Everyone for having me here, I'm Gabrielle. I'm a PhD student at the group of Jordan Horowitz at University of Michigan. And today I want to talk about how non-eclipid response is fundamentally limited by topology and thermodynamics. And in order to motivate our results, I will use a receptor binding model where we have a receptor here in green. In this case, we assume it has two binding sites. And ligands here in yellow can bind and inbind. So we can have one at the left, one binding at the right, or two of the sites being bound. And the binding rates are proportional to the concentration of ligands in the environment. So if you're interested in characterizing the system, we can look, for example, at binding curves, which gives us what's the average concentration of ligands of bound ligands in the receptor as a function of the concentration of ligands in the environment. So what we see is a switch-like behavior where for low concentrations, in average, there is no ligands in the receptor. And for high concentrations, all the binding sites are occupied. So if we look at the steepness of the scourge, which is given by the response of the average of the ligand occupation number in respect to changes, logarithm changes in concentration, we can study how sensitive the system is to changes in concentration. And an interesting question that we can ask ourselves is a system that's out of equilibrium more sensitive, the one that's in equilibrium. So if we are interested in studying a non-equilibrium system, we always start by asking ourselves what happens in equilibrium, right? And then now we're talking about a general observable F and we have an equilibrium, the fluctuation dissipation theorem tells us that this sensitivity is equal to the covariance of F with N, which is the ligand occupation number. And if we further assume that the observable F is bounded between zero and one, which is natural if we are thinking, for example, as the average occupation number normalized by the number of binding sites, this covariance can be bounded by the average of the observable one minus its average. And we have this proportionality constant in the front, which is equal to two for this case, which is exactly the number of binding sites in the system. So we also can ask ourselves which curve saturates this inequality at all values of concentration. And this is what is called a hill curve, which has this functional form over here where age is known as the hill coefficient, which in equilibrium, it's always equal to the number of binding sites. So now we can ask ourselves what happens out of equilibrium and in order to have non-equilibrium driving in our receptor binding model, we couple it to ATP hydrolysis, right? So now we have a chemical potential difference that is driving the system out of equilibrium. So now if we randomly sample rates for the system and we plot the sensitivity divided by this F one minus F and we look at this ratio as a function of the unknown equilibrium driving, we do see that it's not bounded by two anymore, which is what you would expect in equilibrium. And there seems to be a bound over here that even depends on the non-equilibrium driving. So a question that we want, we're interested and we will be able to answer at the end of the talk is what is this term over here that's bounding this sensitivity when we are out of equilibrium? So of course we need to describe what we're talking about and the overall problem that we're interested in looking at what is the, we're looking at steady state averages of observables that we call Q and there is an external parameter lambda which can be externally perturbed. So what we're interested in looking is the response to the response of changes of this parameter lambda. And of course there are many ways to describe non-equilibrium systems and non-equilibrium dynamics and what we are talking about is Markov jump processes which have this very good way to visualize them using graphs, using graphs where each node represents a state of the system and the edges represent the possible transitions between states. And we always assume by thermodynamic consistency that there is always the forward and the backward rates and all of them are weighted by a transition rate. A very important characteristic of such systems are cycles which are trajectories that connect a state with itself without self-intersection and for each cycle we can associate a cycle force which is the log ratio of forward and backward rates in the cycle. And the cycle force tells us how far away from equilibrium our system is. So, and in the way that we think of response and perturbation in the setup is to have the transition rates being dependent on the external parameter. So, our approach to be able to de-tangle and understand all the possible perturbations that the system can possibly have is to decompose the perturbations in different categories. One specific category is called an equilibrium like which accounts for changing uniformly all of the outgoing rates from a specific state in this case N. So, if I wanna give you a little bit of intuition we can assume that if the system comes equipped in an energy landscape this type of perturbation accounts for changing the energy of the state. And as the name says this is an equilibrium like perturbation which satisfies the fluctuation dissipation theorem. But now we also have what we call a symmetric perturbation which accounts for changing the forward and the backward rates between two states. So, perturbing this edge M and M here which accounts for changing the energy barrier between states M and M. This perturbation is zero in equilibrium but we might ask ourselves what is, how is bounded when it's out in equilibrium? And this is exactly what our main result tells us which is a fluctuation response inequality that's telling us that the response of the observable to changes in the energy barrier depends on two different terms. One is a topological term another one is a thermodynamic, it's related to thermodynamic driving. So for the thermodynamic driving part what counts is the maximum cycle force which is the maximum cycle force over all cycles that contain the perturbed edge in this case M and M. And over here for the topology term what we're interested in is structure called the topological consistent splitting which is the splitting between the states of the system in two different subsets. One that contains states M and the other that contains states N. And the splittings they need to be consistent with the topology of the system. So what we have over here is the maximum over all the possible splittings of the covariance of the observable with a D indicator function over all states that are part of the subset that contains states M. And of course we can numerically verify this result. So if we plot the response now normalized by this topological term over here for randomly sampled rates and look at as a function of the maximum cycle force of the system, we do see that indeed it's bounded by the tangent of the maximum cycle force over four. So now we are actually ready to answer the question that we had in the very beginning and go back to our known equilibrium receptor binding model. So now if we use our approach and we look at the sensitivity, we see that this response can be decomposing five different response while three of them are equilibrium like. So they're equilibrium like in states one, two and four. And the other two are symmetric like. So they correspond to perturbing energy in barriers between states one and two and between states one and four. So now using the bounds that we found we have the sensitivities bounded by an equilibrium like term, which is the covariance between F and the ligand occupation number plus now the maximum between topological splittings between states two and four as the covariance of the observable F over indicator functions over subsets that contain state two times the hyperbolic change of the non-equilibrium driving over four. We can also ask ourselves what happens when we don't have access? We only have access for example to the average of the observable. And when we do that we have access to we obtain a coarser version of the bound which is where we have again this functional form average of F one minus average of F. And here the proportionality term is now two which is exactly what we would expect in equilibrium plus the non-equilibrium driving. So we answered the question that we had at the very beginning of the talk which is this term over here it's exactly an equilibrium like term plus a non-equilibrium term. And we can also ask the same question we asked before it's which curve saturates this inequality for every value of concentration. And when we do that we see that this curve has exactly a heel curve like functional form but now the heel coefficient is equal to the equilibrium term plus this non-equilibrium enhanced term. So here in red we have this optimal non-equilibrium heel curve. And now here in purple we do see this curve over here for an equilibrium system and in dashed for a system. Here we lost your sound. Yeah, I was just trying to ask you if you still hear it or not. Yeah. We lost your last 30 seconds. Sorry, we lost your voice. We don't hear you. Maybe the headphone charge so it's the batteries empty or something. I don't know. Yeah, if you take out the headphones maybe it's better. Can you hear it now? Yes, yes. Okay. So I was almost finishing and what I was gonna say is that the steepness of the curve, of the purple curve and the dashed curve are always upper bounded by this heel-like curve out of equilibrium. So if, thank you for listening to the talk and I would like you to take us hateful messages that utopology and thermodynamics constrain non-equilibrium response. And if we apply those results to a receptor binding model, we do have that the heel coefficient, it's equal to a big little heel coefficient plus a non-equilibrium driving. Thank you very much for the talk. There's a question by David Sivak. Maybe you want to unmute, David. Sure, can you hear me? Yes, I can. Great, really nice talk. I was just curious, this one over four factor appears everywhere in the TANGE argument. Do you have any intuition for sort of where is that coming from? The, well, I guess the intuition comes, you need to go through all of the math and how, where everything comes from. I'm not sure if I have a good intuition but it comes naturally if you're using the matrix three theorem and solving these terms over here. And then, so basically this term that always depends on the maximal cycle force over four, it's a ratio between rates of cycles, unnormalized rates of cycles. So I'm not sure if I actually really answered the question but I'm not sure if there is a good intuition of why the over four. And there's a comment by Tom. Maybe you can comment briefly or KVT. Yeah, it just seems to come up quite a lot. Like factors of roughly, well, factors of four exactly I've seen in a number of papers. I'm not sure if I've seen anything in a TUR paper which has a four rather than a two but I've seen a four in Peter Ryan Ten Walder's paper on, and Chris Govind's paper on energy resource costs of sensing which is obviously a closely related system. And I've seen it in a paper on thermodynamic error of time. Yeah, so I don't know if there's anything deep there but this isn't unique that a four it's kind of fallen out of the analysis. I need to think about it. Interesting. Presumably it's not specific to the network's apology here. No, no, it's not. Yeah. And nothing that I showed is specific to this, I use this example but nothing is specific to this network apology at all. And it's time for a quick question by Jonathan who's raised hand, please. Hi, thank you Gabrielle for the great talk. I was wondering if there would be any if you thought there'd be any merit in using this it's sort of an inverse problem if you had some sort of like hill like graph.


Hello and welcome to Data for City Talks, a podcast where we discuss with industry leaders and experts how they have built their careers around data. I'm your host Shannon Kemp and today we're talking to Laura Sebastian Coleman, the Vice President of Data Governance and Quality at Prudential. More and more companies are considering investing in data literacy education but still have questions about its value, purpose and how to get the ball rolling. Introducing the newest monthly webinar series from Dataversity, Elevating Enterprise Data Literacy, where we discuss the landscape of data literacy and answer your burning questions. Learn more about this new series and register for free at Dataversity.net. Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer at Dataversity and this is my career in data, a Dataversity Talks podcast dedicated to learning from those who have careers in data management to understand how they got there and to be talking with people who help make those careers a little bit easier. To keep up to date in the latest in data management education, go to Dataversity.net forward slash subscribe. And today we are joined by Laura Sebastian Coleman, the Vice President of Data Governance and Quality at Prudential. And dare I say one of the leading experts in data quality. And normally this is where a podcast host would read a short bio of the guest but in this podcast, your bio is what we're here to talk about. Laura, hello and welcome. Thank you Shannon I'm very happy to be here. Thank you for asking me. So I understand this is a recent promotion to Vice President of Data Governance and Quality congratulations. Very excited for you. So do you know yet what that means and what you do and what your job is entails. Yeah, so it is recently, recent just in the past couple of weeks actually that this took place. I've been at Prudential since September of 2021, and just got the news at the beginning of December of 2022. I'm in the same team that I was in so it's truly a move up within the team. We're trying to implement data management. So a range of activities around ensuring that data movement is govern is managed and known that we've catalogued our data and can share business metadata with key stakeholders so they can use the data better, and then improving quality. So I've been with with the team trying to work focused mainly on quality improvement. And now my responsibilities will also include data management widely and data governance in particular so we're looking at making sure that we can respond to policies and regulations. And also that we can implement data governance policies that will really change the behaviors of people at Prudential around their data. So just in case anybody doesn't know who is Prudential and what's the company. Yeah, so Prudential is 150 year old company. It has it started out as purely an insurance company. And now it really is expanded into a range of financial services. We help people manage retirement income we have a group insurance business that works with employers to ensure that their employees have a wide range of benefits. We do a lot with financial advisors and try to improve financial production so that people can do better with their finances, and really, so that they can, you know, be kind of future thinking with their finances. Prudential is really about what they call financial health. And as you have physical health and mental health, thinking about your finances as a way of being healthy and, and taking care of yourself is the, the way that Prudential has tried to try to take it because finances as we all know can be very stressful to people. But if you have the tools if you have the education and if you have the help, then you can be successful with it. So that's, it's a wonderful company. I've really enjoyed being there, and in part because they do a lot of good for people. And they're really working to expand markets and, and, you know, serve populations that haven't previously had the opportunity to really think about their finances and in the way that we're trying to help them think about them. I like I love that approach. And, you know, and certainly being insurance involved insurance and finance. I'm guessing there's a lot of regulatory rules and processes that govern, you know, your data right that you have to adhere to. So how much of your job is is complying with those regulations and how much do you I mean, and do you apply data governance on that. Yeah, that is as is the case with many organizations.  And because Prudential already has a very strong culture of risk management. There are folks in place already who have put a lot of very useful controls around data privacy, and, and other aspects of of data security and other aspects of data management sort of that hard core part of like making sure nothing terrible happens to your data. A big part of what we're now focusing on within within data governance is supporting the effort of Prudential to really be more customer focused and and expand its business or look at its business through a different lens. So Prudential has publicly, you know, shared their strategy of customer obsession. We are, as I said, an old, older company, and we have been organized around the business unit level. And that means a lot of our data is organized around the business unit level. But because we want to really work more fully with our customers, that our enterprise data governance function is a very important part of that. You know, we really need to be able to bring together our data so that we can, we can be more responsive to customer needs so that we can know our customers better and the like. And as I said, we will, we are involved with sort of the hardcore compliance and security pieces of data management, but those are already well under control and, and we contribute to that but we really want to kind of move the needle with our ability to interact with our customers. Yeah, it's a great story for especially for 150 year old company right, dealing with a lot of legacy systems and a lot of you to move the needle and to take it beyond and to grow the business is just amazing. Yeah. Yeah, I think it's a very exciting time to be there. And, you know, because the, the challenges are there but you can also see how the work itself contributes directly to the business opportunities and the business strategy. That's amazing. So, stepping back here a little bit Laura so when you were just a child, a very young child, did you dream like, when I grow up, I'm going to be the VP of data governance and quality at Prudential. As a matter of fact Shannon, I did not. So you wanted to be. Well, I had several, several dreams I, my first desire was to write plays and stories. So, I used to write little plays when I was in elementary school. I remember in third grade, my first play was a Christmas play. And then I wrote a mystery play called the house on Mulberry Street. I don't know where I came up with the name, but there wasn't a Mulberry Street near me, but that was that was what I like to do. And then later I thought that I would be probably become a lawyer. I was applying to college and thinking through those parts of it. My intention was to go into law and I chose my college based on that. And, and then I got so I was an English and history major, and I, I got so into my, my, my English degree that I, at that point thought, Oh, I'll go to graduate school and, and, and try to also be a novelist, you know, so I went on and, and got a PhD in English, English literature, although truth be told my dissertation was probably should have been written in a history department. But I didn't know that at the time.  But at that point, it was somewhat difficult actually to get a teaching position in the humanities. And through a serious in my, my husband at that point I'd already gotten married and I had my first child had been born and I was thinking about the world in slightly different terms, like economic terms. And there was series of very fortunate events. My husband who also was a PhD in English. He, he landed a job as an editor of a magazine in Milwaukee, Wisconsin, and we ended up moving there. And that began my corporate journey, but it didn't quite get me to data. And I took a position as a public relations specialist in a manufacturing firm.  And so that wasn't directly data, but they did hire me because I knew how, how to use a computer, which meant I knew what a keyboard was. And I knew what numbers were, you know, but that I realize now wow that did have a lot to do with data quality, you know we balanced every day at the end of the day and we, and and I was in charge of keeping track of the money in the safe and the number of travelers checks we wrote and all of these things that you know were the combination of data and money. So anyway, when we moved to Milwaukee, I ended up in this public relations role. And one of the things that that they wanted us to do what me to do was right for the internet. Which the internet was fairly new, but I also that that meant I was working with the it guys in the company. And I was completely fascinated by what they did like I, I didn't know anything about how programming worked I knew data entry with, but that was the start of just this connection with the technical side of things. And then, when I left that job, I ended up in another corporate communications type role. And I ended up work again working with the it team, and the company I worked for was an insurance company, they sold workers compensation insurance, and their, their business model was they sold a very high end product. And they sold to customers who had a very bad claim experience right so the customers where they had a lot of injuries whether, you know, injuries or severe injuries. And they would work with these customers using the claim data as the basis for a set of improvements to reduce accidents reduce injuries. And so that was my, my first real exposure to a business using its own customers data to help its customers. And of course I didn't know it at the time. You know who knew what would happen with data but it had that job was really formative for me because it had the combination of real use of data that had a very meaningful effects on people's lives and on the success of organizations and an improvement cycle. So they base their, their reduction of claim ratios on sort of six sigma type principles and total quality management type principles and so I had this introduction to both of those things at the same time and and that really piqued my, my interest in data. So, it wasn't until after that, that job ended that I. Well, yeah, as part of that job. One of the IT managers who was responsible actually for developing applications that shared that data via the internet, like a customer could log on and see their own claim experience data and see recommendations and the like. I was working with that IT manager because I was supposed to again write copy for that. And he hired me to manage the people who were developing those applications. So that was at that point I suddenly was actually in IT. And the product that I, I, my team was responsible for producing was a data product. And I started like thinking about data very differently I started thinking about it quite deeply. You know, how do you, how do you do this well. How do you, how do you use it as a communications tool. So, that was, that was what I would say was the beginning. And then, unfortunately, that company was purchased and then 911 happened and the company that purchased it basically ceased operations in the US. And I ended up at United Health Group. And that was really the turning point that the job that I took there was as a data quality manager. It was early 2000s to 2003. And they had a second generation data warehouse and they wanted to make sure that they could report on data quality. So, they brought me in. I, when I think about it now I'm like wow I had no experience in data quality. I had some, but nobody really had had experience in data quality management then you know, there was a lot of a lot of conversation. There were some very smart people who were beginning to talk the talk so you had, you know, Tom Redmond had already published his first two books focused on data quality and David lotion had published on data quality and of course Larry English. So we, you had this beginning of a true foundation. And then the MIT team was working on data quality as well and they started to have they were having conferences and seminars and such at that point so they were starting to launch. So I kind of entered the field of data quality management at an optimal time. There was enough of a foundation so I really had a lot to learn and I had the means to learn it. There was still a lot of opportunity and the landscape as you well know was, you know, already changing, you know, the capacity to collect data the capacity to process data that was all really speeding up at that point. So, that meant that I could learn that as much as I could, and start to apply it and and so that's what I did. So, I did not dream of it as a child, but I got, you know, to that point through, as I said a series of fortunate events. I love it, you know, one of the reasons that I started this podcast and chose this topic because there's no straight path to being in data right like there's so many different journeys. And I think that's the first time I've heard PhD in English to data, which is fascinating. I love it. But I'm guessing that passion though as a child for storytelling still plays a big role in what you do today. Yeah, it does because you have to be able to communicate well with people, you know, a lot of people. Well, first of all, there's a group of people that love data, as you know, and are fascinated by it but those aren't really the people that you need to convince their kind of the choir, you know, and I mean, I, I found out that I was one of those people I love the way I work. I love seeing patterns in data and fascinated by what we collect data about and what, you know, and how, how we go about that so there are lots of people that are very interested but the vast majority of people are often intimidated by data, or by technology they can kind of mix and match the two. So you have to be able to communicate with them. And part of that obviously storytelling skills come into play as part of that and being able to make good decisions about how to simplify data, like, so that people can understand it, you know, give them a door in or a window in so they can understand data, because I think that once you have that experience of seeing a pattern or understanding something better because you have the data to back it up I think most people actually find that very exciting and interesting. And, and they realize you don't need to know everything you don't have to be a statistician to work with data that kind of thing but you, if you can understand what the data represents and you can learn something through it. I think that most people find that really exciting and interesting. So you have to help them have to help them get there. And so and, and I mentioned in the beginning you are considered one of the leading experts in data quality and definitely that path explains how you got to be considered that and you've literally written a couple books on it. Yeah, I've written, I've written two books directly on data quality. And I've been part of my passion has been, how do you measure and make the quality of data understandable. So the is called measuring data quality for ongoing improvement. And I, from the title, you can probably guess that I took very seriously the role of measurement within the improvement cycle. So if you, you know, if you can figure out what are the things that are important about the data that make it of higher quality, and how do you represent them so people can understand when the data is of low quality or of better quality. If you can kind of crack that nut, then you can make the decisions you need to make about where to apply resources for improvement. So when I, in writing that book, actually worked with a team of people that included a enterprise architect, a developer representative of the of our business team and a person from data governance and we were trying to launch a data governance program, and people were saying things about data quality that were, I thought, somewhat naive.  You know, that those two extremes of it's impossible or, or here's all these statistics that I can give you, and you know, do something with them, or don't do something with them or thinking they're going to speak for themselves. So we really wanted in that book we really wanted to think through a framework for how to think about measurement. And then in the second book which just came out in early 2022. It's a very different sort of book in some ways because I really, I, I've been in this now for close to 20 years. And I feel like there's been an evolution in data quality management but it hasn't actually been to the good. So, so what do I mean by that when I, when I started in data quality management there was a lot of discipline around data quality management that came directly from manufacturing and service quality models. So when rich Wang talked about total quality management and Larry English talked about total data quality management. They were really drawing on that well of quality management for manufacturing and the services same thing with Tom Redmond, right the three rich way rich Wang Tom Redmond and Larry English, they are rooted in that in that in that history and in those methodologies. And I learned, but I think a lot of data quality management now has, it has been, it's become an adjunct of data governance, and, and data governance people in general are not rooted in that in in that set of methodologies. There's not been the sort of real in my, in my way of thinking the real discipline around how you think through data quality problems. So, I think it's, I think it's done a kind of disservice to data quality management so in this second book, part of what I wanted to the questions I wanted to answer why, why has that happened. What's gotten in the way of the path that seem to be laid out, and how do we make it better. Right how do we get on track and how do we apply more discipline because I also really think that we need both governance, which sets policy and standards and tries to really change behavior and quality management, which applies standards and, and takes very seriously how we measure things, but also wants to change behavior you know there's a lot of a lot of intersections there. So the second book is, is kind of wider exploration of those questions. And how do we, you know, how do we handle not just the technology part of data quality management, which we have to be in the, we have to be in the right to technology that creates manages stores and provides access to data, but we also need to account for people and process in a, in a much more disciplined way, we can't let those things go. And then we have to account for the fact that what we're working with is data. And that's different from a physical product. It's different from us, even a service because you know you can reuse it you can use it multiple times it's, there's so many characteristics of data that make it slightly different from other resources. So the second book, it kind of takes this wider span of, of ideas and tries to just rethink them. Hello it. It's so timely I think, from based on what I've seen and how data quality is coming to the forefront again. Yeah. Yeah, it's definitely, I think people are revisiting. They revisit quality. As they, as they see more data. Yeah. And I don't want, I don't want people to forget that we have a we have a history of doing this, you know, there are a lot of people. If they're new to the field or if they're not aware of all the thought work that's gone on. They can. I assume they're the only ones that have these problems and, and, you know, so part of what I'm, I'm trying to present is like, there are a lot of problems that people that organizations have already addressed or that there are methodologies to address you don't have to reinvent the wheel. Absolutely. So, tell me Laura. What is then your definition of data. Not surprisingly, given my background in literature. I always talk to folks about data as a representation of reality. So it is a means through which we represent people objects events. And, and when we think about it in that context. You, I, or I like to think about it in that context because it allows me to talk about the kinds of choices people make when they represent anything. You know, so if you think about a painting as a representation of reality right there's no question that the painting is separate from reality, it isn't, it isn't the reality it depicts people sometimes forget that about data. It's also very clear that an artist makes choices about what to include in the frame, how, what the palette is what details to include and exclude those kinds of choices are made when we, when we create data. When you start to think of it that way then when the question of quality can be can be framed in relation to the choices that people have made about how to represent an event or a, you know, a person or an object. And I realize that that is a little bit abstract. But you think about it right if you are collecting, let's say you're collecting data about people and, and you, let's say you run a, a store that sells athletic equipment. Right, you're going to want information about people like their names and their ages and their, you know, where they live all of those sorts of demographic characteristics that any business is going to want to know about their customers, but you're also going to want to know things like what sports they play, you know, what their shoe size might be. Right. I mean that's more important in some ways than some of the other clothing choices that you might make right. I definitely want you want to know if you can you know what organizations are they affiliated with you know is this is this eight year old who walks in, you know, a baseball player or soccer player, you know, is he in little league is she, you know, on a travel team, whatever those are things you might want to know, whereas if you own a different kind of store let's say a bookstore you're going to want to know other characteristics of people, you know, what are their, what are their hobbies what are their interests you know if you, if you see people who buy a lot of cookbooks that's kind of one group of people if you see people who buy a lot of poetry that's another group of people. So, yes you want names and ages and zip codes and all of that but you're thinking about your customers in two different ways if you're selling those different products. So those are, those are choices that you have when you're setting up your systems to collect data you make different choices, and that can affect the quality of the data as well. So, I, that's why I like to think of it as that representation and a set of choices in terms of what we represent. That makes total sense. And I, and I love the abstract comparison. With a robust catalog of courses offered on demand and industry leading live online sessions throughout the year. The Dataversity Training Center is your launchpad for career success. Browse the complete catalog at training.dataversity.net and use code DB talks for 20% off your purchase. So, so then do you see the importance of data management and the number of jobs working with data increasing or decreasing over the next 10 years and why. So, I, I see it increasing. And I've, I've, I've had a mixture of reactions to some of the changes that we're going through. So I'll step back from the question just a moment and, and, and say.  Right. And I told you that I worked for a manufacturer. And in both of those positions. I realize I, I had a lot of things that I did working with data that, you know, we're just part of my daily work. Nobody called me a data manager, but a lot of what I did at the bank was manage data. You know, how much money do we have how much actual cash do we have in the, in the safe, how many people went into their safe deposit box today, you know, those kinds of things and I was recording data continually. But it was part of daily life. And I know, you know, in the, in the companies that I've worked for, which has been mainly insurance since I went corporate. Many people in insurance companies are creating data or using data, that's their job. I feel like at one level, there, there is a really strong need to recognize that people are creating and using data in their regular jobs and be, and as doing that there's a level of managing of that data. They have to engage in data management at least in their daily jobs and I think we kind of miss that that part. But there's also all of these changes in technology and how we collect and use data and, and the uses of data have really exploded. Right. So, when I think about the work that data scientists do, for example, one of, you know, one of the big obstacles to getting data science work done is that data is disparate in an organization or I would even say, I would, I would prefer to say heterogeneous, because it's not like people are separating the data, but different people are creating data in different ways and so you have very heterogeneous data. And, and the data scientists want to be able to use that data quickly and, and, you know, effectively so you've got more data products than you previously previously had. So, there is definitely a need for in that middle ground where people's job is really managing the data itself, you know, ensuring that there are standards defined and that people can follow those standards and, and create data that is less heterogeneous. So that the people that are using it have fewer obstacles that they have to get around in order to use the data. And I feel like that's really where all of our data management skills and you know when we think about professional data management that we need, we need to really hold those skills and recognize changes at both ends of the process, right, of the folks that are data creators, data producers, they need to be aware that way over in the data science area and the analytics and bi that there are people that need the data that they're creating that it's not just to get the transaction finished, it's to really understand the business and to understand the customers and to make better choices that will help both be successful. So, I feel like there's definitely, I, in that sense I definitely see the, the need for an increase in the number of data managers and I want us also as a profession to be more aware of the everyday life part of data, you know, like I'm doing my job and I'm creating data, and then all of this opportunity within analytics. So, there in a sense the real customers of data management because the inputs are going to be produced, regardless, because you know you need to sell products or process claims or any of the activities that of running a business. If that data management middle position can be more successful, then you not only get your claims processed, you also learn more about your customers at the end and you can help influence them and help serve them better. So, yeah, I think there's, I think there's a lot of opportunity, and I, and I hope that results in, in more jobs and I also hope it results in like us becoming. I don't know the word. I keep saying disciplined I, you know, I really do feel like there. You have data management provides a lens into an organization so you have to be able to see the big picture of the organization, but you also need to be able to home in on details that could go wrong. So, that's what I, that's kind of what I like about it. So, so what advice would you have for those looking to get into a career in data management, maybe it's specifically into becoming a data quality specialist. Yeah, so from the data quality perspective I have, I have lots of thoughts, you know. The concept of data literacy, the concept of data literacy is, is becoming something of a buzzword now but I, and I, and that it's unfortunate that it's a buzzword but it is fortunate that people are talking about it. Because I really do think that they're that, that one of the most important things for anybody working with data is to understand what the language of data is. And a lot of the language of data is also the language of data management. So I would say if you are going into either data quality management or data governance, learn that language of data management and understand, understand data in an organization in a holistic way. I've been, I've been trying for years to come up with a good metaphor for, for data within the organization. You know, we've, we talk about the lifeblood, we talk about data is the new oil, you know, both of those are help a bit, they help people understand the role that data plays. My, what I've been saying in my books and also in just when I've talked with people is, you know, data binds the organization together. It, it allows different parts of the organization to connect with each other. So plays that role of, of connector. And so if you can look at your organization and see those connections. Then that is a really good way to think about the problems you need to solve in quality and the problems you need to solve in data management, generally. And in governance, you know, like, okay, if these connections aren't happening the way that they should, then those are the places where you need to work. So getting that picture of your organization through its data is one thing that I would strongly, strongly recommend. The other thing is, it's ultimately a, so much of what we do is about how people behave. And so having the having perspective on people and how they interact with processes and technology is so important. When we started this, I was so excited about the data itself and the patterns and just the way that it worked and how much of it there was, I didn't pay much attention to the people part. And in recent years, I've really been giving much more thought to that.  You left a big part out. This person doesn't understand you because you left a big part out that you assumed everybody knew but they, but they don't. You know, so I think paying attention to the people part is just incredibly important. Like, people will make the changes that they need to make to make data better. And if we ignore that at our peril, as Tom Redmond would say, at your peril. That's great advice, Laura. And, you know, in addition to your books, are there any additional resources for to help learn that language of data management. Yeah, so obviously you guys at Dataversity have just been super with the kinds of educational programs that you've put together and the, you know, the really vast scope of that. I'm always impressed and, and really happy, really happy that you guys are there. There's also data itself so the data management association. And I, I didn't mention this earlier I probably should have when you were asking about the books but I was the production editor on the DM box to. And that was incredibly beneficial to me as a data management professional, because I got, I really had to think deeply, not only about data quality management but about all the other knowledge areas within data management. So, I'm a strong, I'm a strong proponent of learning through the DM box and also using that as a, as a starting point to go more deeply into other areas, particularly with data quality management because there are a lot of really good books on data quality management and they're, they're aimed at, at different problems that you can solve within that as a, as a, an introduction to the, to the DM box, I also published a book called navigating the labyrinth, which is at the time the name of Dama jokingly referred to it as the cliff notes of the DM box. And it is kind of that's what it's, it's meant to be but it also the intention is also to help people within data management communicate up the ladder. So, it's an executive guide so that's a good resource. But one of the books that that I would recommend for people starting on data quality management is the net is specifically to net McGilverry's book on on the 10 steps, because she again takes those takes that quality management methodology and makes it very real, very concrete for whether you're doing a very small project or whether you're doing a larger project she gives you the tools and templates that can really get you started. So, there's a, there are a lot of just really, really good books out there. So I would go to the bibliography of the DM box as well to, to tap into that expertise. And the DM box is massive. It's great resource and just for anybody who doesn't know it stands for the data management body of knowledge. And it indeed is that and use that mentioned it's on 2.0 the second edition of that book. Right, right. Yep. Love it. Well, Laura so and where can people find the DM box and your books, are they all on Amazon, I believe right. Yes. Yeah, so they are available on Amazon and I, a lot of data chapters actually will have a discount. I don't know that they can help people with, if you order direct from the publisher on the DM box. So, find data chapters thing go to dama.org dama national that's yep dama international very good. I love it. Laura, this has been so great I didn't so many things that I'm learning about people and I love your start as a storyteller I think that is just phenomenal clearly it's an evolution that has gone far for you in your role with data. But yeah thank you so much for the taking the time to chat with us today. Yeah you are most welcome thanks for inviting me Shannon. To all of our listeners out there if you'd like to keep up to date in the latest podcast and in the latest in data management education you may go to the university.net forward slash subscribe until next time. Thank you for listening to Dataversity Talks brought to you by Dataversity. 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