 Hello and welcome to Dataversity 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 plus anything awesome founder Dr. Peter Akin about his career in data. Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager 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 talk with people who helped make those careers a little 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 Dataversity partner Dr. Peter Akin and normally this is where a podcast host would read a short bio of the guests but in this podcast your bio is what we're here to talk about. Peter, hello and welcome. Yeah and it's always so good to hear your voices we've been doing for these past 11 years of working together. It's true yeah we've been producing a webinar for 11 years. Can you believe it? Yep and by the way the woman who had that idea was Jeanne Loughlin and she has gone on to be super successful in her ideas thing but always want to thank her for getting it started and you guys of course. Well thank you yeah DataEd online a monthly webinar series we do the first Tuesday of each month on various things. It's always a blast and obviously successful enough to keep doing it right. I just it's fun. So let's talk about you. Let's talk about your career in data management. So what's your current job title or should I say appropriately job titles? You have a few and what do you do at your responsibilities under those jobs? Well I have to start out and say that I've been doing Commonwealth University since 1977 and because I'm tenured there they get absolute first dibs on everything including the fact that they own all the intellectual property in my head all the thoughts that I have part of the deal that we do with that particular piece. I became a professor there in 1993 and I did have a career before that which was a lot of fun. In fact I was one of the first students to go through the university without typing punch cards and I just happened to have them right here in case anybody doesn't know that this is how we used to do computing in the old days and got to learn at the hands of the masters in that process as well but it was quite a thing in those days because they didn't even lock computer rooms. You know we were just allowed sort of on the fourth floor at what is now called Harris Hall at DCU. Is that because they were too big to steal? Well nobody was supposed to steal a computer you're right. Although just quick deviation you remember when beamers came out the overhead projectors we lost one of those at the university at one point and found out one of the fraternities had grabbed it to go watch a football game and they were going to put it back so it was an old school but yeah it was the the computers in those days you would run these stacks of cards through them and it would do things and I found I liked that in fact my advisor said to me well I was going to become a lawyer I thought that was where I was headed and so my advisor at that point sat down and said look Peter let's do a little bit of math there are currently 200 million Americans and there are two million lawyers so you can do the math that's one lawyer for every 100 Americans probably none and this was in the days when lawyers were driving taxi cabs in New York City what a shame obviously things change and careers go up and down as far as all that goes but she said actually you're one of fewer than 200,000 people in the entire world that understands computers at this point in time and you're already there so I would think that would be a much better career path I you know had to have that sort of hit over the head with a two by four to okay yes thank you very much you know it's eternally grateful and it's been a great relationship with the university ever since and I've tried to give back to you know what the students have gotten because I got a terrific education out of the process and I've had a fabulous career I'm super happy to be able to document a little bit of it and sort of can benefit others so what is it you're currently teaching at the university I tend to teach graduate level courses that are projects and the real challenge I think that we have in the university is how to make it relevant BCU calls it experiential learning and my classes are all focused on the process of actually doing things just to take one example data scientists in today's environment are often sort of complained about because they don't have enough interest in learning the whole scope of the project they just want to focus on the the fun parts which is the algorithm I can remember going into classes and students ago you know when can we start the clustering right and it's like yeah I don't really get interested in the question before you start doing the clusters although there is obviously wisdom going to back the other direction but letting them see what the rest of this is and we tend to work with a real-life customer if you will it's typically a state agency but they get to work through these projects and I'm fortunate that I teach two or three a semester and it worked very nicely and hopefully the clients are enjoying the process they tend to keep coming back and working with them they tend to report when they go out of here that those are some of the more important classes that they've taken although it certainly doesn't seem like it at the time because you're in the middle of things as you are Shannon with everything you keep running right now yeah so you're a professor at the university and so but and you also have a second job in career you own a your own company that's with the university so they are co-owners in anything awesome and it's specifically again designed to focus on capitalizing the intellectual property we have so data blueprint was also co-owned by the university and it contributed back into the university so I like to introduce myself as a professor with a positive cash flow kind of not what most people think about from professor perspectives but you know it tends to work in my case I don't mean that I want money but it's nice to have resources that you can use to help other people and that that you know again gets to another hat which is the president of dama I've been associated with that organization for decades and you know the goal of what we do is to make this process easier I was talking to somebody at our last conference Shannon we were last in person back in December and and this individual was working for a specific type of state agency you know we'll just say it's a health department or something and all of a sudden you have this you know insight which isn't too insightful but there are 50 state agencies that are all trying to learn about data governance at the same time and you know probably a good percentage of them were at the event that you threw in order to do that but at the same time what a shame that they should have to go through and learn this individually and then you can take that step by step further which is every knowledge worker in your organization currently is has learned data on their own and they've probably not learned it optimally and so that's really sort of where I'm focusing what years I have to be able to contribute at this point but you know that's that's the kind of thing that's going on in terms of let's just say the danger to the citizenry right sure so okay so you also threw in a third title in there which is president of dama so tell me a little about dama and what your role is there dama is going through a transition as all organizations do but this is a great one for dama to experience we've operated more as a club because we had dozens of constituents as in the chapters of dama and you know tried to work things we don't really have a tight confederation we have what would be called a loose confederation and we've moved in the past couple years to an organization that has to focus more on service and so you know literally we're looking at things like help desk cues and things like that and as the organization matures and grows up and tries to do the kinds of things that you need to do in order to survey a user membership base that's measured in thousands of of really anxious data professionals who want to be part of this and what we want to do is create resources so they can can stop doing it individually by themselves it's all you know rising tide list all boats however you want to think about it but it's it's that grand of an idea so you're part of dama international.org it's a non-profit right and and again just reiterate it's part is to help data professionals uh and you're correct yeah you're working to standardize some of the things right around surrounding data well internally in terms of operations yes we we're changing from an organization that was very clubby in nature to an organization that has to be service driven and so that's what we're trying to do is strengthen our internal systems make sure that when people ask us questions we can respond right away that we can be a good business partner and that we can be of good value to our membership. I love it so you mentioned you wanted to be a lawyer when you grew up or when you initially started in your college yeah I mean is that what you always wanted to be so I mean you know what made you switch I mean I mean again you mentioned that so lawyers are a dime a dozen and was that it or you know and you just have this passion discovered this passion how did you really get into that passion and what were the steps that just transitioned you into that and you're right to get back there because I told you how I got into computers but the data part of it was a little bit more fun so I had a professor named Al Davis there's actually a billion of them out there but this was a very special Al Davis and he gave us a whole bunch of papers as a graduate student exercise and he hands out these papers and the papers that are going into an journal and he says I want you to evaluate them and decide whether or not you're going to accept them for publication and you have four choices except except with revisions send back a bunch of suggestions or reject it outright and the graduate students around the table and we got together of course his grad students doing what do you think about these things we all kind of like you know this this is not looking good we don't like what we're seeing here but they're all his friends you know so of course he did the the usual correct thing which is to set us around the table and then surprise us and say okay here's a piece of paper you tell me right down how you're voting on these things and he got back and we were all actually we said you know we're forced to do it we'll vote to reject him we'll all you know that's our pact among us the suicide pact I guess it was and he pulled all the stuff together said you voted to reject all the papers and I don't have done exactly the same thing it's a bunch of crap right okay that's the bullet on that one but what he was describing Shannon was the fact that when you look at requirements doing things more at the beginning of the project to help the overall architecture and context of the project we'll do more to save money over the long run than anything else we figured out what to do so that focused my attention from all of it to the requirements portion of it and said what we need to do is understand what we're trying to do because if we ask the wrong question we will definitely get the wrong answer and remember it at this point still even today in 2022 is about as good as one in three one in three it project succeeds with full functionality on the schedule as promised within the cost range originally specified my dentist was that bad I'd get the heck out of town right find somebody else to to work around the sensitive anyway yeah no no way right and yet what can we do while we focus more on the requirements now there's a very simple economic argument if you do something that costs a penny during that requirements process and correct it for a penny turns out the cost of fixing that during design which is the next stage down the line again you plan then you make a plan for how you're going to implement the original concept and then you do it you know it's the overall process this gives the opportunity where you say hey what is it that's going to cost 50 cents not one penny 50 cents to do it there so obviously it makes sense there well if you go to the extension of that where you implement the system it's 2000 times more expensive and so more we can work that into our context and consciousness and it's not part of you know of undergraduate education you know would you go into an ERP situation if you knew it was going to type tens of millions of dollars into investment funds in maintenance of systems that really deliver questionable value when you look at all the rest of the things that you have to do to get them to work around that I digress a bit that's requirements I like I like digression the next thing that happened was what is then the most part that you can contribute the most to that you can make a contribution this is how you're trained as a graduate student anyway the world's a big place you want to try and solve some problems around it and so looking at the data requirements it turns out that they are the most objective the most testable the most quantifiable the most machine learning friendly you know you can add all sorts of other characteristics about them that describe them as this really unique asset and so that's where I decided to get into data was to look at it from that perspective and ever since then I've enjoyed helping organizations people and things do interesting things with those and I was fortunate that the Department of Defense where I worked for about 10 years taught me in one form or another to make a decent presentation that some people like to listen to and so in that sense I can tell those stories over and over again you have made a very good effort to allow me a platform to tell those stories and I hope other people find those stories helpful in terms of the same sense but yeah that's a sort of a nutshell story of data is the part we can make the biggest change on and if I sound very Steve Jobsian like that yeah yeah it's a great I love it no it's great so so you found it in your graduate program so what's your what's your bachelor's in administration of management and information systems double major cool and then so what are what are the additional degrees what's your master's in doctorate I did a master in those days it was a master's in business with a concentration in information systems now we have a full master's in information systems but again I eat the dog food if you will I did two vcu degrees there and was very happy with them and was fat and happy sitting around at the vcu computer center where I was working full time at that point and I was in charge of their online services and databases and telecommunication systems and all that and you know things were great and my master's program advisor Dr. John Sutherland came into me one day and said oh by the way Peter I signed you up for a phd program the other day I hope you don't mind seriously that was my attitude too I just you're kidding right he's like no you're I can see you're having a lot of fun you need to go out and learn how to have some real fun and I will thank him to this day for that particular set of inspiration so true to his word he had sent a note up to Dean Andy Sage at the school of information technology and engineering at George Mason and said hey you should check out this guy and I actually showed up for work about two months later first time I've ever given two months notice of a job wow probably a bad idea I wouldn't advise it you know this when you you get blamed for everything then that goes wrong well turns out that's a time-based function so they forget like I'm still here at the table right you're blaming me for stuff and I'm right here we can fix this right now yeah it was a truly interesting experience and Mason was a tiny school in those days and it's grown like vcu has done a great job up there and I met a lot of friends and again got me into a totally different career space so so let's so talk to me about that so what was was that first job you've discovered you love data and in college and so you know what job did you get to use it it's interesting when you're doing your graduate degree if you're a full-time employee by the university as I was and I was very fortunate to be you know scholarships and other things that they threw at me to to keep me around and I had no problem I was totally dedicated to the program once I get started with it but you end up looking at this thing as a business in this type of a school and I don't mean to say it's overly bad it was again an engineering school there is a very fine business school at George Mason as well but this was not that but nevertheless I was at that point making fairly good salary as a director of the hypermedia laboratories ever heard the word hypermedia uh not in the wild no it doesn't mean anything it's like multimedia but it sounds cool right so we were doing you know that kind of read and we had a department of um transportation I believe grant that we were working on again with clients working on I was working on the data problems I was director of the laboratory I was responsible for making sure the funding was secure and everything like that and again fat and happy at that George Mason very very happy there and a colleague of mine who I knew from other days who worked at the defense department called me up and said hey if you go to this high school on this particular date we have an open recruiting session if you're the right kind of information engineer which I know you are Peter wink wink nudge nudge you know we can get you into the defense department here and so I ended up coming on board with them just a few months later after that and I worked for something called the defense information systems agency and I had a wonderful title I was the DOD reverse engineering program manager wow what what does that mean I had to find out really Shannon this you can't make this stuff up I worked the wonderful gentleman named Russ Richard to really understood the program and reverse engineering as part of reengineering and if you think about reengineering what you're doing is you're understanding the things that are good and bad about the existing system bringing forward that knowledge into the new system and reusing it I know that sounds like a very basic idea but you'd be amazed how many times it does not occur in fact I've won lawsuits around that because as a supporting character in the process I don't see people but what happened is is that organizations know they should do this and they don't because it sounds like it's more work than it's worth whereas again I just said the concentration on that stuff early on at the requirement stage is where you're going to save the most money so there's both an economic case and a risk case that you can make in terms of looking at that and I learned that from you know hundreds of hours of DOD projects as well as other projects that I've worked on at the university as well since so you said how long were you at the DOD I was full time for about two years then I came back to VCU I just happened to see a position open Dr. Gene Gayson who was another one of my mentors from previously ended up having a position in the department that she opened and I joke Jim Wynn hired me the first time in 1978 but he hired me the second time in 1993 so a long long-term relationship with Jim and a very good friend I like it so when did you start branching out into consulting and to education and involving it so let's see if I can phrase this in a way that will go down easy for everybody because I'm gonna be blunt in some of my speak and I don't want to do that so what had happened was at the defense department after I finished the series of reverse engineering projects they literally ordered me to write a book they said you must write this down I said well are you allowed to ask that you know can you really make somebody write a book because I'd never written a book before and they said little boy do you want to ask that question twice and see what happens the second time you ask it okay I'll get started on writing book so I went away hyped up and came up with a data reverse engineering if anybody's really interested in it the better version is a condensed version in the IBM systems journal which is available on my website anythingawesome.com there we go a little plug-in for that one Key though is to understand that contextually we do have some techniques that are more useful than others so you're asking how that transformed though so I've got this book out called Data Reverse Engineering and why anybody would be interested in that as the subject is really questionable and if they hadn't ordered me to write it I don't think I would have written it perhaps as a book and as I said that I think that I did a better job the second time coming back and writing it more succinctly in the IBM systems journal article also there's a key metadata model in there that was not in the book that is in the IBM system journal for those of you that are interested in the technical aspects of it. I'm at my office at the university one day and we're always encouraged as I mentioned before the experiential learning the idea of connecting these students so understanding VCU student base is a good important contextually many of these students are first in their generation first in their family to go to school so they don't have good role models and things like that to rely on so there's a fair amount of guardrails that we put up in the program to make sure that they're successful as they possibly can be and part of that is that when I was 14 I worked every afternoon I work full time from the age of 16 until I guess now I've had some times off perhaps but mainly straight through these kids I'm encountering in the undergraduate class have never had a job in their life they don't break leaves they don't understand the process of showing up importantly for you know interesting sorry not interesting but you know even hygiene right we've had issues with that so yes you got to show up every day you've got to be washed you've got to you know blah blah blah blah and again then you get paid right that's how it works right good work or we'll get that process through for you so we were doing lots and lots of that and I get a call in my office at the university and it's hi my name is uh such-and-such and I'm the managing director of Deutsche Bank okay what can I do for you sir they said well I think you've done something that we need to do okay what is that he said well here's the deal and this is high-level managing director who's describing to me the nine million lines of legacy cobalt code that they have in their very valuable asset something called um db trader which was the high-end trading system that they had there were three characteristics of this uh one it was the only online system so they could create in real time it wasn't match to it was multi-currency so they didn't have to do currency conversions external to the system and three it was table driven which meant the architecture could add new thin tech products that we would call now and these guys were the pioneers in that thin tech area uh in those days because they had a really good system that worked really really well but it was on mainframes that were being sunsetted yes we had problems with that even back in the 90s uh this was on Wang cobalt you may have heard of a Wang computer again I was very familiar with them because vcu purchased the university professors that were associated with the Wang university and bought their master's program and brought them in again very good deal wonderful people that we were able to learn from in that process here's Deutsche Bank with this massive significant asset in terms of financial positioning and they can't find the IBM saying we're not going to emulate the the Wang cobalt anymore on this new platform here it's too old just as Apple sunsets their products so this is going to sound nuts Shannon but the relationship that we developed with Deutsche Bank there over a three-year period was about five million dollars and we were flying groups of students up and back to New York City on a regular basis we had put students up in nyu's overflow dorms because they had extra space and capacity these were deals that we were able to make between Deutsche Bank and vcu and a very successful collaboration and a couple hundred students went through that process and benefited in immense ways most of them of course sucked right up under wall street because we were charging wall street at that point in time 25 bucks an hour whereas we were charging i'm sorry i said they're paying the students 25 bucks an hour which in the 90s was a good salary and uh we're charging the bank 50 bucks an hour and our overhead rate at vcu was eight percent again i'm saying this on the record because these are somebody's probably going to come back and want to see these these things so when i was doing that in the university days they said okay go down to uh what's called office of sponsored research it's over in the basement of sanger hall which is in our health systems campus and find a guy there named chuck termside and negotiate a rate with him and i mean an appointment went down to see chuck he looked at me across the desk and said you okay with eight percent and i said yep that was the end of the conversation from then on i was charged eight percent overhead in university area that was fine and that was for applied research we had a lot of papers a lot of books that came out of that particular line of research even though we also happened to make some money and give some students some good experiences in the process we went a little further than that uh but what happened at the end of that process was about 1998 if i recall vcu like many of the other universities realized that they could take the overhead rate from a certain fixed level and make it much higher and we went from literally eight to currently we are 58 percent overhead in today's rate which meant that the amount of service that any of our clients were getting through that previous mechanism had been halved in that process so they looked around and said do you have intellectual property that you can help us declare and get started the company that you're going to get started which is data blueprint i think we came up with that about the same time you guys came up with the diversity so we're kind of on the cutting edge there as far as getting that and so again with vcu's help we declared some intellectual property to them i say we the company basically they own most of the thoughts out of the company as well we own uh trademarks in particular in the area of something called the data doctrine so we're we've published two pieces in that area what it means to be part of the data doctrine that gets to a different issue which we're trying to work with as a community when somebody wants to be more data-centric what does that mean everybody would certainly say yes we would like to be more data-centric now what right so we put some things in place and we can come back to those if you decide that's an interesting area to go or hold a webinar on it or something wrong lots of webinar material here you know hey so that brings us to a good point though um and so what is your definition of data and and how do you work with it today and how do you teach it so there's a very nice definition that we used in the defense department by a fellow named dan appleton and i've not had anybody disagree with this so far uh the the key is that we can put a fact out there the number 42 some random fact now you may know 42 as as jackie robinson's jersey you may know 42 as life the meaning of the universe uh you may know 42 as uh my age i'm not that's not right can't be that old anyway uh 28 years ago maybe 18 can't be isn't that funny how we've been a few years ago i haven't aged past 40 so you know whatever it makes it kind of gradually and now i've looked at my train of thought completely Shannon what was the definition of data and how do you work with yeah 42 right we'll just put that out there as a random fact and you've seen already that we can attach different meanings to that specific fact reasonably a datum which is the proper way to say it although nobody does is to say that that 42 is the meaning of life the universe and everything end of story that association between that fact and that meaning is data now the next question people ask is so what's the difference between data and information turned out information we can do the same association here we have a fact and a meaning we can take that datum and put another piece up that says it becomes a piece of information when somebody asks for it so once again there's a tangible ask in there hey Shannon how many of these happened or what happened with this or whatever there's a tangible specific request that makes it information that also means right away that if you're trying to manage your data and your information separately you're chasing your tail around in circles because it's too much work to try and keep them separated because obviously it's not just a thing is it a fact or a data or an information but at what point in time is it a factor in information and that makes it a real tough problem to solve all right we can go one step further than that because everybody said great i got information now i need to get smart about it so that third layer that we say there is using that information strategically what is it that we found that the organization can do strategically with that information in order to leverage it for example and this is my favorite example currently today you know how bad things are with the poor airlines even this week there've been thousands of flights canceled it's just got to be you know complete awful thing to be working in that and i'm saying is i'm getting ready to make a trip right so uh you know that's the way it works but um if we look at what's happening in the airline industry they're not valued very much basically uh american airlines which is one of the big ones is six billion i think and the united is nine billion uh valued as the marketplace valued however both companies have been offered deals in the twenty billion dollar range to obtain their frequent flyer data that they have in there the difference between those aside from being several billion dollars in difference is the difference in knowing how to exploit that data knowing what to use strategically and whatnot it would be the kind of thing where delta airlines might say to somebody hey we haven't heard from you for a while is there something we can do to help get you into this particular piece or we saw that you were on travel velocity the other day because they know when you're out there right uh you know why did you choose this flight instead of that flight uh in order to give us a particular answer again much more than they could do but in twenty billion dollars in both airlines now probably it's not all twenty billion but it's certainly not nothing and so the question is how do we go about valuing that particular component getting people to look at it as a strategic asset that we can leverage you know and to understanding data further and kind of what your belt your passion around and get this question all the time in webinars you know what is the ROI of data and i know we can do a whole webinar on that topic but you know how do you define the value of data i mean you're talking about it in terms of billions of dollars how does it translate to that so it does and that's the really interesting part is that most people haven't done the work to do that translation and it turns out it's not all that hard first of all let's sort of shout out to Doug Laney for helping to popularize the concept around that in his infonomics book it's a wonderful addition to the discussion in there and he's done a really really fabulous job with that the key there is to realize that the leverage in data is in finding what Tom Redmond calls hidden data factories now it's really not necessarily a great name but if you think about it it's where department a is working on something and they hand it off to department b and the first thing department b does is change this back to this because they always set that setting wrong or forget to do something like that and they can't make it go back to the first step in order to do that and those are all over your organization unfortunately we use the phrase dying by a death of a thousand cuts right well probably there's nobody's dying right so you're unfortunately unnecessarily bleeding from a lot of places and if you reduce that it would be 20 to 40 percent of it budgets for most organizations 20 to 40 percent now one thing i wouldn't want to be in today's environment is a cio their job is hard enough but the one thing that they can count on as a cio is that their budget next year will be much less than the budget this year because everybody's got it in their mind that going to the cloud is going to save people money done carefully it can but most organizations just as they don't deliver on time within budget within schedule functionality sorry within the cost third one they also again don't do a good job when they move to the cloud and we can get into another whole issue around that but it's a wonderful inflection point and a very good place to look and just as a simple piece of guidance i would never put any data in the cloud that i didn't at least have a good idea of some of its quality characteristics uh some people think that means it has to be of high quality i don't think that's the case it has to be of known quality and that's a big difference in that but what we do is we tend to just say it's almost free so we'll stick it out there and then die when our next amazon web service bill comes and hits us so cio in this context is chief information officer absolutely sorry if i wasn't no that's another piece that i'll just put on the record here which is kind of fun um i've been involved in development two pieces of federal legislation and the first one was the law that came about that called into action the cio's it's not that they didn't exist before but the federal government made it a mandate that somebody needed to be in charge of it and so it's like one throat to choke kind of thing and i was consulted on that law and i'm very happy to contribute to it again it hasn't really stemmed it cost of increased but it has at least given us some it's the law so we should do it it's a good thing that's a good place to be second one is this new thing called the federal evidence-based process uh policy act and fifa has done three things in particular one it has made all data in the government open by default with obviously exceptions for sensitive data two it's mandated the use of a chief information officer excuse me i said that wrong a chief data officer that is not the chief information officer right i think that's a very interesting test so when businesses do that i say well one third of the economy is now working with that as an experiment do you want to sit back and watch or do you want to participate that's a reasonable question organizations can decide how much appetite they'd like to have for that our third piece of the fifa law is though is really interesting it's now against the law for any lawmaker uh in the federal government to decide and again i'll just make an example here of the department of education that says school type x is better than school type y whatever those are i don't know and i'm not you know that's not part of the discussion but if you're going to make a policy judgment on that the law is now that you must specify in advance that we're going to use this pile of data from here and this pile of data from here and we're going to use this model all of those public in the same way that open data is public and then we're going to tell you how we're going to apply the algorithm and then everybody can apply the same algorithm uh in the same manner which will lead to open transparency around this so that people can't uh do this so you know nobody's been arrested for not obeying the cio law probably nobody will be arrested for this one but we do at least have the interesting characteristic that the penalties are higher than hippa oh yeah yeah okay that's serious again we've got way off track here but fascinating stuff no it's good it there's a really nice um point in there you know you mentioned cdo's chief data officers who are relatively what are relatively new on scene right it's a fairly new job title that um has been that's still growing and then yeah and there's still a lot of questions about who they report to and where they live you know um we see a lot of other jobs um emerging uh you know in around data we're seeing an increase in data modeling jobs and data architecture so in terms of that do you see the importance of data management and the number of jobs uh working uh in data increasing or decreasing over the next 10 years and why do you see that this evolution continuing or is it going to change what am i seeing good good question so one of the things that that's been really interesting for me again just a slight digression as we move into the the answer there is that instead of just staying at vcu i've been really fortunate to have been invited to spend a total of eight immersions as i call them uh so i i spent a number of years working with nokia in finland and i was it's a tremendous experience with doge bank in frankfurt doing uh you know really on-site work working with wells fargo for a period of three years and really doing some very exciting work so i think i'm actually qualified to answer your question here shannon and that is that there's a big gap right now between organizations that are doing this well who look at this now i'll take it back to another story that i think john botega will recognize as well i know you'd speak with him occasionally and cross pass with it that work group as well great great folks over there john wrote the first time he read my monetizing book he said this is nuts this is what my cio's think about this is how we do this this is you know if you did anything else you'd not be doing your job and i said yes john but let's look at who your your peers are the the chiefs of the largest most sophisticated banking systems in the entire world of course you guys have it and they do a great job and they are very generous with their time and energy to show the rest of us some of the things that they've learned in the in the process of that but the vast majority of organizations are just figuring out what data is much less that they need a data leader of some sort to be in charge of of that particular function and so as you mentioned there's not a lot of them that are out there but i have been fortunate to work with a number of organizations and really up to to in depth what we're seeing is there's tremendous growth in the rest of the world and just in the same way that we've seen other parts of the world as they've opened up they've skipped the copper wire uh shannon one of the things you and i joke about all the time is my poor internet connection and thank you whomever for for keeping this uh up and running for us for this this time it's almost a miracle the other thing is my my power supply went off weirdly and just started beeping for some reason i have no idea why uh on that but yeah so everything's working right at the moment but i'm at the other end of a twisted pair and so my twisted pair copper is a real problem it's an impediment to the entire community that i live in here 20 miles north of richmond virginia um it just means that this part is going to be internet poor for a long time i get two megabits to the desktop and 0.3 megabits back up yes uh again you have questions about whether that worked for you just go to a website called fast.com and see what it tells you your numbers are uh you'll be amazed a lot more than two and 0.3 let's just say like that uh so that process these these immersions though have shown me that we're where the growth is expanding is way beyond most of our peripheral visions here and i i mean that shannon you're seeing growth overseas in terms of the webinars and the marketing that you're doing as well we're experiencing exactly the same thing and they are coming along and they're saying you know we could lay copper wires to everybody's house and do all that sort of thing but if we just put it on the air it'd be a lot cheaper and faster we just turn it on you know just like that so they're getting that and they're jumping and they're not making some of the mistakes that we've made on the way up and and we're seeing a lot more of that growth out there and i think it's going to be up to us to help our organization to try to realize the true expense of what happens there and that's you know gets to this this other component of being able to put a dollar amount on some set of values again i can tell you lots of stories around that but having those examples and knowing that other people could do it the goal is to inspire and to try to say can you find these patterns in your organization just the same way that lent silvers and created all those data modeling patterns are you looking to learn how to implement a successful analytics strategy join us on october 19th for six live expert led sessions at enterprise analytics online register for free at eanalyticsonline.com well you know to that point you know especially as a professor you know do you see universities you know changing what they're teaching how they're teaching it i know you have and you know are you how do you advise people to get started not just that the executive allow where where do they go from there you know how to become a data architect or a data scientist or you know is that a good career choice so you have to produce it before it gets you that's a barrier right i mean that's just keeping away a lot of people from that so yeah because having that introduced as an elective at the undergraduate level we have i could tell you what am i more inspiring i was on the receiving end of inspiration i don't know what that is inspired moments is that the way you say it correctly but we were going through and what we do in my my in-person class that met this semester was that we went through a whole series of a design exercise and we got to the part where somebody was actually laying out the interface and this individual came to class and just went i was so excited doing this assignment i had the you could just tell she was lit up and just you know going to go off in a career in that general direction we will find those if we look for them and make ways so that they can become part of the conversation but if we don't mention it at all which is what our current situation is they discover us after about 10 or 15 years in it and that's a lot of wasted time that we really need to focus on trying to eliminate that big big gap that we have in there again i'm mixing damas stuff up with the academic stuff but i see it as all related yeah absolutely you know and what is your advice to somebody who's just getting and discovering you know data it's a career in data management whether it be an analyst or or a data architect or what not you know what's their your best advice and how do you keep up to date and the latest and greatest and any words of wisdom and how to make a successful career and really learn about data it has to do let me tell a data science story this is not to knock data science because we saw exactly the same pattern occur if you remember we went through the MDM work you know you always have focused on the whole enchilada right at the diversity but there was one group that got in and said MDM is going to solve all of our problems and we saw exactly the same pattern which is that people became allied to the technology and not concerned with the business context here so i'm telling the story about data science it's not to pick on data scientists or anything along those lines but it's a combination of two things that occurred one an individual went and ran into the the CEO of the company at one point just in a casual conversation hi shannon we're in same elevator together so it's loud right you know i mentioned to the fact that we had gotten to an 86 solution on one of the things that we were making more progress towards and the reaction was very different now i'm already a very pink person here i'm going to go look just to you know make steam coming out of my ears and all that sort of thing young we never do anything at this organization less than 110 percent and i do not want to hear from you until we have got you know just completely missed the picture right it's just a few ships passing on the night miscommunication sorry it was corrected no problem but what was interesting about it was that on further investigation the organization discovered that even the 86 solution was too much that the the data scientists had understood the business context in which the problem was attempting to be solved they would have solved it at a 70 solution two years prior and that's a lot of time which translates into a lot of resources and money on this so the key is it doesn't matter what aspect of technology that you're interested in what aspect of data that you're interested in what you've got to do is be able to relate that to some component of the business value and it's not that hard to do if you just start practicing it's kind of like writing in a journal or taking notes or other things that we do i'm trying to think of a musical analogy Shannon because you and i are both musicians and so we have you know we understand the value of practice from the perspective and that there's some things that can't be learned without practice and that that goes for enterprise architecture and much of this data stuff as well you can read it in a book and you will understand step one step two step three and then you discover that somebody forgot to change the date field here to here and now you don't have access to your you know tool that you used to use so you're going to have to try to do it with excel oh my goodness again these are the things that take our knowledge workers in general much broader than just data people knowledge workers in general spend 80 percent of their time trying to do their job and 20 percent of the time actually doing their job so they're creating work products that are redundant they're doing all kinds of things that are simply not helpful they're searching for information again they're asking their their colleagues for it and this is just a terrible waste of our resources if you can remember Shannon i know you lived during this period so you might not have been aware of it but they kept talking about irrationally over exuberance and trying to figure out why the stock market was going up as it was during the clinton years and the answer they think after reflection is that technology helped us become more productive and that was an unknown technology dividend that kind of boosted our economy and helped and that's great thing nope i'm not complaining about it at all this is the next area that can do that kind of workforce when you look at what we're asking our knowledge workers to do again i just on boarded for my eighth um immersion here and the onboarding process was beautifully done these guys have been practicing they really it was a nice onboarding process i showed up for places where they've spelled my name wrong and because it's a key field it sticks with you for the entire duration of your you know i don't really care when you spell wrong it's just kind of awkward when people are looking for you and trying to spell your name oh we've all had bad data experiences for sure but uh do you have to be interested in technology first i don't think yeah i don't think so uh again i i think what we've noticed over the years is that there's a high correlation between musicians and mathematicians now you don't have to be mathematical either but if you are mathematical you're likely to do this but if you're a musician you're likely to be sympathetic to it and i think that there's another class of workers that also stand out as very useful over the years that i've been doing this in really four decades at this point um but it's restaurant workers and the the key there is that if you're working in a business you're typically either a service business or a product business it's you know hard to change from one to the other uh in that sense and yet in the restaurant world you have to be good at both it's production in the back room and it's uh service out front and people who do that and understand that tend to understand systems and tend to make uh good so all of the things equal with two candidates i'll take one that's worked in a restaurant again that comes across well and all the rest of that uh in there and there's there's so many opportunities for people like that think about so right now Shannon our growth rate and data is going like this and our ability to analyze it is messing my hands up here totally much lower at a rate right i should put it like this is where i could rely on my power it's going this way and then our rate of an office and this way we're giving the the the students and trying to you know get back to what we were really focused on which is trying to help these people i mean yeah in some cases we have been teaching students incorrectly all right for example you know what a case tool is but if you were to ask a 30-year-old they wouldn't know what a case tool is it's gone from their vocabulary just like they don't anymore know how to sign their name on something because we haven't taught them script by the way an interesting part about the scripting if you remember back and you may not have had this so let me know but when i was there they gave us ruled paper very much like the bricks that you have behind us and they said it's too high you leave one between you're allowed to make a lower case letter that's half height and you have to touch the top of that thing and then through an uppercase letter you have to touch the top of that part of it and it gave you objective criteria literally guide rails that we can watch if we don't even tell people that data is a career option how are they going excited about it how are they going to know that it's a useful and people rely on it and it doesn't actually know that much about data either because they've all been taught exactly one course in database in data which is how to make a brand new database and if there's one skill we do not need more of on planet earth it is how to build a new database do we wonder when we have a problem with too many databases and the only thing we've taught people for the last 30 years is when you have a problem with data build a database now it doesn't matter what you call it the data lake or a data lake house or a warehouse or any of the other things it's still another pile of data and our goal should be simplicity rather than complexity in terms of what we're trying to do because the world is tough enough to get across with messages much less with data in terms of how people are going about it so do we need to start advocating uh high school do we need to start advocating earlier start uh a movement and letting people know that this is out here or you know do we just have people continue to fall into those roles and just and I you know I've had emails saying I just got this job as a title as a data or what do I do I'm looking about for my later literacy books I can wave the cover about uh on that in order to do that that really does get to sort of the heart of the matter because if we argue about it as an it problem we're missing most of the rest of the world which is where most of the action is it is important and things that we should do but we also need to focus on what I call the knowledge worker so what Todd and I did in the data literacy book Shannon was to to take and divide the world up into objective criteria and again it goes back to it's just clearly a passion of mine if you could make it something that everybody looks at and says you're on the fifth brick you know counting from the left or whatever it is you're doing everybody can agree on that that actual basis so of classes of people that are out there in the world there are close to eight billion human persons on the planet earth of those about two and a half billion with a b have a mobile device of one kind or another some sort of a rectangle or there and I still have an invisible iPhone this is really you do so that's a group of people that really does need some data education and I'm not advocating or silly enough to believe that we could go out there and take the mobile phones away from everybody so you're going to get them back when you can pass this test you know that the world doesn't work that way but we can have starting to have conversations with people what we call responsible adults who are responsible for these mobile data spreaders and we can also lock down their phones and I don't mean that in a bad way if you go out now as a young person you're more likely to get a call from somebody that you don't know than a call that somebody that you do get we of the more mature generation people don't bother as much although I did have an interesting experience just last night Shannon which you'll find interesting we got zoom bombed in a kind of official meeting yeah it was horrific just wow people actually waste their time doing that okay but you know anyway back to our topic here trying to understand where it is that we can make a difference and looking at these younger people coming yet long here I've been so fortunate in my career to be colleagues and friends with so many great individuals Clive Finkelstein who just passed away John Z who's unfortunately suffering a bit right at the moment as well so wish him well lots and lots and lots of other people and I'm sure you're going to capture them in this series which is just absolutely wonderful from that perspective hopefully we'll be able to know you're doing a careful job with curating this will become a resource for other folks to take a look at when they finally get around to saying okay now it's my turn to start doing this because there's nobody else left and I am the person who knows the most about data in our organization right at the moment which I should be data leader for that organization and not have to start doing something that's going to help our organization do more with what they're trying to do 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 DVTOX for 20% off your purchase let me ask this one more question here you know you mentioned your book and data literacy so first let me ask okay so you wrote you the DoDF for student rights your first book which is fascinating so now how many books have you written I know the answer to this but I'm gonna ask how many books have you written Peter Egan so 12 books at this point and I'm really pleased it doesn't obviously make me wealthy as far as that goes but I do get at least favorable feedback and most importantly corrective feedback from people which is how will I make this this entire process better I love it and so so data literacy why data literacy and you know this is also a fairly new term that's booming in the world of data management right so what is it and why is it emerging and so hot right now I'm a devotee of the concept called surveillance capitalism and if this is your first time hearing that word start googling it you'll see that Shoshana Zuboff up at Harvard really coined the term wrote a wonderful book on it which is a fairly weighty tomb but it's a wonderful book and the idea is simply this that there is a class of organizations out there that really want your Shannon's data and they want to make money off it now again we've been talking about how to make this stuff pay for itself and here's a group that's turned around and said just by walking around and being a mobile data spreader Shannon you are playing right into our hands and we can take that information that you have and monetize it and use it in ways to quote help you out through the process now let's be very clear about the motives on this which is so unfortunate their motive for this is to personalize the advertising that you don't listen to instead of just sending you random advertising that you don't listen to on this and the idea of course that once you start advertising that next leads you directly to behavior control and if you go to the Cambridge Analytica scan bill and see what's in there they were able to manipulate entire country's opinions by messing with their data and that's a very unfortunate thing that the experiment literally was seeing things that are coming through a newsfeed type of capability if we turn on certain switches and make them see happier or sadder type stories that will influence them to become more enraged and their enragement translates into dollars both in terms of advertising but also in terms of political action and can inflame political groups which we've seen of course plenty of evidence about that without getting on to weighty a topic there anything else you want to add well I do think that the key behind all of this that we're doing is that if we're wrong about data the data is the asset that you need to have that it is then somebody should kind of let us know and make a good business case and say it doesn't matter just do whatever you need to do throw your data in the cloud don't use standardization don't try and make a don't try and make a very good case for your efforts in this area and what I tell people is that nobody in today's environment questions the value of an HR organization right you just can't imagine most major corporations saying you know it's a cost-cutting time again I think we need to that we don't everybody's going to behave themselves we won't need lawyers anymore HR and by the way HR went through a transformation that I'm hoping our industry does as well which is that about 80 years ago and this is a story from John Laddly thank you John for giving me the opportunity to tell us but about 80 years ago HR was down at the workgroup level and workgroups were thought to be able to determine best what their workgroup needs were therefore the hiring should be local and that's how it was done and we observed over time with the data that that wasn't the case and that standardizing those practices would result in a fairer environment and and being able to even out and reduce risk and cost of the organization so nobody thinks today let's get rid of HR I think we're done with it and yet we look at data with that same longevity for the organization and and say gosh let's do this on a project basis and basically on a project by project basis with an IT shop that is only 33 accurate that's just asking a lot to happen with data that's why it hasn't happened in the current way and why we're saying there needs to be a change going forward so that we can do this I've working with two organizations right now that claim they are green fields let's start off with nothing else we get to put our own data we're going to design it right from the ground up and they've already made several mistakes that I keep pointing out to them and not not to say that you know we don't want to hold them up we understand the need to move quickly but the idea is there are certain things that you need to make sure you get right on this if you don't get it right we're going to be doing things like now again Shannon you're in uh Portland you've been on a 10-digit zip code now for many years right yep poor Richmond Virginia you're my little enclave here just went on 10 digits last year last last fall oh terrible thing what's the world coming to with 10 digits in a phone number you know it's because they didn't plan it out in the first place to get it what if we run out of telephone numbers oh that'll never happen well again jenny you have one number two numbers three numbers maybe if you have an ipad with a wireless card you might have four telephone numbers because that's what the system that they use it wasn't just that they were telephones it was telephone numbers that were used ran out on real quickly these problems are not going to go away they're going to continue to get worse and we're going to need a class of people that can do exactly what you and I've been talking about today and I'm so thankful that you provide an opportunity for folks to participate in these educational opportunities well peter thank you so much as always it's such a pleasure talking to you I really appreciate you taking time uh any any ways for people to connect with you that you want to give a shout out for and where they can find your books if you google me you'll you'll I'll be the first person at the top of google for you uh on my website anythingawesome.com I love it well thank you and thanks for taking the time again and thanks to all of our listeners out there and if you'd like to keep up to date on the latest podcast and the latest in data management education you may go to dataversity.net forward slash subscribe peter thank you so much 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