 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 with William A. Tannenbaum, lawyer, partner, and the head of AI and data law practice at Moses Singer. 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 can help make these 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 William A. Tannenbaum, lawyer, partner, and the head of AI and data law practice at Moses Singer. 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. Bill, hello and welcome. I'm really glad to be here and to speak with all the DataVersity members. So this is very exciting. You're the first lawyer that we've interviewed. We've interviewed a lot of different practitioners and consultants and vendors and marketing, but first lawyer. And as I mentioned, you are a partner and head of AI and data law practice. So what does that mean? What do you do? Well, a lot of firms, when they focus on data, they focus on privacy and the risks of data breaches and the stuff like that. The group I've built takes a very different approach. We focus on data as a business asset, something that can be used externally to commercialize data and something that can be used internally to develop strategy and to improve operations and actually reduce legal risk as well, right? Because there's regulations you need to comply with. And we also focus on how do you make data professionals? How do they work with IT professionals? So when you use data as a change agent, the data drives the shift. That's the strategy. But it needs to be implemented with the IT team. So we work with ITOs, chief CIOs, chief data officers, and in-house legal departments. Fascinating. So how do you work with data specifically in your job? So is it mostly advising or do you have a lot of data as well that you rely on to assist companies like this? Well, we use a lot of internal data here at the firm. And we use some AI to do our stuff. But to focus on what the group does, that serves clients, right? We provide services to companies that come to us with either strategic questions or specific questions. So a lot of the things we do are, I guess there's four of them. One is we help companies develop internal data policies. And that would be how do you govern compliance? How do you govern compliance with regulations, with company policies? Lawyers call something playbooks, which are really a whole bunch of templates for starting contracts that you use to reflect company goals, limits, liability, and then they get negotiated. But we draft the playbooks for companies, then the other kind of agreements we do. And let me just back up a second. So a policy isn't really an agreement, but it's very similar to an agreement. It's just not a legal document that forms a contract. And then the other types we do are if there are intra-company divisions that are exchanging data and the data professionals want to be sure the data is not misused because it was built in a certain way, then that's an intra-company contract. Then there's two kinds of external contracts. One is when you do a contract with a provider, a vendor of data services or data products. And then the other one is when you're doing an external contract with another company. So you're commercializing your data or you're providing data, you're on one side or the other. So those are the basic kinds of agreements we do. And in connection with the agreements, there's what we call counseling and advice. So we will advise on the risks because I could write an absolutely perfect contract, except the other side wouldn't sign it. So we have to decide what the business goals are and prioritize the big ones. And there's always some risk, so we have to take the risk and then put it in a box so that it's manageable. And then we talk about intellectual property protection, privacy laws, data breach, all this legal stuff that people come to us to say, how do we do this? And that's what we do. Sorry to interrupt, but we also work again, as I said, with the combination of data and IT, and then work very closely with in-house legal departments or procurement, whoever in the company is in charge of doing data-type things. That's great, very important and things that companies need to tackle and address. And I love that proactive approach. Let's back up here a little bit. So tell me, Bill, when you were very young, just a wee lad, is this what you wanted to be when you grew up? But did you always want to be a lawyer, first of all? Not when I was a wee lad, but when I got older, yeah. And the history is kind of interesting. So I went to college, I went to Brown. And Brown, you can make your own major. So I designed one in the history of science and technology. And I decided that's really interesting. And when I was a young lawyer, there really wasn't like IT law. It was a little too new. And I did become the president of an organization called the International Technology Law Association. And that was a bunch of lawyers who were in this field. We were new in the field and we were going to kind of make it up because it didn't really exist. And just a secret. So close your eyes, close your ears. But a lot of things that lawyers do is make stuff up. So the new stuff happens at our desks first. It's not like you can read a case about this. It's not like you can go read a book. So we think really hard and decide, how do we structure this? So I did a lot of technology based on my major. And then I started doing intellectual property. And then I started doing technology services for companies. And in recent years, I've decided that data is an asset and that most law firms are not approaching it in the right way. So that's when we go back to when I discussed it as a business asset. And so right now, from a legal point of view, there are legal boxes that people like to put things in. It's IP, it's real estate, whatever it is. I think the data as a commercial entity as an asset doesn't really fit within a legal box. So what do you do about that? And there are two ways to approach it. You can have a general theory of what data is for legal purposes. And it doesn't mean it's the same thing as for real life purposes. But trust us, lawyers know what we're doing and we need to put it in a box so that if it ever goes to a judge, they know what to do with it. But there's a lot of innovation going out there. And that's the really fun part. So just to go back for a second. So when we do data licensing, what's the problem? Problem is, how do you define the scope of a data license? How do you define what people can do with the data? So as I mentioned at the last speech, I gave a data versity, I've developed a model that I call decision rights. And that's how do you define the scope? You say, if the easiest way, the most precise way to say is I'm giving you this data and you can make certain decisions on this data. And these are decisions you can make. So just for example, it'll be healthcare. So you can make a series of decisions relevant to healthcare, but you can't use that data and go out and do something with automobiles and then sell it. So that's a way to define the scope of data when there's no real good intellectual property license or something that fits. And that's kind of an example of what we do. And we often get asked, well, why don't you just have the AI write the contracts? Good question. Do I think AI is smarter than I am? No, I don't. I think I can do this better than AI can. And the limitations of AI are that there's a lot of soft parts of contracts. You put material compliance in or you say three out of seven times. There has to be some squishy stuff to get the deal done. And sometimes no one really knows what their exact requirements are. So that gets worked out. So AI can't really deal with the squishy stuff. And also sometimes the contracts are just a little bit illogical because there are provisions that don't exactly line up or you have to make compromises to get the deal signed. So that's a judgment call. Two days before you're signing the contract, you got 10 issues. What are you gonna do? Usually lawyers will look at and say, well, we'll give you two, we get two and then we'll compromise on the others. That's not something a machine can do right now. But stay tuned, machines are pretty smart. Lawyers aren't perfect and this will go. Yeah, AI is getting better, for sure. There is no doubt about that. It's pretty impressive what's happening. But so you created your degree at Brown. What inspired you to do that? It sounds like you have a lot of passion around technology. So where did that come from? And when drove that decision? Well, that's one of the reasons I went there. It's cause you can make up your own major. And Brown is really good for people who have kind of an entrepreneurial approach to their education. People who are very curious, people who don't wanna do the normal things. And so we have a saying that there's this college up in Cambridge, which will remain unnamed. And they think that they rule the world. And so we say, fine, you rule the world. This guy's at Brown in Providence, we change it. And that's what we do. So I was very attracted to the whole thing of Brown. And then I didn't like technology. And I will say this, I am not a STEM person. I don't wanna sit there and do higher math. Not sure I could, but the whole premise of my major was that technology and science, they don't float above the world. They're part of their time. So there was social Darwinism before there was Darwin. And then Darwin, part of the whole culture of time at the frame of the time was that, there were certain countries to be blunt and they could have colonies and they were better. So there was a hierarchy. So you see a lot of that in Darwin. If you do it today, you're gonna do more genetics so you don't have the cultural baggage. So the fun part about history of technology is that it's a branch of intellectual history, but it's still history. And what is history? History basically is saying, what happened and why? And why do I think that's the case? Because things change over time, they're very present tide. So people are gonna think of the civil war one way today than they did a hundred years ago. So that's kind of the fun part. And then when you get to law, it's very simple and it's simple. It's like, what do you wanna have happen and why? So it's the same kind of thinking and that's why I enjoy both. So then as you're majoring in the history of technology, what inspired the addition of going to law school and becoming a lawyer? Because I just like, I like lawyering, or I thought I would. And my father was a lawyer, so I got an insight. And I'm a little nerdy, so I did my research and decided what I like. And I knew when I talked to people that there was room for innovation. A lot of people think lawyers just do things with paper that have already been done before. And some do, I don't. So when I was doing the history of, when I was doing software law really early, there weren't a lot of rules that we knew. So it was fun just to kind of create it. And it's kind of the same thing with data. When I went back before, it's like, what is data legally? So in a contract, you may define it one way. And in another contract, you may define it another way because that's tied to what you're trying to accomplish. So the connection between college and being a lawyer is that it's, there's a lot of commonalities to it. But law kind of takes it to the F degree. And I will say that my daughter has an MFA in creative nonfiction. I don't really know what that is, but I think I do creative nonfiction when I'm a lawyer. A lot of respect for that, yeah. That's amazing. I love that. I love that you're innovating and not just practicing the laws that are in place already. And that- Five years, I just wouldn't do it. And the fun part about being a tech lawyer is whatever you're doing in five years, you're not gonna be doing it. You don't know exactly what you're gonna do, but then something comes along. Like outsourcing, I did outsourcing. When I started as a lawyer, was there outsourcing? No. And when you're doing data, could there have been what there is today? No, because now you've got more powerful machines. They're a lot more easier to program. They're faster and storage is virtually cheap. So you couldn't have done all these analytics five years ago, but just wasn't underlying tools to make it happen. So that means could you do data five years ago? No, because no one was doing it. So now people have started and things are usually ahead of the law. So we're trying to catch up and make it safe. You talked already about a couple of different definitions of data. So is there a foundational definition that you kind of stack everything else on or kind of shift this way or that? What is your definition of data? Well, it's a little squishy, but that's intentional. Because if you make it rigid, then you can't do what you need to do. So what lawyers do is we manipulate concepts. So we don't want them to be solid concepts because then there's no room for innovation. And it's like big data works with stuff that's happened. Lawyers deal with stuff that hasn't happened yet. So we have to be innovative about what are we gonna do with this new problem? Or how are we gonna solve this weird problem but still make it fit in the law? So we have to take something and put it in a legal box to make it work, but it's not exactly, if it's in the legal box and that's a little bit of form over substance is what we call it. But we have to bridge that gap. And so again, the fun part is just the innovation and my definition of data. You can debate whether data is a technology or not. I don't really think it's a technology even though, if you think it operates and accomplishes things, then you may claim that's a technology. But the focus that I use is what I built my law firm's group on that it's an asset. And sometimes it's a tool, sometimes it is what you're buying. You're buying a bunch of data. And I think, in my mind, you have data that turns into information that generates insights. And when the rubber hits the road, it's when generates an action, an event, a result. So I think there are those four stages in data. And when data turns into information, it's not quite data anymore. And then when it turns into an insight, it's clearly not data. But if you're using data and you're not using it to get an insight and to do something and what are you doing? So that's what the business uses and that's what we do. I love that. I've heard over the years, of course in our webinars and things like that, many people call data as an asset, but I really like that perspective of it. I think that's really makes it a lot more clear for me. Anyway, thinking about it in terms of, in legal terms. Yeah. And sometimes the definition of asset wasn't really an asset like, outside of data, what would an asset be? An asset would be this really cool machine that makes a car. That would be your asset. So sometimes when people talk about asset, they talk about it in the data science purpose, which is it's an artifact, it's a thing. We consider it something that has business value. I mean, that's what you call a lawyer. And that's what I mean. So asset means one thing in data professional land, and it means something else in the commercial legal land, which of course is a problem because then people, they're both saying data asset, but they mean different things. Very true. Very, very true. With a robust catalog of courses offered on demand and industry leading live online sessions throughout the year, the Dataversity Training Center is your launch pad for career success. Browse the complete catalog at training.dataversity.net and use code DBTOX for 20% off your purchase. So do you see the importance of data management and working with so many companies now dealing with their data? And do you see the number of jobs working with data increasing or decreasing over the next 10 years? And why? Well, data doesn't manage itself, period. It's pretty simple. So someone has got to do it. Someone's got to build a solid foundation that can be flexible enough to do different things. But without the foundation, what are you doing? You don't have a common vocabulary, you don't have a common data set, you're just thinning your wheels. It's like asking some IT guy to define data or an IT guy data flows through pipes and it doesn't really have a content. So I think data management is really critical and it's become more and more critical when data is a change agent, data has external commercial value. So to make it an asset, you have to manage it. And it's easier to build in regulatory compliance under retrofit it. So management is gonna be that. And when data becomes internally strategic for a company, then things change because now it's, business people are really starting paying attention to it. And do they want self-service analytics? Do they want cloud-based analytics? What kind of machine learning do they want? So when you talk about all that stuff, that's terrific. But you're not gonna have AI without data. And you're not gonna have good AI without good data. And I'll give you one example because I do some healthcare tech and they were trying to teach the machine to distinguish between like a skin rash and a malignancy. And I feed it a lot of photographs and you let it basically do pattern recognition. Well, what was the flaw in this approach to data? When doctors think that it's a malignancy, they usually measure it because without the machine, that's an indicia of the risk. So for some reason, when these people did these photographs, they put a ruler in when they thought it was malignant. So when you feed it to the machine, what does the machine do? The machine serves. Is it a ruler? Or not. So at the end of the day, you basically ended up where you started. Yeah. When people thought it was malignant, they put a ruler in. So the machine goes, oh, if it's a ruler, it's malignant. So unless you're really focused on what data is, then you can't manage it. And there are certain assumptions that people who are not in the data world, they make bad assumptions. So going back to healthcare again. So there's this thing called an EHR. You have to have equity. How fun being a lawyer if you don't have equity. So EHR is the electronic health records, which is the key database that hospitals use. So somebody from Silicon Valley will come in and go, wow, look at the size of this database and look at the longitudinal dimensions and look at the breadth. It's got 10 billion patients in it. This must be really valuable. And what do EHR systems have? They have, you put the illness in a box and the box turns out to be reimbursable or not reimbursable. So if you think about it hard, what's the purpose of EHR? The purpose of EHR is to get paid and get reimbursement. It's not a collection of clinical data. So if you run all these analytics, you're running analytics on kind of what the government decides is a reimbursement box, which isn't the same thing as what doctors would do. So you run all this data analytics on a very big data set that is built to do something that you're not doing. It's built to get reimbursed and you're trying to figure out how to do medicine. So good data management is, okay, if you wanna do this, here's the data you need. And you don't need this, even though it's really cool and attractive and it's got a lot of data in it because it's built for a different purpose. And I think that's where when we work, that's where we work with people who do data management because they can tell the external people, what do you really need? And then we'll give you the data. Or we have data and you can do certain things with it and we have a high confidence in it. But if you wanna do it for something and you're using third-party data that we bought for one purpose and you wanna use it for another purpose, it's like, whoa, it's back to my EHR example. It's not fit for purpose. So I see data management as, if you're gonna use it as an asset, then you've gotta manage it. And if you manage it, you've really gotta know the science of data. And that's what makes you special in my life. I like it, yeah. So what would advice would you give to people who are either looking to get into those data jobs or into a career like yours where they wanna be a lawyer and work with data as an asset? Let's put, I wanna ask myself a different question, okay? So here's the question I ask. So you're in data management in a company and the world is changing. What are your opportunities? So let me ask that question. But that's okay with you, I like that question. That's a great question. I think the answer is, you've gotta think about it from the perspective of the business units, which everybody does, but you really have to keep that in mind. And then you've gotta understand that when you do a deal and it's really critical to the company, the people who are doing the deal, who are usually lawyers and business people, are under intense CEO or a C-suite scrutiny because it's now critical and it hasn't been done. So that means a couple of things. That means you have to learn to speak English because these people don't speak data. So if you speak data, you say data asset or integrity of data or a life cycle of data or stuff like that, they're gonna assume it means something that it doesn't mean. So you have to set the stage for them. And then if you wanna succeed, you gotta pretend you're sitting at that executive table and you gotta say, okay, those people don't wanna know about all the good stuff that I do. They don't wanna know my homework. They just wanna assume what they wanna assume. So you gotta think, what are they trying to accomplish? And the more that you can help them accomplish that, then that you become a key valuable guy. It's like, we don't understand what this data is, but Sally can tell us that you can do certain things with this data, stuff you haven't even thought about yet. And certain things are a little risky. And then you become part of the business team. And I think that's fun, right? Because now you're doing something it's just not making money. It's just kind of doing something cool, right? Cause now data does things that it didn't used to do. So my advice would be, you can either do data stuff, which is a very satisfying thing, or you can kind of move over and be a real advisor to those executives that are doing something commercial. And then everybody always dream, not everybody, but a lot of people, I would love to be in the C-suite. I would like to be at that. All right, if you're there, then the pressure's on. Cause you have to do something different than you used to do. So if you're there, you can't be quiet. And if you're there, you can't say, this is the perfect result. Because from a lawyer's point of view, to do a deal, there's always risk. So you have to assume risk. And so you have to have somebody tell you how risky is the risk. And that's one of the things data people can do because the other people really don't have any idea. So it's your fundamental knowledge of what you're doing. And then if you need more data, you're gonna say, we don't have this data, right? You wanna do all this marketing stuff. And we have no data on any marketing campaigns, you know? And then for some reason, marketing and sales are different. Not that I understand that, but whatever the difference is, it's different. So then you've got to deal with that. So my advice is just, you know, think about it from the other perspective. And I say this because that's how I do contracts. I sit there and go, okay, what is the other side want? What is the other side thinks the risks are? And then I have a better understanding of what I need to do. And unless you understand what the other person is thinking of, you can't keep moving forward. And I just did a contract or what the other person was thinking of was just stupid. And so we had to kind of politely reorient them to a better paradigm to deal with the problem. And I'm not saying that's gonna happen at your company, but I am saying there's gonna be a certain lack of fine tune understanding. And that's what you bring to the table, you know? Makes sense. And any advice if somebody wants to be a lawyer in this space? So if you're gonna be a lawyer, you've got to decide, I think that you're comfortable with uncertainty and you're very excited about being creative. And I know most people don't think we're creative. A lot of people think we're just this tax you pay to walk across a bridge and lawyers are fungible and, you know, all right, we're gonna pay a tax. That's just what we have to do, you know, or we've got to do legal compliance to stay in business. But that's all you wanna do be a legal compliance officer, you know? If you wanna take something that's new and make it work, then if you're a lawyer, you've gotta be the kind of guy who's comfortable with making stuff up and innovating. And then things different come along. Okay, so now we got AI tools. Well, AI tools help lawyers, right? And so it's gonna be, all right, that's cool. Let's use this tool. If you're a software programmer, probably good for being a lawyer. If you're a math person, really good for being a lawyer because you'll understand logic, you know? Now logic's gonna fall away in the ninth inning, but at least it helps you bring, you know, a good kind of clarity of thought. So I think it's, you know, if you go to law school, it changes how you think. It really does, it's a whole new, and that's basically what law school does is, I mean, they teach you law, but they teach you how to think differently. And that's when you talk to a lawyer, you know, what do we do? We go, okay, what do we wanna do? What could go wrong? What are we gonna do when it goes wrong? Yeah, okay. Then it's gonna go wrong again. So what do we do when it goes wrong again? Then it's gonna get weird. So how do we wanna put it back in the box? That's stage three. And so, you know, when I approach a thing, I think about this five steps ahead. And then I talk about it with my client. And then I don't go start with the fifth step. I start with the first step when I'm doing this, but I have an idea of where I wanna end up. So I think if you're a lawyer, you've gotta have an internal path. And it's gotta be grounded in, you know, science and tech. And you've gotta open your mind because five years ago you couldn't do something and now you have a tool where you can do it. So if you're not willing to stick up with the tech and the different practices and really understand data management, data quality and data governance, you know, this is just some weirdo black box to you and you're not gonna be any good to anybody. Yeah, okay. Makes sense, makes a lot of sense, that's great advice. Bill, any, so if somebody wanted to get in touch with you and solicit your services, how would they do that? So I'm also uncomfortable about being a little bit of an advertising guy, but this is how you do it. You look me up on LinkedIn. So it's William Tannenbaum and it's Tannenbaum. It's a single N, it's T-A-N-E-N-B-A-U-M. My grandfather couldn't afford three Ns when he went to Ellis Island, so there were only two. And then Moses and Singer is about like what it sounds. It's M-O-S-E-S, like Moses from the Bible. And then Singer, S-I-N-G-E-R, like Broadway. So our firm is Moses and Singer. And then you can go to the Moses and Singer website, look up people, and you'll see my picture and you'll see my bio and you'll see my email address, which is WTannenbaum at MosesandSinger.com, all one word. And that has a double S, no double N and Tannenbaum, but double S and Moses Singer. And I'd be delighted to talk to anybody, off the clock, so to speak and kind of find out what your issues are, give you some guidance and then you go away because we're not mercenary. It's not all about the money for us. It's about the intellectual engagement and learning from people. So I invite calls and it's like decision rights. Few of you on decision rights, let me know because I wanna learn from you. If you think I'm wrong and the data is technology, I'd love to have that discussion. And if you want some advice, I would love to give it to you. And what do you get? You get a lawyer who's really enthusiastic about this and wants to learn. And some things you know, some things you know you don't know. Some things you think you know, but you don't know them. But usually we know what we don't know. So that's how we know to ask questions. And I know this about you, Bill, as I have seen you at our conferences, giving talks, presentations and really not working with a lot of people and really engaged. I know you have a lot of passion around this and I think that came through today as well. So thank you for what you do. Well, thank you for the time. I really enjoyed talking with you and everybody else at the conferences. I really like going to the conferences. And I like the fact that they're not legal conferences. I like that it's real world stuff. And I really like that. Oh, if you see me in a conference, come on up. We'll have a chat. We'll have a sidebar. Well, Bill, thank you so much for taking the time to chat with us today. And to all of our listeners out there, if you'd like to keep up to date on the latest podcasts and the latest in data management education, you may go to dataversity.net forward slash subscribe. Until next time. Thank you for listening to Dataversity Talks brought to you by Dataversity. Subscribe to our newsletter for podcast updates and information about our free educational articles, blogs and webinars at dataversity.net forward slash subscribe.