracking the right data gets the best people on right? It impacts our revenue. I mean, there’s just so many. And then, of course, in logistics, you don’t put the right stuff in,

John Wessel 11:49
doesn’t get to the right place.

Eric Dodds 11:50
Can you share that story that you shared with us when we were chatting before the show about that, you know, sort of seemingly insignificant data entry issue. And then the impact. I think that’s just such a good example of what you’re talking about. Yeah,

Joyce Myers 12:05
of course. So in a previous life, when I was working as an Army civilian, we gathered all of the army maintenance data, and that maintenance data came in from computers all over the army, every location, right? So this data starts at the lowest level. It starts from the mechanic who is working on an individual truck, or trucks, and so at that point in my career, in the life of technology, right, we were still tracking some of that information on paper. So we’d walk out there, someone would walk around the truck and they’d say, this tire needs replaced, or this mirror needs replaced, or whatever the issue was, right? And then the mechanic would do the work, and they would fill out, and it took me however many minutes to fix it. So that amount of time is that allocation, right, that time allocation that knows that this particular maintenance activity takes an average of 50 minutes, sure. So that’s a standard, and they can justify it right? And we know that when we go and take our car into the dealership, they know that it takes about this much labor to do something right arm is no different. But if you don’t feel that form in, then the guy putting that information in the computer, they don’t necessarily know that, but it’s a mandatory field. And then they’re not the one who did the inspection, they’re not the one who replaced the tire. They’re not necessarily the mechanic. They’re the clerk putting the information so easy for them, they just go, point one point 1.1 or whatever the case is, whatever they put in there zero, because the field just has to be filled in, right, right, so that data gets aggregated from that organization, up from that organization, up from that organization, up, ultimately feeding into the folks at the top are doing statistical analysis and looking at information. They’re not replacing a tire. They don’t know it takes 50 minutes, or whatever the amount is, but they’re seeing a trend across the army that when they put all the data together and they analyze it, it really only takes, like, six minutes to do a tire. We know that’s not true, but the data is the same. And so at the top level, they look and they say, whoa. Well, I guess the organizations don’t really need three mechanics, because this stuff doesn’t take as long as we thought it did, yeah. And so ultimately, as they do those new documents to say you’re authorized three mechanics, maybe next year you’re only authorized two. There’s a cost savings. And while the military is not revenue generating they still have to be stewards of the resources. So if we can save money by reducing. In positions that’s a good thing. Make you move that money somewhere else. Yup. So now this person putting the data in has no clue that their simple point 1.1, whatever, feeds all the way to the army. They may never even see that decision. They may be moved on to a new unit, yeah, because sometimes it takes a while for that type of analysis and follow on. So we, I used to brief that to groups of people, and they would go, wow, I never knew that what I’m putting in the computer goes all the way up to the arm. So I think that’s the same in our organizations, right? And in every role that we’re in, what is the information that we’re providing being used for, what business decision is being made? And then we have a little bit more of an understanding and responsibility of, let’s make it good quality, yeah, or at least the best that we can Yeah,

John Wessel 16:00
and I think there’s another avenue here, because it’s what is it being used for, and then there’s what could it be used for? Because it very well could be. They had been collecting that data for years, and nobody looked at it. And somebody even told the individual that was inputting, like, Ah, don’t worry about that. We don’t look at that. And then somebody comes, oh, we need to have an initiative. We need to save some money this year. So that’s like, Oh, we’re gonna look at this. Oh, we have all that data. So, like, there’s all these scenarios where, like in data, there’s not typically a good, like, we’re talking about, like, master data dictionaries, all that there’s not typically a good measure of fidelity or quality for a data point like, like, in statistics, there is, but we’re just talking about data of like, hey, you know, this is like, like, we were talking on the show the other day. In engineering, if you need a part for a spaceship, there’s this level of precision that’s required, right, within like, point five, you know, millimeters or something. But in data, there’s not typically that I’ve seen any sort of accounting for, like, Okay, this is, like, measured from a very precise machine and automatically input here, where this was a human, fills it out, like you said, on a Friday afternoon, with a very low, like, fidelity, you know, for the field. So I think that’s a big problem for a lot of organizations, and

Joyce Myers 17:21
It’s, you know, like you said, depending on what the data is going to be used for, and oftentimes, and that’s such a great example, because our information and our data either supports a process or is generated by a process, it’s important. So for example, you’re doing business process re engineering. You hear the phrase digital transformation all over the place. People want to optimize their business processes and gain efficiency. So some of that is, you can just remove some steps. But what if in this process, there’s this form, and this form has been around since 1960 Yeah, if you always had this form, because in 1960 you needed that information. But that’s so overcome by perfect example, when I was in the army, we used to track a registration. Why did we track it? Because in World War Two, it was required to drive the trucks on the Germans. Wow. Fast forward to the 2000s Do we really need that? Is it still a required element? Is it just this extra made up number that somebody’s coming up with? Right? So we did a several year study to say, why do we need it? Why was it several years because we had to look all the way back to why we had it in the first place? Right? Thought, reason still valid. Do we still need this? And where else is it being used? Because, just because it’s on one form, data has a life as its own, it’s up in all these other posts, so that fidelity is not just impacting that one process. Yeah, understanding, and that’s where that optimization is, we hear people, processes and technology. I think you gotta kind of know the information and data too when you digitally transform, because you don’t always need everything. But like you said, what else could we use it for? You know, is it, can we drive innovation by knowing this information? What other questions can we ask that are relevant today with, I don’t know, large language models or networks or artificial intelligence, the greater umbrella, right? Is it valuable to us now, it would appear so, because it appears that everything matters now, right? But do you want to put a bunch of junk in? Do you really want to know what you’re using the information for? So I Yeah, that’s a great comment. Research on so.

John Wessel 20:02
So apparently I’m really pushing paper today because I have another thought on the paper thing.

Eric Dodds 20:07
You can order a transcript of this show, mail it to you, yeah, if you fill out the form. But

John Wessel 20:13
There’s another thing around the fidelity thing and paper is it was much easier to think through cost with people and paper and capturing data like, like, oh, I need to capture these 10 more data points. Like, okay, we’re gonna need to hire more people and buy more paper, like that, that is a lot cleaner than like, okay. We need to, like, capture 10 more data points, oh, storage space, basically free, compute, like, a little bit more than free, but not much more. Okay, great. There’s no cost. Well, there is a cost, and the cost is kind of, what you’re saying is, it’s the well, once we add this, it’s actually infinitely harder to remove it than add it, because, like you said, you spent multiple years trying to, like, understand if we could remove this one thing, whereas when they added that they probably spent a couple weeks, I don’t know, but there’s that weird ratio of it’s really easy to add, but it’s so hard to take. And when things appear free up front, it is like, Oh, well, we’ll just capture in case we need it. But it can cause, actually, so many problems. Yeah, if the fidelity is bad or it’s misused, like, we captured it with this requirement, and like, later, like, people use it for the wrong thing. And that

Joyce Myers 21:27
goes back to so many conversations over the years. Right when we started, we didn’t have databases. We had the data that we had, right? We could see ourselves, and then we could see our neighbors, and then we could see our parents and our parents and our parents and all of the family, right? We could see it all with the right permissions, but we could see more, and the more we could see, the more we wanted to see, right? Did we know what we were seeing? Did it matter? It didn’t matter to us at all, but we wanted it, and then once we had that data, nobody wanted to get rid of it, right? So archiving and retention and disposal of the natural life cycle of data is at some point it no longer holds value, right, right? Or it no longer holds value in an active production system,

22:27
right? You can

Joyce Myers 22:28
keep it, archive it off, and get it in a couple hours or a couple of days, right? But we’re so and this is a generalization, instant gratification, gratification, we’d want it. We want it now. We want to be able to log in and find it right. But if you have, if you have 80 years worth of data, does that 80 year mark really bring value to your analysis? If it’s a historical project, it’s probably right. So that’s interesting too, right? And then let’s think about that. We’re talking about paper. We’ve got how many folders on our desktop that we’ve filed Word documents in or PDFs in, right? So those aren’t in databases. That’s unstructured data. It’s our electronic version of paper, of forms of reports. So we’ve got all of that as well, and we’re not measuring it to a finite amount because everybody does it differently. Every word document is different, every right so there’s so much opportunity for us to continue learning and seeing what that next cool thing is that can help us get there.

Eric Dodds 23:40
Yeah, one question I have for both of you, because John, you spent, you know, you worked for a logistics company, and one thing we were talking about on the show recently was how data is try is a way to it’s an attempt to describe what’s happening in reality, right? I mean, the most real data that we can experience is what we’re doing right now. I mean, we are exchanging data live, and it’s happening in real time, right at the pace that time goes and but obviously that, you know, you can’t run statistical analysis on that, right? You have to actually distill that into, you know, some approximation of what’s happening. It’s interesting to think about all the ways that we do that, but in every case, you lose fidelity. And earlier we talked about, you know, sort of how there’s a process, or on a show. Earlier this week, we talked about how there’s this process where you sort of have raw data, and then it needs to be refined so that someone can model it, then you model it and it loses more fidelity, et cetera. What’s interesting about logistics to me, and if we think about logistics as data, i. Uh, in like you are really, actually trying to get pretty close to physical reality, especially when you think about moving equipment or other processes like that. So I’d love to hear from both of you on how we think about logistics as data. How do you control losing that fidelity, because in many cases, if we think about it, let’s just talk about distilling this conversation we’re having into a transcript or a summary, right? It’s okay to lose a lot of the fidelity, because I’m just sort of looking for a summary of the conversation because I want to write a LinkedIn post, you know, to get people excited. And that’s great. You know that it can be very lossy, and that’s fine, right? But when you’re trying to get trucks to, you know, repaired into a certain place for soldiers that may be in battle. Now we’re talking about data that really needs to try to, you know, it really needs to map pretty closely. So yeah, Joyce, why don’t you speak to me and then John would love to hear about your experience as well. So

Joyce Myers 26:02
one of the phrases that is like, I don’t know It doesn’t cause trauma, but it’s definitely there. So as we were gathering the data from all the different places and leaders would say, we want to see where the things are, and we need it in real time, real time. So real time is like right now, when it’s happening. So we got them off of real time to near real time. So what is near real time? Within 24 hours? Within 48 hours. So that’s so important, what you just said, right? Because the farther away you are from the actual event, the more likely that the data is not the same, especially as a truck is moving or on a shipping status. It’s been picked up at the warehouse. It’s gone through six RFID tags, right? And now you know it’s here, so real time, it might still show it at the it will show it at the sixth RFID tag near it might show it at the third or you might still see it at the warehouse, because you’re not tracking all of the tags. We used to see it at the warehouse abroad, you know, in shipment and arrival, that was it. Yeah, you were lucky if you got the middle piece. So I think that’s a really important thing that’s farther away you are from the point of the actual creation of that data you as a business, as an analyst, as an executive, have to decide what is the acceptable timeliness of the data for your question that you’re asking?

27:54
John, yeah,

John Wessel 27:55
I think I can really mirror that. In my experience, the thing that came to mind was warehousing. But it’s very similar to what you’re saying. Is there, there’s typically a, like, absolute minimum requirement of, okay, I need to know this high level. Let’s call it a status, like, picking shipping, shipped arrived. There’s just a few of them, like, we absolutely have to have this. And then the fidelity below that, like, is super helpful for optimization of business processes and performance, potentially. So I’m thinking specifically in warehousing, in warehousing. And one side note, it’s actually interesting to like, there’s all these, like, changing of systems typically, like, if you’re like, if you’ve got like, a third party involved, and you’ve got a carrier involved that’s moving it, and then you’ve got a somebody receiving it, like, each person’s system is different, like, Software wise, but the data, like, ideally, like, stays in sync. So that’s like, a whole nother thing to think about, but assuming that you get that part right, then it’s like, okay, so the shipment arrives on a dock somewhere. It sits on the dock, and says it got stuck on the dock. It got shoved behind something, or whatever. Maybe it was a small ship. It’s like, that’d be great to know, and that would really help with troubleshooting when that shipment doesn’t flip over to be delivered as expected, or somebody called would be great for you know, let’s say customer service to know, like, oh, it’s still on the dock. So those are the types of things that like, when you increase the fidelity, it’s helpful. But there’s challenges where you’re actually typically adding work to increase the fidelity. So you can imagine, if you work in a warehouse, like, Okay, I’m going to scan it in when we first get it, and then I have to scan it in again here, because that’s like a checkpoint. I have to scan it in again here. And there’s some neat RFID technology that can make sense sometimes, but sometimes, like the RFID, like in the private space, is cost prohibitive compared to the cost of the item. Like, if you’re shipping very cheap, you know, unit priced items RFID. The cost, it doesn’t make sense. So then you have that problem, and then there’s the labor problem. Like, each of these subsequent checkpoints, a lot of times you’re adding labor to each touch, which you’re trying to reduce. So it’s this weird push and pull, of like, interesting, higher fidelity with but keeping the labor cost and stuff low, the

Joyce Myers 30:18
trade offs right? And that’s where that date, the different costs. And then as a leader, as the analyst, you can make those recommendations. Here’s a trade off space, right? We could add it in this part of the process, yeah, so working in the corporate job that I’m in now, right, as as so I’m inward facing, working internally for my company, and then my company supports external customers, and so it’s a really interesting dynamic one. It’s the first time I’ve ever done this focused in so I am. I’m learning all new sets of information and data that perhaps I didn’t know before. But the concepts remain the same, right? Do we understand our process, whether it’s a logistics process, or an HR process, or an IT Process. Do we understand it? Do we know that information flows, and that goes back to right sometimes data on its own doesn’t give you what you need this data and this data information to make the decision. It might come from different systems. It might come from different organizations, like you said, sometimes it’s third party logistics. That’s where Master Data and Data Quality starts to come into play. Whether you’re supporting a customer or whether you’re doing an internal for your own business, is everybody calling the customer by the same name? Is every employee called the same name? If I go to a database and I’m Myers, comma, Joyce or Joyce, middle initial, Myers or j dot, Myers, but they’re all me, right? Now, there’s three different entries. For me, it looks on the surface like there are three different employees, right? So that’s where understanding master data, regardless of what the master data is, an employee, a customer, whatever it may be. That’s where I think organizations, especially from a logistics perspective, can really gain some ground by understanding because regardless of what your end goal is, those master data are going to be used across multiple bank goals. So I think we could always just really focus on identifying and managing our master data and having people understand what that actually means. A lot to really bring some improvements.

Eric Dodds 32:46
Joyce, can you speak to you and make a statement about data and information? You know you have data, but you need information that I think is a really fascinating distinction, because in many companies, you just refer to all of that as data, right? We need data or we want to be data driven. Can you speak to that distinction? Because data and information are not the same thing necessarily. So I’d love to hear, I’d love for you to dig in on that a little bit. So I’m going to put

Joyce Myers 33:21
It is like a real life example that has nothing to do with a job, for example. So I wake up in the morning and I want to know how to dress, and I look outside and it’s sunny. That’s a data point. It tells me that the sun is shining, right? So I could look at my app and it shows me a little sunshine. That’s a data point, but it’s 33 degrees out there. Yeah, it’s cold, so now I’ve added the temperature to the thing, so it’s a little bit more information. So one data point, Sunny. I might go out in shorts and a T-shirt. I might make a decision on one data point, but now I’ve added the temperature that sells me cold. Okay, maybe I like it to be cold. That’s okay. So I could make it that’s information. Now I’ve had more than one data point. I’ve got a bigger picture, but the wind is blowing 30 miles an hour, yeah. So as you bring these different data points together, you get a fuller picture. I got some information about Alexa, tell me what the weather is today. Today, we’ll reach a high of 52 degrees, with sunshine and winds of up to 32. I have information about the weather, right? Yeah, temperature is a data point, the sun is a weather point, the wind is a data point, not another point. But when I put those together, I have weather information, yep, right. And so we all do that. We. We all look at our phone, at our weather app. We all, yeah, look at those types of things. So I think, as an organization, people like to talk about data all the time. I can’t find my data. What data do you want? Well, I need to know. I need to know something, right? Well, is it one data point? I need to know we need temperature at two o’clock on Friday, yep, yep. So I think sometimes just understanding that because our reports, our Excel spreadsheets, our dashboards, that’s information. It’s a lot of data, yup. And if you have so much data, too much data, old, bad data, then your information that you’re making

Eric Dodds 35:47
It is cute, yeah, yeah, yeah. It just makes me think about it, even just in meetings I’ve had this week. I wish I could go back and say, okay, is that data or is that information, you know, and just, I think it’s such a helpful distinction. Well,

Joyce Myers 36:04
we can use them interchangeably. It’s, it’s my per I mean, that’s sure. I think it really helps people understand the flow better. Like, if I’m in HR and I onboard an employee and I put that name in there, that employee is a data point, but everything about it put together is the information about that, yep, yep, that maybe starts to be used in other places.

Eric Dodds 36:32
Yep, well, I know we’re getting close here. I have a question. And then John, I want to leave time for you to get one more question in. But Joyce, I’d love for you to speak to our listeners who, you know, heard about a multi year process, you know, to deprecate a registration number, and who maybe really identify with that and say, you know, I’m in a company. We’re swimming in data. We don’t need half of it, but it just feels like a really big mountain to climb to actually make change and turn data into information for our previous topic. Can you just speak to that person after having gone through that, I’m sure, in an environment where security and hierarchy, you know, probably tend to be a little bit more stringent, you know, than say, Yeah, than the average, you know, than the average private business. I just love for you to give that person some tips and encouragement on how to face that when they go into work tomorrow. So

Joyce Myers 37:35
I’m gonna start with change is hard. All Change is hard, right? And so that was changing not only a data element, but regulations and policies and cultural beliefs and use of history and emotional attachment to the people who managed it. So keeping in mind change is hard, and people don’t like to change. So if you keep those two things in mind, instead of starting with the big thing, we’re taking it away for the whole line, right? We said we’re going to research why we need this, right? And we broke it down. Why? If you go with the who, what, where, when, how, whys, who uses it, then you start to get a stakeholder base right. What role do they play? What are they using the information for? What? What are they using it for? Where is it being used? Why did we start using it in the first place? When is it used? When you ask those types of things, when you’re doing change, when you want to remove something, some of those will naturally help us bring out the maybe we don’t need to move it, or maybe the Trino space, like you were talking about earlier. It’s not the value of removing it is not worth keeping it right. Mm, so, so that there’s a lot of analysis that goes in, but patience, right? Is it really true? If you can get into the mode of doing the who, what, where, when, how, why, for all of those kinds of things that really helps, and then what happens if we don’t do it? And so that’s what I would challenge, regardless if you’re trying to remove old data, people do not want to get rid of their data. And so it’s like, well, what will we use it for? When will we use it? When you start asking people the questions and they realize they can’t answer them, then it opens their eyes, right? But just saying we’re getting rid of it, then people don’t feel heard or seen. What they’re using it for, feels negated, that maybe what they’re doing wasn’t. Is important, right? And I’m talking about one specific example, but it took several years, and we actually had a luncheon party where we had a little like bingo chart on when we were going to get it approved. I love it. Yeah, it is one of my favorite memories, actually. But yeah, I would just encourage people to be patient and remember, people don’t like change. Change is hard, but you don’t have to change everybody’s mind, just the right people and then the lower and how lies. And I would say, in everything that we’re doing, from a data perspective, keep those questions in mind.

John Wessel 40:37
I like to ask this question because I know we have a number of data practitioners at all levels that listen to the show. What advice would you give somebody that’s practice? Yeah, and data practitioners like, very broad, like they are more engineering, more analytics focused. There’s a lot of versions of that. But what advice would you get somebody that wants to do more leadership focused work in the future, and is currently doing, you know, maybe exclusively individual contributor work. What career advice would you give somebody

Joyce Myers 41:09
good to know your business? So as a data person, whether you’re a data analyst, a data engineer, building pipelines, building databases, admin Andy, and so he’s a data scientist, anyone of those? Is anyone putting that data together for information to solve a business model? Yeah, when you know that business, when you can speak the language of the business, and it doesn’t have to be your whole business, maybe you really like HR, maybe you really like or you’re doing all the time a lot of analysis for your engineering department, and get to know the business reasons. Because what that does is it gives you bilingual ability, you can speak the tech side and you can speak the business side, and that’s a really big missing gap. We have a lot of people in the business who don’t understand their data at all, and we have a lot of data people who don’t understand the business. I would say, yeah, for me, I started in logistics and accidentally, like, I’m not a data scientist, but I know how to put the pieces together and help others put the pieces together. Because I understand,

John Wessel 42:25
Yeah, that’s great advice.

Eric Dodds 42:26
I love it. Well, it’s been such a joy to talk to you. Joyce, I think this was, you know, throughout three years plus of doing the show. Now, one of the themes that just come up over and over again, and I think this show, really, in many ways, is a great summary of it is there are people behind the data and people who are going to consume the data, and so it really all comes back to the people element, whether it’s paper or, you know, the advanced systems we have today. So thank you for a great reminder. Thank you for your service, and thank you for all of the good lessons that we learn and our listeners learn today. Thank

Joyce Myers 43:02
you so much. I had a great time, and I appreciate the invite.

Eric Dodds 43:07
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