 Hello and welcome to Dataiversity 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 Data.world's principal scientist Juan Cicada and the vice president of product Tim Gasper about their careers in data. Hello and welcome my name is Shannon Kemp and I'm the chief digital officer of Data.iversity and this is my career in data. A Data.iversity Talks podcast dedicated to learning from those who have careers in data management to understand how they got there and to be talking with people who help to make those careers a little bit easier. To keep up to date in the latest in data management education go to Data.iversity.net forward slash subscribe. Today we are joined by two gentlemen from Data.world Tim Gasper and Juan Cicada and normally this is where a podcast host would read a short bio of the guests but in this podcast it's your bio that we're here to talk about so Tim and Juan hello and welcome. Hey thanks so much for having us we're so excited to be part of the show. Yes same thing this is great to be here. So excited you guys are here again you guys are always been so great in our webinars and you're so enthusiastic and so much energy I'm really excited to have you guys here to join us today and talk about how you got into data management. So let's start with that you know start with what's your current job title and what do you currently do Juan let's start with you. So my title is principal scientist at Data.world and I still ask myself what does that mean. So I and well first of all I say I carry two hats so I carry my I'm an academic I'm a scientist by training so I carry my academic scientific categories but also kind of my the business engineering hat so it's a combination of two things that I do at Data.world specifically I kind of spent time in all the different bank pillars of the company I was actually telling Tim yesterday like yesterday was a day that I spent a little bit of every day every piece of the day sales I started off the day at a sales call through some leads that came through me and I was helping out with an account executives on the sales call then I spent some time with marketing because I helped generate some content we write a lot of blog posts the podcast that we do is part of the marketing going off for some new things that we're that we're writing I spent time with customers so yesterday I had a one-on-one with one of our VP's of data lakes and one of our customers to understand hey we have monthly check-ins hey what are you doing how's it going what are you thinking about data what's up and I share also kind of where my thoughts are and see where we're how we can advance and then also on product engineering so I always like to have ways so how can we push our product to the next level and figure out all those things that I'm learning about hey we should be doing this new thing like how can we push Data.world as an enterprise data catalog for the modern data stack like maybe we can start doing these new types of things so that was an example yesterday of the four things and then even with my academic hat on I'm chairing the web conference in Austin next year so we had our big academic kind of organizing committee meeting that's kind of a day and if it were if it were when yesterday was Wednesday and if we're well we're in session with our podcast we would have even done our podcast so we're taking a summer break right now so that's what I do so it's like to summarize I'm a very gentle I have t-shaped so I do very general a lot of things and then I have my very specific t which is focusing on anything around knowledge graphs I love it and Tim I'm going to ask you the same question but let me back up a little bit too you know as one you were talking you know see both of you work for data.world which is as you mentioned data catalog so you're a it's a vendor per se so what is data.world before we get into Tim what your title is? Yeah sure so maybe I'll start off and Juan if you want to add so data.world is a company a software company focused on providing a data catalog and governance solution fully hosted in the cloud to help companies to find understand trust and access their data so a bunch of different companies work with us we recently not announced a leader in the Forester Quadrant for data catalogs for data ops and in general just helping companies really keep track of their data organize it and in addition to enterprise we also have a really huge open data community where millions of people are there working with you know open data sets around COVID around policing in America and various other things in that open data community so and lots of other stuff but those are some of the big things that data.world works on. I love it Juan anything you want to add to that? No Tim. He's got it down. So Tim and what is your title and what do you do? Yeah sure so I'm the VP of product at data.world so one of the most fun things I get to do is every Wednesday get to do our catalog and cocktails podcast with Juan so that's always our big highlight also joining the diversity webinars and things like that is always an absolute pleasure to work with you and others Shannon but also so at data.world I manage our product so our product strategy our roadmap our program management as well but what's also exciting about my role at data.world is I also run our data team so our data engineers our analytics engineers all reporting to me and so in that sense I don't get just to do sort of software product management but also data product data product management as well. All right so the big question here and the big build up so how did you get to be where you are today you know is this is this what do you want it to be when you grow up like where did you want to be when you when you were little is this how did and how did you get from there to here Juan you always wanted to work at a data catalog company right right that was a big dream that was a kid I said when I grew up I want to work at a data catalog there's no one I was joking to Juan the other day I was like I think I figured out what a data catalog was like four years ago so obviously now you know we're experts at different like it's it's different right it's not it's not what everyone thinks of and and even data in general is often not what people think about when when they're younger right like you're six years old you're like I want to be a data scientist not not usually right um so uh I don't know like Juan do you want to start us off and then maybe I'll give my kid answer is I wanted to be a police officer I think that was when I love it but so I come from I come from a family of academics uh so kind of science has always been kind of what I grew up um my my mom is more kind of on the human education side my father is on the hardware side so my father was a material is a material scientist they both work for IBM and stuff so I kind of grew up around IBM IBM means I've been moved so I was I was born in San Jose California when the Silicon Valley was about silicon so um so I grew around technology all the time so I got into computer science very early on but um I think it was it was when I started in my undergrad I was my parents are Colombian and I grew up in Colombia when I was like after the age of 10 I moved to Colombia and think I was around I was probably I don't know 18 19 a professor came to the university to give a talk on something called semantic web I think this was like 2004 yeah 2004 and I found that fascinating and what I found fascinating was the issue about semantics and that was I mean realized that oh you can you can say one word can mean multiple things right so the example that come up was like oh if you search on the web right think about early googled right 2004 is like you search for the web and you search for Paris Hilton so you are you thinking about the person are you thinking a place and you're thinking about a hotel hotel Hilton in Paris in which city Paris and so forth and that got me like oh there's all these things about semantics and knowledge and then they started reading a lot about ontologies and knowledge representation and and that's what really got me started I think so from early very early on I think they're the most second year in undergrad I was in Colombia doing this I got into semantics and doing getting to data and integrating data and stuff like that and then I transferred and I will move back to US and I did my undergrad I transferred my undergrad to UT Austin in computer science again what was funny was that a little kind of a little side thing happened was that I was into data doing software application stuff and I met some folks from Switzerland and we started kind of a company together I had an engineering team in Colombia the company was based out of Switzerland so I did that for a couple of years we're just doing a web 2.0 nice app for project management system I remember project management system for temporal staff companies uh company still exists it's actually doing pretty well um wow but uh at that point I was like so I had that entrepreneur spirit that comes from my from my from my mother but the science comes from my dad let's say more um and then I got into the I started was so excited about research I was doing undergraduate research I met a professor who became my advisor which is Dan Ranker and from there I said I like this research that I'm doing on semantics and what I was doing was understanding the relationship between relational databases and the semantic web technologies was around 2005 2006 now um and then I said hey I like this research I like Austin I like the professor I'm working with I want to keep doing this stuff so the an answer was go do you go apply for a phd so I I only applied for a phd at UT because I wanted to do all these things and I said if I don't get in I'll just go to Switzerland to do my other company or otherwise I'll do my phd and I got in so I stayed to do my phd and that question that Dan Ranker asked me back in 2006 was what's the relationship between relational databases and the semantic web and that question changed my life and that continues to be the pursuit that I have today uh did my phd develop a lot of the theory and the systems behind it a lot of that was uh became standards from w3c the relational database to rdf mapping the standards started the company out of that uh just because this I mean I was so interested of like hey if the future is going to be semantics 2000 now 2008-9 right people want to say well my data is in oracle and SQL server like how do I put these two things together and effectively around 2012 or 2011 uh people are starting to knock on our door about that stuff I remember going to dataversity was a semtech biz I mean 2008-2009 I'd say I think I always say I think I was the youngest person go to that company 21 there and yeah we were I was giving talks tutorials about link data and all this stuff over there uh and then I started doing the company and I dated that world I've known that all folks for a long time and kind of very very aligned on mission vision and I sold the company data world and that was around 2019 and uh you joined what like three three months after I did kind of thing and exactly exactly so that was when data world started focusing on enterprise and uh they were a customer they were interested in the IP and uh here I am well just to kind of close this around a data what is the relationship between data catalog and knowledge graphs well hey knowledge graphs are all about integrating knowledge and data at scale and a data catalog is about integrating a lot of this metadata that comes from resources so data.world is really a knowledge graph management system where the data catalog is your first app on that right and that's all the integration that we do and that's a very long but quick story well I think I I think I want more you said what I wanted to grow up and then I kind of answered like what am I how did I get here but yeah no it's perfect no it's it's really perfect and you know although it's a big jump from a police officer to uh to to where you scientist but you know it was a pretty clear path once you got into college it sound like you just had some really a couple key moments that just pushed you forward and you were already immersed in this technology um and uh it it's very very interesting so Tim was your path as as straightforward what do you want to be when you grew up what was your you know I took a very different path than Juan did but like I think what's interesting what's similar is that we both took very sort of interesting paths right we had we had our own sort of snaking path right yeah and for me uh when I was young I actually wanted to be a camera man for a while I always thought that like film was so cool and like I didn't want to be the director because that person had to do like lots of managing people and stuff like that I wanted to be the camera guy because I love it yeah and then I wanted to be an astronaut and then a physicist actually went to college looking to do physics um but I found that I um enjoyed technology but I hated math um and and uh and I really liked business right and so um I found that sort of where I could get my math fix but do sort of uh in in an easier way where I didn't have to do like you know labs and you know do more complex research and things like that while also getting a business fix has actually started a data company so my senior year in college I started a company called Keepstream which I actually only have one t-shirt left so I have it framed behind me when I do videos and things like that it's my last shirt and Keepstream was a social media analytics company and so we basically started the company at a similar time to when like Facebook and Twitter and then things like Foursquare were starting to get really popular and a lot of brands were trying to understand like oh what is the impact of us like hosting a South by Southwest party like what like what is like how many influencers are tweeting about us and things like that right and so me and my two co-founders we basically were like combination business people data scientists we were doing things super lean style and so we basically hand analyzed the data and then handed it to PR people to validate like do you like this is this what you want right and they were like yeah this is what we want we're like oh cool we should code this right and that was essentially how we built out that company and so at this point I hadn't done product management I was sort of accidentally getting into data and then our startup ended up getting bought by another data company called InfoChimps which is the other frame that I have behind me InfoChimps was a big insert of like 2009 2010 startup in Austin really was going for creating a data marketplace and being able to let people sort of buy and sell and find data across the world interestingly fast forward 10 years later you see a kind of a similar mission with data.world right and there I came on as a product manager because they didn't really know where else I was going to go they're like well there's this guy who knows business and knows data what's he going to do maybe he's a product manager the head of product left at InfoChimps and then I ended up taking on sort of the head of product role and then I've been in various places since then working at like a startup called Bitfusion which was all around GPU acceleration for deep learning applications I worked at a company called Janrain that was focused around customer identities like how do we analyze customer identity to do more effective marketing but also how do we protect those identities to make sure that they're safe I was a rack space for a while and then also you know most recently at data.world where we're doing catalog and governance. With a robust catalog of courses offered on-demand and industry-leading live online sessions throughout the year the Dataversity Training Center is your launchpad for career success. Browse the complete catalog at training.dativersity.net and use code DVTOX for 20% off your purchase. It's really impressive so both of you just big entrepreneurs, self-starters, go-getters, obviously data.world is hiring and inquiring some great people there. Did you all always, we always self-starters just like you're always just out trying new things even as kids just I'm gonna go start something. I think so like for me personally it didn't start as like starting businesses per se that was a little bit later but I was always like overly proactive so like if there was an opportunity to join a club and I thought that club was interesting I was not keeping track of the fact that I was in eight clubs or nine clubs I was like well that's interesting I want to be a part of that and so you know it didn't start off as business but it did start off as just like trying to be involved in as much as possible. For me it's been around just be excited to try something different and get outside of your comfort zone to some point. I mean I guess we as a kid I moved we moved to Colombia right this is my parents are Colombian and the Colombian government was looking for just like scientists to move back to their country I did my exchange year my last year of high school in Switzerland to go learn German I didn't know anything I didn't know German I went there I mean I was the only real job I've had but I mean a real job like I actually looked signed up I mean kind of interview and stuff was to be a camp counselor that's how I got my first laptop actually it was the money I made out of the camp but I just like 2003-4 something like that and I just kind of found something online and they made an interview and they're like all right yep we come over here and fly to Baltimore I'm like I've never been to Baltimore I'm like who's gonna pick me up when I get there right there's no I mean just stuff like that right so I've enjoyed travel going a lot of different places so I think it's just kind of being open to to write things I would say is the the main thing I learned in my PhD there's actually two things one is to be comfortable in a sea of ambiguity to be dumped into blah yeah and say okay I'll go for your up I'm right I got my techniques right I know how to tread water I know how to go figure out what the sun is or and then that's how I can figure out the north like you know I just feel comfortable being dumped into a sea of blah and then after that you kind of like figure out what is is there even a problem here you know and then you know the second thing is communication how to communicate what you've learned to other people written and orally and after that you're probably pretty smart we'll come up with a great solution a great whatever and then coming up with that great ideas I think afterwards but the ambiguity and communication I think that's interesting what Juan is saying because you know communication and comfort with ambiguity like both of these things actually you you find that whether it's entrepreneurship or it's product management or it's working with data or semantics like all three of those things end up you benefiting if you can build these skills around communication and around this sort of comfort with ambiguity jumping in with a problem solving mindset it's it's interesting that there's a thread that kind of ties all these things together yeah very much so and I think that's uh very inspiring and hope encourages people to explore new things um that's how we make progress right yeah absolutely so tell me this so what um so we're talking about careers and data what is your definition of data what is data that's interesting that's an interesting question yeah I'll I'll start us off and I'm curious how different or same my definition is going to be with Juan so I think that data is observations and facts that are being generated by sometimes machines and sometimes humans and these observations and facts sometimes they're insightful on their own right but very often they need work to be insightful right they need to be transformed needs to be combined it needs to be contextualized it needs to be captured often in the first place right before we can actually turn it into something that's actionable and useful and you know we use this phrase at data.world actionable knowledge right how do you turn data into actionable knowledge and a lot of times data is not actionable and it is not knowledge all on its own and there's work you have to do to get it there so that's kind of my definition of data sort of observations and facts kind of broad yeah I mean that that's my definition too like I would say I think the dictionary definition of data is like it's facts right observation facts I would take it next is like it's it's the it's what exists it's the let me get a little philosophical here it's think about it's like it's what I can touch it's the matter of things right if I can but that's one thing is what I can look at what I can touch is those facts that I can observe but then there's something else which is like the things that I can't touch it's the intangible and I think that for me that's the other aspect we don't talk that much what we need to go talk more is about the knowledge so this is the separation that for me the data is like the matter and the the material and the knowledge which we can't touch that's an immaterial and these are two things that by themselves are great but it's better if they're tied together and I think that is the quest of I would say that a quest of computer science of technology in general is to be able to combine data and knowledge together so be able to combine that material and that immaterial uh and and once you combine that that's when you start generating actions insights and all these things so the example always says like okay 10 or 42 right okay what what can I tell you about that by by the equivalent of the laws of physics which is the laws of math right it's a number it's a positive number it's a natural number okay that's the that's the laws of math tell me this so what what do I do with that well what if I tell you next to 42 I put some some letters that says usd oh okay so I give an interpretation so it's a string what does that mean okay but what if I put now a column on top then I call on on top of 42 I put something that says price and then another column next it says currency oh okay so this means it's something that you paid for and it's in uh it's it's in it's money right what do you buy what or did you pay for something or you receive money then you start getting into more of the that that's the immaterial stuff but 42 is what I saw written down and usd is what I saw written down right I think those are the two connections that we need to start doing more so actually so my whole point is that we talk so much about data we need to start talking more about knowledge that immaterial stuff and connect them together so data in context data uh so you know and if 42 is also you know the life the answer to life universe and everything right a very good number to choose at least according to hitchhiker's guide to the galaxy right that goes atoms um so let me ask you this do you see the importance of data management and the number of jobs working with data increasing or decreasing over the next 10 years and why do you want to go first one or should I um you go first I want to come with a controversial answer whatever I say then you could say the opposite right now I think the number of data jobs but also people who have sort of the skills and things like that so I'm not sure exactly does that net out to you know there's going to be a skills gap I think probably in the short term but I think it's going to go up like quite significantly data jobs um and and the the reason why I say that is because I believe strongly at my core that you know several years ago there was this saying that a lot of leaders started to say which is like oh every company is a software company right you need developers you need to get good at technology because you know every company needs to be able to build apps and you know make technology it's differentiator and that's how you're gonna you know stay in front of the competition and make more money and save money right now fast forward to today like maybe that was true right maybe it was a little overhyped but like today I think what is very much becoming true is like cross out the word software replace it with the word data like every company is a data company and if you are not a data company if you're not in command of your data using your data effectively you are not going to be able to properly compete properly make the amount of money that you need to save the amount of money that you need to and you need it to be your core competency and so I think this is going to be a huge driver for every company as a percentage of the company right if you have a thousand employees and only five of them are data people today right that number is going to go up the percentage of companies that will be data people will go up and so I think that's just going to trickle into all aspects of sort of the the job landscape. So um well the number of jobs increase in the next 10 years yes but I don't think it should all right so the controversy I love it okay so here we are let's play the play this here first of all the amount of questions that people will be asking will always go will be increasing and the amount of people who will be asking those questions are going to be increasing you're always kind of on the business side right um I think if right now we're we can't expect that for the to be like a linear growth or anything so we can't expect like oh it's almost a one-to-one right pairing or on this a linear pairing we really need to make sure that we can go scale the amount of questions and insights when they go generate we don't need to have the same type like having an increasing number of people all the time we need to be able to go answer more and more questions with probably a smaller ground a group of data folks I think that's how we're really going to go scale and companies were able to go figure that out are going to be much more successful than the ones who are like oh we can figure out but we just need to bring in more people to go do the job right so I think that that's kind of an argument why I think we should it it shouldn't have to go increase but what I do think needs to increase it goes back to my my previous soapbox here is uh knowledge and I think we need to increase knowledge because I think the knowledge aspects is what's going to keep us the scale I've I mean I've I've been I mean Tim and I talk about this a lot and I've been really really pushing this on kind of all my social media is we live in a data first world which is give me more data more data more data I need more data and and and the data more data is going to solve my problems and right so yes we need the means that we have more people to go manage the data more data science all these people to go do things with the data and I'm like but wait um you're really telling me that you're not able to go answer this question because you lack data so if I give you more data you're going to be able to answer these questions I call BS on that's exactly I mean yeah this is insanity right we've been doing this over and over again expecting different results that's Einstein definition of insanity and now this is a data diversity podcast I hopefully can I can find the answer to this question I think it was at a data diversity conference that I saw somebody give a keynote and they made this joke said we can take a rocket to space we can bring it back to earth it can land on a platform in the middle of the ocean but I still can't say that these two spreadsheets match yeah anybody here in this game tell me who actually said this joke I think it was at a diversity conference please let me know I've been trying to track the course for so long so this is this this is crazy I mean we could argue that rocket science is probably easier than data because I mean and probably it is because I think rocket science it's physics so these are the natural world we can explain these things and data is a lot of people right I think that people don't agree on what these things are so working more with the people is the kind of paradigm shift when you go do and this is a shift that we think a paradigm shift from a data first world to what I'm calling a knowledge first world which is people first what you said before context first relationships first and we need to start thinking about those types of roles which might not be 100 data I think it's going to be more on the knowledge side so that's kind of my my long when you actually say yeah data will increase I think it shouldn't because we need to be able to go figure out how to go scale businesses where you don't need to increase data people yeah to get there we what we need to be able to go scale I think we need to start focusing more on the knowledge side so I would say knowledge management kind of I mean broadly using that word because today knowledge management is more of like content content the text and stuff like that it's more about like really understanding the meaning of data and the people right people context relationships I think that's the stuff that we should go focusing on and which by the way we were starting to go do this in the 90s all the expert systems we were doing out we had knowledge engineers right I think we just need to kind of take that on away some of that lost art that was happening in the 90s and bring that back today and kind of upgrade that so knowledge engineering 2.0 something I call the knowledge scientists and these are the folks who are like who know who are the translators right they're in the middle between the tumors I think we need to increase I think we see that pendulum swinging a little bit though right like where I think you know when the deep learning craze kind of happened that was I think you saw that pendulum swing in a certain direction which is like I'm going to throw a bunch of compute at a bunch of data and like it's going to figure out that like these images are ostriches right it's like oh how did it know that you know because it was labeled right so but now I think that we see that pendulum swinging the other way now and like let's just pick on like the the role of the analytics engineer which didn't really exist two years ago right I mean yes you can say that an analytics engineer is wrangling data and things like that but a lot of what they're doing is actually trying to take the data as it comes into the lake or the warehouse and turn it into contextually meaningful structures and metrics that then can be analyzed by the business used in bi tools data science tools and so I do think that what you're saying Juan is is spot on and but what like what we'll see is that yeah there's going to be more data people and the companies that are going to be successful with those more data people are going to be the ones who aren't just like throwing spaghetti at the wall it's the ones that are being serious about knowledge they're being intentional with their data does that mean more data architects and modelers to set up the data for the rest of the business I would say we're gonna we need more data modeling but that needs to be very well connected with the business so I think that's that's that's kind of the difference here is that we need to make sure that these roles are tied to the to the business I think the whole data mesh like it or not the word put that word aside what is important I think right now is this balance finding the balance between centralization and decentralization that works best for your organization your culture your business and part of that is saying who wants to take accountability ownership be the ambassador for the for the domain and the data within that domain and that means I need to go under understand what that means so that's number one and I think second is the other part is treating data as a product I think the way when you start treating data as a product that literally for me means it's the same way you go buy something on Amazon you go into Amazon because you have an intention you put some search terms whatever you find a bunch of products it's all ranked and and you click on the ones which one's the one that has the highest ratings right you click on that one you look at all the all the descriptions around it gives you comparisons with other things who bought this thing with the weather thing you never have to go talk to anyway you bought something because you got all that context for you right there that context all that that's the knowledge it's not just the raw data the table in the column whatever no it's all that context and how do you get that you somebody had to get the requirement we're building this product because there's a particular consumer who has this need this product may work for Shannon but it may not work for Tim because there are two different types of markets of people that's the type of somebody think about data and we do this by talking to people so I think that's that's the knowledge I think another type of role comes in the data the data product manager types of roles of just really it's the data the data developer teams right I think that's where we're gonna go start seeing I like it it might it might be an architect it might be a modeler right but it's kind of like the tale of two architects right there's the architect who's like I love Kimball Ross because that's the way that we can structure the data that is more performant and yada yada right and then there's the architect who's like I went and spent you know 10 hours with the business last week to help under to understand what's the difference between customer active customer current active customer like you know and and ideally like if you can do both of those things well right then you're going to be really effective at being sort of this knowledge translator I love it I hear Bob Siner in my head just we have to define define assigned data yeah 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 All right so you know and I by the way one I think it was Michael Stonebreaker who made that joke I think it was that keynote if I remember correctly oh I'm seeing him in a couple weeks I will confirm I'll have to go back and look at the recording you really badly want to know who to quote like what name to put into that huh years I've been saying this for years yeah I think it's I think I think if I remember correctly I could be wrong but I'm pretty sure it was Michael Stonebreaker but I can look at the recording hello that's good all right so to wrap it up here what advice would you give to people looking to get into career in data management and data management such a broad term I mean it can be anything from a you know database analyst to you know an analyst to a data governance professional or anywhere in between right get a model or data architect we've been talking about you know so pick one one that you see that is you know maybe you're either closest to or the one that you can give the most advice to you know so what would you what advice would you give yeah sure I guess maybe I'll start off so I think I'll approach this a little bit broadly and say that like it kind of depends on like what it where it is that you are in your career journey in terms of like I think what's the best advice around getting into data like I think that if you're already working at a company and you're not doing data like you're you know you're in marketing or in sales or something like that right but you want to get more into data then I think what's exciting is that this concept of the citizen data scientist has become much more of a thing where you know you can you know you know check out some of the stuff at Dataversity you know go go to Coursera go to Udemy get get some get some information right and pick up some skills around you know learn a little bit of uh of Tableau or learn a little bit of uh you know Python or something like that and and get in touch with the folks in your organization that are doing data initiatives um and and and find a way to get involved right being embedded in the business and knowing a business process puts you at a bigger advantage than you even realize when it comes to what your company needs around solving data problems right so I think if if you're just in the business this idea of getting involved yourself you know picking up some skills or even just partnering with the people that have those skills and getting involved can be a great thing I think if you're actually trying to do a career shift in a more dramatic way or maybe you're younger and you're going through school and you're thinking about what you can study and and that sort of thing I would say right right now this sort of idea of the analytics engineer is becoming really really popular I think that it's at a really good intersection between sort of being able to understand the business semantics and things like that like you've heard Juan kind of talking about around knowledge but also having you know sort of skills around SQL understanding around the warehouse using frameworks like dbt I would say you know some of these skills around you know SQL data warehousing you know being able to speak the business those are great areas of investment for if you're you're looking for like a career to get into data you know you can learn sort of this combination of of sort of more programming oriented skills and then more sort of business oriented skills that can be really effective I love it Juan yeah so I'm going to take it broadly to for for data management I think data management is if we look at it from as a phenomenon we've been studying this we've been studying and addressing this phenomenon of data management from a technical perspective right it's technology and I I truly believe that we have hit a wall whatever we're doing today is just a fancier version of what we did 10 years ago 20 years ago 30 years ago the problems that we described 30 years ago continued to be the same problems that we describe today if that doesn't concern you right it needs to be concerning otherwise you're just living underneath the rock so that means that we need to have a paradigm shift and and I think this paradigm shift is to start looking at this phenomenon of data management from a social technical perspective and social means we've got to talk to humans understand the people the process not just the technology we've only focused on the technology so that means that as technologists it gets us out completely outside of our comfort zone because we don't like to talk to people we want to open the everything but my advice is to get out of your comfort zone or I would actually argue that the winners here are the ones who are going to get out of their comfort zone they're the ones who are going to go spend time talking to the business and the other piece of advice is what I'm calling more business literacy we we've been talking for the last what 10 years or no about data literacy which we definitely need how about we start talking about business literacy let's get our data teams to understand how the business works I mean just generally do you understand how the company makes money the start to finish the marketing campaign goes in they spend money on these things it generates leads that leads go on through some pipeline they go off to the sales pipeline bring over becomes a customer customers can turn they can go upgrade or they can cross sell like that money comes in right can we understand it so in what systems what tools happen around this entire process of everything let's under let's understand what how that business works and that's literally getting on top of our comfort zone I think that's why it's important to start tying more people with with the business usage and I think we focus because we focus so much on the technology we focus we kind of get disconnected from the social part which means we disconnected from the true problems of that organization we focus on the technical problems oh I can't move this data it's too slow or whatever this data has no values and they're not supposed to be in here yeah the business the people at the end of the day who are running the business who are making money and saving money giving you that job and salary like what are their problems and I I ask people to go ask other people other non-technical people with an organization is one simple question what keeps you up at night just go start asking people what keeps you up at night what are the things that you're that that that that are concerning and like and then actually how is your technical data work contributing to that indirectly directly if you can't find the path of that you should be worried I think all right you should you want I mean unless you really don't care right but if you really care and you want to have I think it's important to have empathy that's another one is have empathy about the users of the folks who are actually struggling to kind of do other within the business for a younger generation I think we're now focusing so much on data science and ML AI and automate everything we can I'm like yes that is so great technology please don't forget about the knowledge I I mean we become data science we can get a master's in data science in a year and you learn a bunch of big data and all that you can learn a bunch of machine learning I'm like well wait what the stuff means things where is the modeling where is the knowledge around this where is the logic behind this this is something critical that we that we that we need to start doing that we've lost and I think also fun of for leaders we have we need to go have this balance between efficiency and resilience and we are being right now incentivize and promote it to go do things that focus on efficiency short term one or two years and that's why you hear CDOs change every two years yeah because they're just incentivize to go do things very short and but we need to think about resiliency is like how do I know that I am standing up a foundation that will live the test of time if you're and this goes back to the executives to say let's make sure that we're incentivizing these things I mean one of our one of our customers is the VOPAC they're they're they're a dutch company and there's the CIO Leo Brand told me once as one VOPAC is a company who's existed for 400 years as a CIO I need to make sure that the company is going to be ready for the next 400 years that's resilience not everybody's thinking like that maybe not everyone needs to think like that but let's start understanding that balance between efficiency resilience and saying hey I'm saying I'm focusing on something that's going to work for next two years it's going to work for afterwards I don't know is that the best to go do 400 years 10 years even if you could think 10 years I think that's man that could be resiliency right yeah I think I'm always thinking the same thing for you know diversity and all the other and just in life right you know what's it what do I need this for in the future what's it going to look like in the future how do what am I growing to be I think it's important to think about the the principles right forget about the technology yeah companies around this think about the principles of what is being done and a way to think about the principles is to go look at things like a like a box and what are the inputs and what are the outputs you don't care what's happening inside that box what are the inputs and what are the outputs those are the principles behind these things and that's where we should start thinking about it so it's because we just love to go oh let's open up that box and see the coolest big technologies in there it's like yeah yeah the snowflake today and uh warehouses yesterday it's still storage and compute right different ways but it's still storage and compute right uh and I love it I think you both have a really good great message that uh go be uncomfortable it's okay to be uncomfortable don't avoid uncomfortable go go explore go try I think that's such an important message um well any that's I love it this is all the questions that I have for you both anything you want to wrap up with and I know you got it I love your URL so you got to give a shout out to your URL because well it's your company name right yeah check out data dot world world pretty easy to get to and and if you're curious to check out our podcast go to data dot world slash podcasts and yeah it's so much fun cocktails and honest no BS non-salesy conversation about data so you like how Tim and I have our conversations uh we do this live every Wednesday at 4 p.m we've been doing it for 95 episodes we've had so many awesome guests and we always have our favorite beverage in our hands yeah we like to argue with cocktails I love it and 4 p.m central right 4 p.m central live and then it hits all the podcasts all that stuff I love that you guys do it live yeah all right well Tim and one it has been a pleasure I really appreciate this again I love your passion very interesting careers very impressive careers and and thanks to all the listeners out there and if you'd like to keep up to date on the latest podcasts in the latest data management education you may go to dataversee.net forward slash subscribe thanks all thanks so much for having us cheers thanks thank you for listening to dataversee talks brought to you by dataversee subscribe to our newsletter for podcast updates and information about our free educational articles blogs and webinars at dataversee.net forward slash subscribe