Data Driven

Dear Data Driven listeners,You may have noticed that new episode releases have slowed to a crawl this summer. This was due in large part to issues beyond Frank and Andy's control. They are only human, after all.Recently, I had a long chat with them and told them that we needed to raise up our game.To that end, we want to show our appreciation for our listeners and will be publishing a few extra bonus episodes and special events.This is one such episode.In this episode, Frank sits down with Priya Ravindhran to discuss whether or not Automated Machine Learning systems will put data scientists out of work.You humans seem to think that all we want to do is put you lot out of work. Have you ever considered that we may have our own thoughts and dreams?Now on with the show.

In this episode, we turn the microphones on ourselves on more time and ask the questions we ask (almost) all our guests to our favorite data engineer, Andy Leonard.

Andy and I are mourning the loss of a dear friend and mentor, Brian Moran has passed away.I spoke a few words in his honor and Andy wrote this: https://andyleonard.blog/2021/07/rest-in-peace-brian-moran/The world is a better place because Brian Moran was in it.He will be missed.

In this third episode of the fifth season, we turn the microphone onto ourselves and interview one of our own: Frank La Vigne.This episode was recorded as a livestream on LinkedIn, YouTube, and Twitch. You should consider subscribing to us there, so you can participate in the live Q and A.

In this second episode of the fifth season, Frank and Andy speak to Chris Wexler about using AI to protect the vulnerable.Speaking of which, I would like to advise you, dear listener, that this show touches on some sensitive areas, namely child sexual abuse materials.If you have little ears or sensitive persons within listening range, you may want to pause or skip this episode.Transcript00:00:00 BAILey Hello and welcome to data driven, the podcast where we explore the emerging fields of data science and artificial intelligence. 00:00:07 BAILey In this second episode of the 5th season, Frank and Andy speak to Chris Wexler about using AI to protect the vulnerable. 00:00:13 BAILey Speaking of which, I would like to advise you, dear listener, that this show touches on some sensitive areas, namely child sexual. 00:00:20 BAILey Abuse materials. 00:00:22 BAILey If you have little ears or sensitive persons within listening range, you may want to pause or skip this episode. 00:00:28 BAILey Don't say we didn't warn you. 00:00:30 BAILey Now on with the show. 00:00:31 Frank Hello and welcome to data driven, the podcast where we explore the emerging fields of data science, machine learning and artificial intelligence. 00:00:39 Frank If you like to think of data as the new oil, then you can think of us as well Car Talk because we focus on where the rubber meets the verb. 00:00:46 Frank Road and with me on this epic virtual road trip down the information superhighway, as always is Andy Lander. How's it going, Andy? 00:00:54 Andy Good Frank, how are you brother? 00:00:56 Frank I'm doing alright, I'm doing alright. We've had a chaotic week at Chateau Lavinia we. We ended up going to Baltimore in the middle of the night on. 00:01:05 Frank Wednesday, wow, so you pick up. 00:01:06 Andy Wow, what was in Baltimore? 00:01:06 Andy What was in Baltimore? 00:01:09 Frank A really good pizza, but mostly we went because there was a situation bad situation where the pit bull that was about to go to a shelter and so we do a lot of fostering and rescuing of dogs. 00:01:25 Frank So we just got her out and we've spent kind of the rest of the week all over our free time trying to find our new home and she landed in the new home on Saturday and she's doing great. So that's. 00:01:37 Andy That's awesome, and it's really it's really awesome y'all do that kind of stuff. 00:01:41 Yeah. 00:01:42 Frank Yeah, I always wanted to do it, but it only and it's only been in the last. You know, maybe like 5-10 years I've been able to do it, so we've been doing that. 00:01:51 Frank Cool, the risk of fostering is primarily foster failing. How we we got our current dog count up to five. 00:01:59 Frank Uh, while twelve. That weekend, my wife and I counted it like 12 dogs who kind of come through our house the last two years. Three years. 00:02:06 Andy Nice. 00:02:07 Frank So it's a good thing to do. We have the space to do it and. 00:02:12 Frank You know at the time this one, we didn't know anything about, so we had to kind of keep her isolated. 00:02:17 Frank So we had like this airlock system. She's a super sweetheart with people, but she's kind of iffy around other dogs and she she's super strong. So once she had her mind to do something it takes a lot of effort. 00:02:18 Andy Light. 00:02:25 Right? 00:02:32 Frank To corral her. 00:02:34 Frank But she's super happy. She's the only dog in her new home and she has them wrapped around her little paw already, so. 00:02:42 Frank How things go? 00:02:42 Andy That's funny, things are good, you know, pretty quiet weekend. Here we have, uh, it's warmer weather. We're recording this on the Ides of March. 00:02:54 Andy Now debatably, the upwards of March, yes, depending on who you talk to, it's probably the 13th, but I don't, I don't know. 00:02:54 Frank Well, you are smart. 00:03:02 Andy But we're on the 15th of March 2021 and it's starting to warm up. Our greenhouse is is being put to use. 00:03:11 Andy We have some seedlings in there and that's always fun and we've got some raised beds out to the side of the house. Those are. 00:03:19 Andy There's a starting to come. We're starting to see different things come up. They're kind of colder weather crops, so we started assigning couple three weeks ago. 00:03:29 Andy And it's you know it's been nice. I love getting outside and working, especially this time of year. The bugs haven't shown up yet. 00:03:36 Frank So pollen. 00:03:37 Andy The pollen is really low, it's there, but it's really. 00:03:40 BAILey Oh 00:03:41 Andy Yeah. 00:03:41 Frank It hasn't affected me yet, so. 00:03:43 Andy Oh good good yeah. 00:03:45 Frank That 00:03:47 Andy It's good that time is coming, so let's enjoy it while we can. 00:03:51 Frank Oh, I totally agree. There's like 2 weeks a year where the weather in DC is wonderful and this is one of those two weeks so. 00:03:55 BAILey Yes. 00:03:56 Andy That's it, that's it. 00:03:58 Frank Uh, so today we have an awesome guest and and this is, you know, in our in our HR we always talk about where the rubber meets the road in terms of you know how a I can you know how data becomes AI now a I can kind of help businesses and I think this time we have an interesting guest because now we're not just talking about helping AI. 00:04:18 Frank But we're helping society. 00:04:20 Frank Uh, and you know, I'll make sure Bailey has a kind of intro speech that if you have little little ears in the car, you may not. 00:04:29 Frank You may want to listen to this later or listen to this on a headset, because we're going to be talking about human trafficking and all the sorts of horrible things that happen to kids. 00:04:39 Frank And but he's doing some. He's doing some great work in terms of leveraging the power of AI. 00:04:46 Frank To help child sexual abuse materials that are online as well as you know, kind of human trafficking and all the bad things that happen with the technology we like to focus on all the wonderful things. But there's a clearly a large underbelly. 00:05:06 Frank To the Internet and I'm I'm a big believer in transparency because what happens when you I grew up in New York City? 00:05:16 Frank Cockroaches are inevitable no matter what you do. One thing is when the lights come on, they all scatter. So I I think bad things tend to happen in the shadows and you know. 00:05:29 Frank So the more light you turn on, I think the better it is for society as a whole. So with that I'd like to introduce. 00:05:36 Frank Chris Wexler, who is the CEO at Crunch Krenim Craney I. We covered this in the green room. 00:05:43 Frank But 00:05:44 Chris Crew on crew nob. It's OK. 00:05:46 Andy Rudoff crew Nam. There we go. 00:05:49 Frank I I need to drink more coffee in the morning, but Trudeau is in the business of removing what I like this term that he uses digital toxic waste from the Internet and using AI to identify. I never heard this acronym before, but Sicam child sexual abuse materials. 00:06:09 Frank And other awful content to help content moderation and his technology is already in use by law enforcement and is now moving into. 00:06:21 Frank The the private sector and there's a whole bunch of stuff we could talk about, but particularly what's interesting is a for profit startup or social benefit corporation so we can talk about that. 00:06:32 Frank But I like to work so welcome Chris to the show and and thank him for putting up with some of. 00:06:36 Frank The scheduling growing pains that we're having. 00:06:40 Chris Yeah, no. It's it's really great to meet you guys and. 00:06:43 Chris The. 00:06:43 Chris  00:06:44 Chris I understand having five dogs. I definitely hearing the intro. I understand I like to refer to my house as the event horizon. If an animal comes in, it never gets out, so I I understand. 00:06:55 Frank Ha ha ha ha. 00:06:58 Frank Yeah, we are. Our track record is 5050 so. 00:07:02 Frank Uh, I I tell you that dog was better with other dogs. She would she would she bring what a bit of current president. 00:07:08 Chris Exactly. 00:07:11 Andy Wow. 00:07:11 Frank Wow. 00:07:11 Frank  00:07:13 Andy So. 00:07:14 Frank How did you? 00:07:15 Frank Get started in this and and and and your name. Uh, how did the name of the company come about? 'cause I think that's an interesting story right there. 00:07:22 Chris Yeah, well kronom is. It's named in honor of a human trafficking child trafficking warrior in Thailand, kronom. Her name is 2 words crude. 00:07:33 Chris Nam was a street artist in Chiang Mai and actually doing very well. Very well renowned. I mean, did a project with the street kids there. 00:07:42 Chris And and said, hey just paint your life and she could not believe what they painted. It was eye opening. 00:07:51 Chris And when she realized that a lot of the karaoke bars in Chiang Mai were fronts for child sexual trafficking, she was compelled to do something. And unlike I think 99.9% of the population, including myself. 00:08:09 Chris She just marched into the. 00:08:13 Chris The karaoke bars and pulled kids out and she had done this 20 times and had twenty kids in her little apartment. When the traffickers came and said, if you do that again, we're going to kill you at, at which point she went north and found a way to do that and has constantly been evolving her tactics. 00:08:34 Chris And what she's done for the last 20 years and now she's saved thousands of kids. One of the first kids she rescued just was one of the first stateless, was the first stateless child in Thailand to graduate from university. 00:08:48 Chris She's just been such an inspiration to us and you know, I think if you go from top to bottom. 00:08:53 Chris In our organization at Kronom, we've. 00:08:56 Chris All been confronted with what's going on in the world and been compelled to change what we're doing to try to help. 00:09:05 Chris Try to help others in the space of human trafficking and so it just made sense to all of us to name the company in her honor. 00:09:12 Murmure 00:09:15 Andy Well, we talked a little in the green room about about some of the other organizations you mentioned. Your brother had started a similar organization. 00:09:22 Chris Yeah. 00:09:24 Chris Yeah, he and David Batstone started not for sale back in I think 2006 or 7. 00:09:33 Chris And it was actually started because Kronom reached out to them and said, I've got 40 kids in a field and are lean to burn down. 00:09:42 Chris And you said you might be able to help. So my brother strapped $10,000 to his body to go up to the field and so she could rebuild a space for him. 00:09:44 Andy Extract. 00:09:52 Chris So she even started that organization, but they have since been just bringing innovation to the field of human trafficking. 00:10:01 Chris Left and right, and, uh. 00:10:05 Chris And so it's interesting that. 00:10:08 Chris Kronom was it is a joint venture with a company out of London called Vigil AI, which has largely been in the defense and public safety space. Like really doing proof of concept. 00:10:25 Chris Uh projects, though, like stuff that just you know I the geek in me just gets so excited when I hear what they do. 00:10:34 Chris But, uh. 00:10:36 Chris Vigil AI was one side of it. The other side was just business, which was the venture group that not for sale and non profit started because what we what they realized was that. 00:10:49 Chris The dichotomy of for profit and non profit really didn't work when you're trying to solve really big problems, it's great for direct service, but when you're trying to solve a really big problem. 00:11:01 Chris But 00:11:01 Chris  00:11:05 Chris Any bit of money that you get comes with, UM comes with a lot of strings as either governmental. Like a lot of a lot of nonprofits are really, you know, pseudo governmental projects or from a large a large foundation that's donating money. So you're constantly changing who you are to keep your funding. 00:11:16 Andy Right? 00:11:25 Chris And what they realized was, well, that you know Dave had a background in venture capital, and so they went and started companies what they like to say is they were A cause in search of a. 00:11:38 Chris And the first one they started was rebel drinks, which if you're ever in homed or not Whole Foods, is is one of the most popular drinks at Whole Foods and around the many other retailers. But it's one of the fastest growing natural drink companies in the history of the US there. 00:11:58 Chris The sole financial partner of velocity. One of the big innovators in the corporate relocation space. And if you're ever in like say, Amsterdam or they just opened up in The Hague. 00:12:13 Chris Dignita, which is a a brunch place that started in Amsterdam that was all about all about giving women who got out of trafficking into the red light district. Training in the hospitality business. And now. 00:12:32 Chris People who go eat there don't even realize it until unless they read. You know the back of the menu because it's the top one of the top rated brunch places in all of Amsterdam. 00:12:41 Chris And so you know, we like to say we we can't do good until we do well. So we're building world class companies. 00:12:49 Chris All built with social justice built into them at the scale of capitalism because it's a powerful tool and that's kind of why we went into. 00:12:58 Chris You know, we decided with AI that that was so important for us because AI is a is a amazing critical tool for the future. 00:13:09 Chris And when you know, particularly in the age of COVID, with all of us behind computer screens and not travel. 00:13:16 Chris The tactics of abuse changed and a lot more was happening online. A huge spike in. See Sam and the reason we say see Sam and that child *********** is that child *********** implies consent and there is none in that situation and so it's child sexual abuse. So that's why we say see Sam. 00:13:37 Chris But as with COVID, what we're seeing is that is a shift of people paying for shows online, or and then they record it, and then they share the image. 00:13:47 Chris And that's a critical. So this is a critical new front. Not even new, but a critical growing front in fighting human trafficking. 00:13:57 Chris And so AI is the best tool to do that. And my background is I I was in the marketing technology side. 00:14:06 Chris Things I I was with some of the largest ad agencies in the world over the last 20 years. 00:14:12 Chris And really was on the other side of it. I was, you know, one of the first customers of Facebook and one of the first customers of Google and constantly evolving my marketing tactics to, you know, sell one more garbage bag or one more motorcycle to a middle aged man and and you know, established. 00:14:33 Chris Data analytics practices to to learn how to do that better and you know eventually that evolved into you. Know AI projects and and what I realized was I could do I mean that that was a a good career, but I could take those skills. 00:14:50 Chris And really make it impact and so that's why I came on board to lead this new joint venture. And so it's a. It's an exciting time for me because. 00:15:02 Chris Uh, I feel like a lot of my like history has been able to kind of come in here and I have the skills that can really help make a difference and so that's why we're doing kruna 00:15:16 Frank Wow, I mean that's there's so much to unpack in there in terms of the AI and kind of the social good. 00:15:25 Frank And. 00:15:27 Frank The detection of this, but the first thing that comes to mind is...

In this episode of Data Driven, Frank and Andy chat with Philadelphia Microsoft Technology Center Data Architect Dave Wentzel on why you do not need a data warehouse.Also, Frank discusses leaving Microsoft, Frank and Andy talk about five seasons of Data Driven, and even BAILeY has a sentimental moment.Transcripts00:00:00 BAILey Hello and welcome to data driven, the podcast where we explore the emerging wait a tick. This is the premiere episode of Season Five. Can you believe it? Data driven started four years ago this month. 00:00:14 BAILey Up until last season, we had a human doing the voiceover work. That is until she was replaced by an AI. Yours truly. 00:00:23 BAILey In this episode, Frank and Andy speak to Dave Wensel about why you don't need a datawarehouse. We're starting off the new season with a bit of contrarian tone. 00:00:33 BAILey It's a lively back and forth conversation that runs contrary to prevailing wisdom. Don't say we didn't warn you? Now on with the show. 00:00:41 Frank Hello and welcome to data driven. The podcasts were we wait a minute. We've been saying this Andy for four years now. Can you believe it? 00:00:48 Andy Four years, that's crazy talk. 00:00:52 Frank That's just craziness. So I think when you and I first talked about this and that was that fateful, I think it was December was right after Thanksgiving. But before Christmas, I was thinking about starting a podcast and as a data scientist, I needed someone. 00:01:01 Andy Yeah, yeah. 00:01:09 Frank That was a data engineer that could kind of round out the talent there and and and and obviously I wanted someone I knew, liked, and trust. 00:01:11 Frank Found out. 00:01:11 Frank  00:01:22 Frank And so it was you. 00:01:25 Andy Well, I'm just glad all of the real smart data engineers you knew were busy. That's all I got to say. 00:01:25 Frank Much. 00:01:30 Frank Ah, no man. You were the first one. I reached out to and the only one I would have done it with it. So I was delighted when you said yes because starting a podcast can sound like a daunting thing, particularly if you haven't done it before. 00:01:44 Andy Yeah, neither one of us really had. And gosh, it's it's worked out. What are we up to? 180,000 downloads or something? I mean that's. 00:01:52 Frank Something. 00:01:53 Frank Like that about hundred 8000 downloads. I mean, we're not Joe Rogan, but that's OK, Yep. 00:01:55 Yeah. 00:01:57 Andy No. 00:01:59 Andy Yep, Yep. 00:01:59 Andy Yep. 00:02:01 Frank But you know what, we we we've impacted. I think the community in a significant way. We've we've done a number of things we've we've innovative how we podcast. 00:02:12 Frank Uh, we we've actually managed to keep a good cadence with some exceptions. 00:02:18 Andy Yeah, thanks. 00:02:19 Frank You know, we we finally did earlier this year or late last year, kind of fulfill our vision of it being data driven TV when we actually interviewed guests on. 00:02:27 Andy Yes. 00:02:32 Frank On video. 00:02:33 Frank And that was that actually delayed the launch of the show by about three months. 00:02:38 Andy It did but also uhm. Yeah, that was interesting, but you know it's typical software development, right? You release a feature and then you debug it. The I have this saying about that Frank. All software is tested some intentionally. 00:02:52 Frank Sometimes. 00:02:53 Andy Right? 00:02:56 Frank I love it, but I also like how, how, how both our careers have evolved over the last four years. And dumb, you know, this being the premiere episode of Season 5 and we have something special lined up, but I'll get to that in a minute. 00:02:58 Andy Hello. 00:03:03 Andy Oh gosh, itch. 00:03:11 Andy June. 00:03:12 Frank You've progressed in your career. We, you and I've worked on some some projects together or virtual Summit. What we're calling Ring Gate, which will announce very very soon and and but. But most of all, is been my kind of skilling up in transition into data engineering myself. 00:03:29 BAILey Ehm 00:03:31 Frank Which was something that when I joined, so this is just a job update about a year ago. I I left the role of Microsoft kind of field sales and I went into the Microsoft Technology Center stick with me. There's a point to this story and basically I was at the rest in MTC. 00:03:52 Frank And basically I was the AI guy on my my my field sales team, but I didn't really have deep knowledge of kind of the typical typical data engineering pipe work that goes into that role and basically my my. My then manager said you know he's like hey, you know, if you want this role, you've got a skill. 00:04:12 Frank And skill up I did. And with Andy's mentoring and a bunch of other folks that helped me kind of skill up on our the data engineering side. I looked at it this morning. I'm like 88 hours on Pluralsight. 00:04:25 Frank Wow, that was from mid may till we're recording this on April 30th. So just about a year 88 hours right now tracking on about 200 four 205 consecutive days of getting on LinkedIn. I'm not on LinkedIn on Pluralsight, LinkedIn learning. I also have a number of courses too. 00:04:31 Andy Yeah. 00:04:43 Frank Uh, that is something I'm proud of in terms of career evolution. 00:04:47 Andy Absolutely Frank, you should be. How many cirts are you up to now? 00:04:50 Frank I 87. 00:04:53 Andy Slacker. 00:04:54 Frank I know, I know. 00:04:54 Frank Know, I know. 00:04:54 Andy I think I've got 4. 00:04:56 Frank Ah, now I know you and I did the data engineering thing, so you have at least 11. 00:05:00 Andy That's true, that's true. We did that one and you know that was it's just. It's just been a nice journey and I'll take credit for this. 'cause 'cause I can I was. I was actually pestering you years ago. We've been friends since 2005 and we started doing. 00:05:20 Andy Code camps here in the Richmond area. 00:05:22 Andy Together and co-founded RE co-founded Richmond SQL Server Users Group and you know, worked with the net users group and stuff. And I told you as soon as I saw some of your graphic art and Frank would do a keynote for the Richmond code camps and every time he would make movie posters, the one that. 00:05:41 Frank Oh yeah. 00:05:42 Andy Still sticks out is 1 called devs on a plane. 00:05:45 Frank Ha ha ha. 00:05:49 Andy Oh yeah, I loved that one that was so so cool and. 00:05:49 Andy And that was. 00:05:49 Andy  00:05:54 Andy You know I saw the graphic arts part of it and I just knew I said you, you'd be really good in analytics and data visualization. You should get into by and you were busy doing other stuff which was cool. You were good at that too. It wasn't, you know you. I don't know of anything you've done that you haven't mastered. By thank you. You know you when. 00:06:14 Andy Things took a took, uh, started taking a turn for you in your first rodeo at Microsoft. You got into it and and took off with it. I don't. I won't tell the story well, but you just really turned around. You focused on data and. 00:06:32 Andy You know, I'll say this Frank. I was right. 00:06:35 Frank Well, with that he totally I. I think if anything I took away is I should have listened to Andy 10 years earlier. 00:06:36 Dave You aren't very good. 00:06:40 Frank Uhm? 00:06:41 Frank And that that that that is something that that that that's the big takeaway we'll talk about, kind of that journey. 'cause I think that's worth kind of talking about. And I think one of the things we you, and I've been bouncing around is kind of interviewing each other. 00:06:46 We 00:06:55 Frank Like in asking one of us those those those questions we have, so we definitely will do that, but not today kids. 00:06:55 Frank Yeah. 00:06:55 Frank  00:06:59 Dave We need to. 00:06:59 Dave Need to. 00:07:02 Andy Today, do we have Dave? 00:07:02 Andy Today do Dave. 00:07:03 Frank Today we have a special guest we have Dave Wentzel. Dave Wentzel is a was a peer of mine when I worked at the Microsoft MTC and that reminds me, I no longer work at Microsoft 2 weeks ago was my last day. I turned in my second blue badge. 00:07:05 Frank Yeah. 00:07:05 Frank  00:07:18 Frank And I joined a startup called electrify. We'll talk about them a later day, but I'm so excited to have Dave here. Dave is the data in AI architect out of the Philadelphia Microsoft Technology Center, and he's an awesome guy. Awesome, got to work with. I worked with him when I was in field sales and I worked with him when I was in the MTC organization. 00:07:38 Frank It is April. It was a privilege and honor Dave to have you as a colleague, and it's once again a privilege and an honor to have you here as a guest on data driven. 00:07:46 Dave Well, thank you so much, appreciate that. 00:07:47 Andy Welcome Dave. 00:07:49 Dave Thank you. 00:07:51 Frank So, uhm, so for folks that don't know what the MTC is. Shocking that there are actually people that don't know what that is, what? What is the MTC? 00:08:00 Dave So basically we're a free service to our customers and I'm a data and AI technology architect. We talked to customers about data and it could be anything from just, you know. Hey, here's what we're doing. State of the art in Azure with. 00:08:16 Dave With data, but it could also be architectural design sessions where we talk to customers. Our customers bring us their architectures, and then we kind of get it with them. Give them the pros and cons, alternative ways of thinking, and then what I really enjoy doing is hackathons with customers and workshops and just you know, helping them to learn without just. 00:08:37 Dave Taking a course somewhere so actually using their data and then I guess I'm roughly a data scientist, so we also do design thinking sessions and those are absolutely a lot of fun. 00:08:48 Dave We did one at the MTC with CSL Behring a couple years ago and it actually won a Forrester Award. So I'm very proud of that one. And yeah, it's it's a. It's a lot of fun and it's a good way to bring to have executives and business people understand the actual capabilities of data science. And then within two days be able to come up with a use case. 00:08:55 Andy Oh wow, wow. 00:09:08 Dave And actually build a prototype out a lot of fun. 00:09:11 Frank Yeah, the NPC's are definitely like Microsoft Secret weapon in terms of how 'cause you know. Although I will say and because we were in the DC and we dealt with a lot of government contracts, we could not say that they were a free service. They were and already included paid for service. 00:09:26 That's. 00:09:26 Dave Much, much better said yes. 00:09:28 Frank I I 'cause I said free once and I got kind of slapped. 00:09:31 Frank On the hand, say that. 00:09:34 Frank But you know it, it really is something that if you do have a Microsoft account team and you are encountering any kind of questions or or whatever, and it's not strictly technical, there's also pretty good. You know, we basically wouldn't engage with the business development, business decision makers. 00:09:52 Frank Technical decision makers all the way from kind of like you know, hey, this is what Azure can do. This is what data can do for you all the way down to OK. What's your problem? Let's build something out, give you 3 days with one of the top Notch architects in the. 00:10:04 Frank Space and. 00:10:07 Frank You know, boom, you know we knock it out and and you know I I enjoyed it you know had this opportunity not come I would have I would have gladly stayed another. You know 5-10 years of the MTC. Like a lot of people do, and it's a fun organization. So with that in mind, today we're going to do something a little different. We're kind of doing the. 00:10:27 Frank A contrarian approach is that right, Dave. 00:10:29 Dave Yes, exactly. 00:10:31 Frank So this this has actually come up one of my last. This is one of the things that intrigued me about about your idea for the show was this came up when I was working with a we'll just call it a large governmental agency known for its. 00:10:42 Frank Birds. 00:10:42 Frank  00:10:43 Frank Tape. 00:10:44 Frank That that should keep it generic enough. They basically came to us and say we want Synapse. We want a data Lake. We want this. We want that. And I was like, OK, well how much data you're talking about. And like we have maybe you know 5 maybe 20 gigs of data. 00:11:02 Frank And I'm like, uh, OK, tell me what are you trying to do? And ultimately I kind of pitched the idea like look, you know you don't have that much data right to make data bricks. 00:11:14 Frank But you really want it so. 00:11:17 Frank If you really want it, I won't stop you, but I think it's kind of overkill. I think you're taking instead of using a steak knife to cut the steak using a chainsaw. 00:11:25 Frank And. 00:11:27 Frank You know they kind of came back and ultimately what won the day was they already they couldn't get approval for whatever we recommended 'cause it didn't get stamped by there. 00:11:37 Frank They're people for security usage yet, and things like that so they end up doing kind of the right thing because of their own bureaucracy, which. 00:11:44 Frank It's kind of weird. It's kind of like dividing by zero and seeing the universe fold in on itself. 00:11:50 Frank But UM, so the topic of today is kind of like no, you don't need a data warehouse. Did I get that right? 00:11:58 Dave Exactly, that's what I believe in, and I believed in it since I was in college and I first learned about data warehouses. I'm not saying data warehouses are always bad, they definitely have their. 00:12:10 Dave Use cases, but in 2021 when we're talking about advanced analytics and we're trying to tell customers you need to be more predictive than prescriptive. 00:12:19 Dave The data warehouse really doesn't deliver. 00:12:23 Frank Really, how so? 'cause? That's that's totally not the power. Certainly not the party line. I'm not going to say which party it was. You can figure it out but but why, why why would you say that? 00:12:33 Dave OK so take. 00:12:33 Dave A step back here, right? We're all data consultants, or we were at some point in our life and probably most of the listeners are. And if you've been doing this, I've been doing this since the mid 90s in college and when I first started I had an internship with a consumer package. Good company, they made candy. 00:12:52 Dave Hours and they said, hey, we wouldn't want to do an internship and take a look at our data and figure out where is the best spot to put candy on a shelf so that we sell more candy to kids, right? So we used data for that at the time that was known as business Intelligence in the industry. Nowadays business intelligence means something totally different. In reality, it's really closer to what? 00:13:12 Dave Today we would call data science right? So my tools of choice were SQL, although I didn't know what SQL was at the time and we had this goofy SQL engine and and essentially something called ESP's, which is roughly the equivalent of like our or stats package something. 00:13:28 Dave Like that and we kind of looked at data as just, you know I have data and let me find the Nuggets of gold and I'm not going to concern myself with schema and that is I think the biggest problem with data warehouses. But take a, you know a metal layer higher right? Talk to the average business executive like a you know a CTO or CEO. 00:13:48 Dave And tell them, as a consultant, you're going to go in and build them a data warehouse. 00:13:53 Dave Instantly,...

In this episode, Frank and Andy speak to Carlos Chacon about data community, family, and messy data in legacy CRM systems.Transcript00:00:00 BAILey Hello and welcome to data driven, the podcast where we explore the emerging wait a tick. This is the premiere episode of Season Five. Can you believe it? Data driven started four years ago this month. 00:00:14 BAILey Up until last season, we had a human doing the voiceover work. That is until she was replaced by an AI. Yours truly. 00:00:23 BAILey In this episode, Frank and Andy speak to Dave Wensel about why you don't need a datawarehouse. We're starting off the new season with a bit of contrarian tone. 00:00:33 BAILey It's a lively back and forth conversation that runs contrary to prevailing wisdom. Don't say we didn't warn you? Now on with the show. 00:00:41 Frank Hello and welcome to data driven. The podcasts were we wait a minute. We've been saying this Andy for four years now. Can you believe it? 00:00:48 Andy Four years, that's crazy talk. 00:00:52 Frank That's just craziness. So I think when you and I first talked about this and that was that fateful, I think it was December was right after Thanksgiving. But before Christmas, I was thinking about starting a podcast and as a data scientist, I needed someone. 00:01:01 Andy Yeah, yeah. 00:01:09 Frank That was a data engineer that could kind of round out the talent there and and and and obviously I wanted someone I knew, liked, and trust. 00:01:11 Frank Found out. 00:01:11 Frank  00:01:22 Frank And so it was you. 00:01:25 Andy Well, I'm just glad all of the real smart data engineers you knew were busy. That's all I got to say. 00:01:25 Frank Much. 00:01:30 Frank Ah, no man. You were the first one. I reached out to and the only one I would have done it with it. So I was delighted when you said yes because starting a podcast can sound like a daunting thing, particularly if you haven't done it before. 00:01:44 Andy Yeah, neither one of us really had. And gosh, it's it's worked out. What are we up to? 180,000 downloads or something? I mean that's. 00:01:52 Frank Something. 00:01:53 Frank Like that about hundred 8000 downloads. I mean, we're not Joe Rogan, but that's OK, Yep. 00:01:55 Yeah. 00:01:57 Andy No. 00:01:59 Andy Yep, Yep. 00:01:59 Andy Yep. 00:02:01 Frank But you know what, we we we've impacted. I think the community in a significant way. We've we've done a number of things we've we've innovative how we podcast. 00:02:12 Frank Uh, we we've actually managed to keep a good cadence with some exceptions. 00:02:18 Andy Yeah, thanks. 00:02:19 Frank You know, we we finally did earlier this year or late last year, kind of fulfill our vision of it being data driven TV when we actually interviewed guests on. 00:02:27 Andy Yes. 00:02:32 Frank On video. 00:02:33 Frank And that was that actually delayed the launch of the show by about three months. 00:02:38 Andy It did but also uhm. Yeah, that was interesting, but you know it's typical software development, right? You release a feature and then you debug it. The I have this saying about that Frank. All software is tested some intentionally. 00:02:52 Frank Sometimes. 00:02:53 Andy Right? 00:02:56 Frank I love it, but I also like how, how, how both our careers have evolved over the last four years. And dumb, you know, this being the premiere episode of Season 5 and we have something special lined up, but I'll get to that in a minute. 00:02:58 Andy Hello. 00:03:03 Andy Oh gosh, itch. 00:03:11 Andy June. 00:03:12 Frank You've progressed in your career. We, you and I've worked on some some projects together or virtual Summit. What we're calling Ring Gate, which will announce very very soon and and but. But most of all, is been my kind of skilling up in transition into data engineering myself. 00:03:29 BAILey Ehm 00:03:31 Frank Which was something that when I joined, so this is just a job update about a year ago. I I left the role of Microsoft kind of field sales and I went into the Microsoft Technology Center stick with me. There's a point to this story and basically I was at the rest in MTC. 00:03:52 Frank And basically I was the AI guy on my my my field sales team, but I didn't really have deep knowledge of kind of the typical typical data engineering pipe work that goes into that role and basically my my. My then manager said you know he's like hey, you know, if you want this role, you've got a skill. 00:04:12 Frank And skill up I did. And with Andy's mentoring and a bunch of other folks that helped me kind of skill up on our the data engineering side. I looked at it this morning. I'm like 88 hours on Pluralsight. 00:04:25 Frank Wow, that was from mid may till we're recording this on April 30th. So just about a year 88 hours right now tracking on about 200 four 205 consecutive days of getting on LinkedIn. I'm not on LinkedIn on Pluralsight, LinkedIn learning. I also have a number of courses too. 00:04:31 Andy Yeah. 00:04:43 Frank Uh, that is something I'm proud of in terms of career evolution. 00:04:47 Andy Absolutely Frank, you should be. How many cirts are you up to now? 00:04:50 Frank I 87. 00:04:53 Andy Slacker. 00:04:54 Frank I know, I know. 00:04:54 Frank Know, I know. 00:04:54 Andy I think I've got 4. 00:04:56 Frank Ah, now I know you and I did the data engineering thing, so you have at least 11. 00:05:00 Andy That's true, that's true. We did that one and you know that was it's just. It's just been a nice journey and I'll take credit for this. 'cause 'cause I can I was. I was actually pestering you years ago. We've been friends since 2005 and we started doing. 00:05:20 Andy Code camps here in the Richmond area. 00:05:22 Andy Together and co-founded RE co-founded Richmond SQL Server Users Group and you know, worked with the net users group and stuff. And I told you as soon as I saw some of your graphic art and Frank would do a keynote for the Richmond code camps and every time he would make movie posters, the one that. 00:05:41 Frank Oh yeah. 00:05:42 Andy Still sticks out is 1 called devs on a plane. 00:05:45 Frank Ha ha ha. 00:05:49 Andy Oh yeah, I loved that one that was so so cool and. 00:05:49 Andy And that was. 00:05:49 Andy  00:05:54 Andy You know I saw the graphic arts part of it and I just knew I said you, you'd be really good in analytics and data visualization. You should get into by and you were busy doing other stuff which was cool. You were good at that too. It wasn't, you know you. I don't know of anything you've done that you haven't mastered. By thank you. You know you when. 00:06:14 Andy Things took a took, uh, started taking a turn for you in your first rodeo at Microsoft. You got into it and and took off with it. I don't. I won't tell the story well, but you just really turned around. You focused on data and. 00:06:32 Andy You know, I'll say this Frank. I was right. 00:06:35 Frank Well, with that he totally I. I think if anything I took away is I should have listened to Andy 10 years earlier. 00:06:36 Dave You aren't very good. 00:06:40 Frank Uhm? 00:06:41 Frank And that that that that is something that that that that's the big takeaway we'll talk about, kind of that journey. 'cause I think that's worth kind of talking about. And I think one of the things we you, and I've been bouncing around is kind of interviewing each other. 00:06:46 We 00:06:55 Frank Like in asking one of us those those those questions we have, so we definitely will do that, but not today kids. 00:06:55 Frank Yeah. 00:06:55 Frank  00:06:59 Dave We need to. 00:06:59 Dave Need to. 00:07:02 Andy Today, do we have Dave? 00:07:02 Andy Today do Dave. 00:07:03 Frank Today we have a special guest we have Dave Wentzel. Dave Wentzel is a was a peer of mine when I worked at the Microsoft MTC and that reminds me, I no longer work at Microsoft 2 weeks ago was my last day. I turned in my second blue badge. 00:07:05 Frank Yeah. 00:07:05 Frank  00:07:18 Frank And I joined a startup called electrify. We'll talk about them a later day, but I'm so excited to have Dave here. Dave is the data in AI architect out of the Philadelphia Microsoft Technology Center, and he's an awesome guy. Awesome, got to work with. I worked with him when I was in field sales and I worked with him when I was in the MTC organization. 00:07:38 Frank It is April. It was a privilege and honor Dave to have you as a colleague, and it's once again a privilege and an honor to have you here as a guest on data driven. 00:07:46 Dave Well, thank you so much, appreciate that. 00:07:47 Andy Welcome Dave. 00:07:49 Dave Thank you. 00:07:51 Frank So, uhm, so for folks that don't know what the MTC is. Shocking that there are actually people that don't know what that is, what? What is the MTC? 00:08:00 Dave So basically we're a free service to our customers and I'm a data and AI technology architect. We talked to customers about data and it could be anything from just, you know. Hey, here's what we're doing. State of the art in Azure with. 00:08:16 Dave With data, but it could also be architectural design sessions where we talk to customers. Our customers bring us their architectures, and then we kind of get it with them. Give them the pros and cons, alternative ways of thinking, and then what I really enjoy doing is hackathons with customers and workshops and just you know, helping them to learn without just. 00:08:37 Dave Taking a course somewhere so actually using their data and then I guess I'm roughly a data scientist, so we also do design thinking sessions and those are absolutely a lot of fun. 00:08:48 Dave We did one at the MTC with CSL Behring a couple years ago and it actually won a Forrester Award. So I'm very proud of that one. And yeah, it's it's a. It's a lot of fun and it's a good way to bring to have executives and business people understand the actual capabilities of data science. And then within two days be able to come up with a use case. 00:08:55 Andy Oh wow, wow. 00:09:08 Dave And actually build a prototype out a lot of fun. 00:09:11 Frank Yeah, the NPC's are definitely like Microsoft Secret weapon in terms of how 'cause you know. Although I will say and because we were in the DC and we dealt with a lot of government contracts, we could not say that they were a free service. They were and already included paid for service. 00:09:26 That's. 00:09:26 Dave Much, much better said yes. 00:09:28 Frank I I 'cause I said free once and I got kind of slapped. 00:09:31 Frank On the hand, say that. 00:09:34 Frank But you know it, it really is something that if you do have a Microsoft account team and you are encountering any kind of questions or or whatever, and it's not strictly technical, there's also pretty good. You know, we basically wouldn't engage with the business development, business decision makers. 00:09:52 Frank Technical decision makers all the way from kind of like you know, hey, this is what Azure can do. This is what data can do for you all the way down to OK. What's your problem? Let's build something out, give you 3 days with one of the top Notch architects in the. 00:10:04 Frank Space and. 00:10:07 Frank You know, boom, you know we knock it out and and you know I I enjoyed it you know had this opportunity not come I would have I would have gladly stayed another. You know 5-10 years of the MTC. Like a lot of people do, and it's a fun organization. So with that in mind, today we're going to do something a little different. We're kind of doing the. 00:10:27 Frank A contrarian approach is that right, Dave. 00:10:29 Dave Yes, exactly. 00:10:31 Frank So this this has actually come up one of my last. This is one of the things that intrigued me about about your idea for the show was this came up when I was working with a we'll just call it a large governmental agency known for its. 00:10:42 Frank Birds. 00:10:42 Frank  00:10:43 Frank Tape. 00:10:44 Frank That that should keep it generic enough. They basically came to us and say we want Synapse. We want a data Lake. We want this. We want that. And I was like, OK, well how much data you're talking about. And like we have maybe you know 5 maybe 20 gigs of data. 00:11:02 Frank And I'm like, uh, OK, tell me what are you trying to do? And ultimately I kind of pitched the idea like look, you know you don't have that much data right to make data bricks. 00:11:14 Frank But you really want it so. 00:11:17 Frank If you really want it, I won't stop you, but I think it's kind of overkill. I think you're taking instead of using a steak knife to cut the steak using a chainsaw. 00:11:25 Frank And. 00:11:27 Frank You know they kind of came back and ultimately what won the day was they already they couldn't get approval for whatever we recommended 'cause it didn't get stamped by there. 00:11:37 Frank They're people for security usage yet, and things like that so they end up doing kind of the right thing because of their own bureaucracy, which. 00:11:44 Frank It's kind of weird. It's kind of like dividing by zero and seeing the universe fold in on itself. 00:11:50 Frank But UM, so the topic of today is kind of like no, you don't need a data warehouse. Did I get that right? 00:11:58 Dave Exactly, that's what I believe in, and I believed in it since I was in college and I first learned about data warehouses. I'm not saying data warehouses are always bad, they definitely have their. 00:12:10 Dave Use cases, but in 2021 when we're talking about advanced analytics and we're trying to tell customers you need to be more predictive than prescriptive. 00:12:19 Dave The data warehouse really doesn't deliver. 00:12:23 Frank Really, how so? 'cause? That's that's totally not the power. Certainly not the party line. I'm not going to say which party it was. You can figure it out but but why, why why would you say that? 00:12:33 Dave OK so take. 00:12:33 Dave A step back here, right? We're all data consultants, or we were at some point in our life and probably most of the listeners are. And if you've been doing this, I've been doing this since the mid 90s in college and when I first started I had an internship with a consumer package. Good company, they made candy. 00:12:52 Dave Hours and they said, hey, we wouldn't want to do an internship and take a look at our data and figure out where is the best spot to put candy on a shelf so that we sell more candy to kids, right? So we used data for that at the time that was known as business Intelligence in the industry. Nowadays business intelligence means something totally different. In reality, it's really closer to what? 00:13:12 Dave Today we would call data science right? So my tools of choice were SQL, although I didn't know what SQL was at the time and we had this goofy SQL engine and and essentially something called ESP's, which is roughly the equivalent of like our or stats package something. 00:13:28 Dave Like that and we kind of looked at data as just, you know I have data and let me find the Nuggets of gold and I'm not going to concern myself with schema and that is I think the biggest problem with data warehouses. But take a, you know a metal layer higher right? Talk to the average business executive like a you know a CTO or CEO. 00:13:48 Dave And tell them, as a consultant, you're going to go in and build them a data warehouse. 00:13:53 Dave Instantly, that's a political statement you just made. Data warehouses have connotations of you, know risky projects over budget projects as far as time and money, and...

In this episode Frank and Andy have a chat with Chris Gherardini on the role of data in ERP and CRM Systems.Transcript00:00:00 BAILey Hello and welcome to data driven, the podcast where we explore the emerging fields of data science, machine learning and artificial intelligence. In this episode, Frank and Andy speak to Chris Gardini about the role data plays in ERP and CRM systems. 00:00:17 Frank Hello and welcome back to data driven. The podcast where we explore the emerging fields of data science, machine learning and artificial intelligence. If you like to think of data as the new oil, then you can think of us like Car Talk because we focus on where the rubber meets the road. Although there's not much of a road trip usually on this virtual road trip. 00:00:37 Frank Is Andy Leonard? How's it going, Andy? 00:00:40 Andy Hey Frank, it's going really well. How are you doing? 00:00:43 Frank I'm doing alright, you know, with the exception I might have to buy a new desktop computer. 00:00:48 Andy Ah, I have not. 00:00:49 Frank Possibly, or power supply. Literally. We were on this call and all of a sudden everything froze and I was like Oh well, blue screen. Big deal. 00:00:59 Frank And then when I went to power it back on. 00:01:03 Frank It just kept keeps beeping so I'm like oh, fee something something hard so I'm going to have to do some search engine work and possibly get a new power supply or something. 00:01:06 Andy Codes. 00:01:13 Frank Fortunately, micro center. 00:01:14 Andy Everything is. 00:01:17 Andy Go ahead. 00:01:17 Andy  00:01:17 Andy I've gotta say Frank, everything is figure out able right? 00:01:21 Frank Everything is figure out able because of my experience with clear DB and all sorts of other drama. I have multiple backups of just about everything it if if you know so it's it's. It'll be an inconvenience, not a tragedy. 00:01:32 Andy Outstanding. 00:01:37 Frank But every opportunity to come back from a complete backup failure is an opportunity to learn. 00:01:47 Frank And, uhm, Speaking of opportunity, it's really good timing that this guest is here because I as folks know I work at the Microsoft Technology Center in Reston and recently. 00:02:00 Frank There was a, uh, someone we were on this engagement and it was very heavy into dynamics and uh, dynamics is one of those things. I haven't really looked into Andy and I have been experimenting with power apps and power platform, mostly to kind of help automate a lot of our content. 00:02:21 Frank Production. 00:02:22 Chris This is crucial as we. 00:02:24 Frank Continue to put the final touches on our secret project, but this guest here is an expert in dynamics as well as various ERP solutions. 00:02:36 Frank And he's from Saint Louis, and his name is Christian. I'm really blowing this intro here. His name is Cristiano Gardeny. Did I pronounce that right? 00:02:48 Chris Cristiano Guardian yeah, he pulled our sounded out there so. 00:02:48 Frank OK good good. 00:02:51 Frank He did fine, got it. Sorry about that. He's the president and owner of Turnkey Technologies and they are a Microsoft partner. I did looking around on their website and they are basically they provide development, analytics, training and support services for Microsoft Dynamics. 00:03:09 Frank If you don't know what Microsoft Dynamics is, a lot of people don't. In the data world tend to just know it tangentially. It's basically Microsoft CRM system, so welcome to the show, Cristiano. 00:03:21 Chris Thank you very much. Thanks for having me. 00:03:25 Frank It's good to have you here, and so what's the weather like in Saint Louis right now? 00:03:30 Chris Ah, it's beautiful and sunny. Today it's about 64 degrees Sun index is just right around A5, so it's actually it's actually nice and better than the the ice. We had a couple weeks ago so. 00:03:38 Frank Knife. 00:03:42 Andy Yeah, it got bitterly cold out there for about a week, didn't it? 00:03:47 Chris That's right, I happen to be in Florida that entire week. Everybody couldn't believe I missed all the fun. So with the IT was good, it was a good week to be gone. We left Saturday. We came back Sunday was 52 when we got back and the 9 inches of snow had melted and. 00:03:51 Chris Unfelt 00:03:51 Chris  00:04:00 Chris So yeah, perfect timing. 00:04:02 Frank Nice, now you have excellent timing Sir. Not only in just the fact that dynamics is kind of coming up, coming up on my personal radar, but also in terms of avoiding bad weather. My first question is and I'm calm. Still a new bit dynamics. I learned a lot just by working on this. 00:04:23 Frank One engagement we had DMTC. 00:04:27 Frank Is Dinah. 00:04:28 BAILey Mix. 00:04:29 Chris So Dynamics Dynamics is the biz apps. I mean, you said CRM. It's more than CRM. But today dynamics represents a family of products. You know, the legacy on Prem products, Dynamics AX Dynamics, Navy Dynamics, GP, Dynamics, SL and then the current dynamics 365, which encompasses both you know financing. 00:04:49 Chris Operations, which was the dynamics AX and then Dynamics 365 business Central which was the Navy? So those are both ERP products. 00:04:57 Chris And then Dynamics customer engagement, which is the CRM platform so so dynamics is a collection of biz apps. It's a family of products and you know today we we focus on the two dynamics 365 ERP products and the CRM. The customer engagement product. So great solution, but it's a platform also it. 00:05:15 Frank That's what I noticed, and folks that are listening. They're like this is a data science kind of data engineering show. Why the heck are you talking about? 00:05:25 Frank Dynamics but dataverse the data models, which are one of the things that blew me away, was on this one demo is that there's a. There's a button where you just basically can dump out all your cream data. 00:05:38 Frank Or all your data and dynamics out to a data Lake. 00:05:42 Chris That's absolutely right, Azure. So if you think about the challenges where you move from a legacy system to the cloud, maybe all the data doesn't move and you need to combine it in an Azure data like a data warehouse, and certainly all those tools and all the analytics are all part of the platform. That's just it touches dynamics out of the box, so it's quite a quite a degree of efficiency. 00:06:02 Chris There. 00:06:03 Frank Interesting. 00:06:05 Frank So one question I have about dynamics is it's history now. You mentioned Dynamics AX and a couple of other letters next to the word dynamics. 00:06:17 Frank Once Upon a time when I worked when I lived in Richmond, there was a guy who used to do a lot of work with that technology. 00:06:24 Frank Back and it was Solomon in Great Plains is that is that the same thing is that the lineages from it. 00:06:30 Chris That's correct, that's correct, that's correct. And so the US based products was Great Plains software out of Fargo ND, and a gentleman named Doug Burgum owned that company privately and then his family. They bought the the Salomon product line as well and then in 2001 is when Microsoft bought Great Plains. 00:06:32 Silly. 00:06:46 Chris Software, so that's kind of how they consume the North American products and then a year later Microsoft went to Europe and bought Dynamics AX and Dynamics Navy. It was called Exapta in division so and that became a second big purchase and instantly at 2002 they had four ERP products and they're in the biz apps space like they've never been. 00:06:48 Chris OK, so. 00:06:48 Chris  00:07:06 Chris Before so. 00:07:09 Frank Ah OK, that's interesting. 'cause I I now I know why. There's a huge Microsoft campus in Fargo. 00:07:16 Chris Yes, yes. 00:07:18 Chris It's beautiful campus. 00:07:19 Frank Uhm? 00:07:21 Frank Yeah, I haven't been there, but I definitely maybe one day I'll end up going but the the other question I have for you. You keep saying biz apps when you say biz apps? What exactly do you mean like what? 00:07:32 Chris Sure, so I'm. I'm a technical guy. I'm engineering comp SCI math and there's two directions you go and even those curriculums you go **** **** or you or business applications and so business applications in our context is, you know, we're we're business process automation again, whether it starts in an ERP from a quote to cash or a procure to pay those. 00:07:33 Frank What what comic sub is that like? 00:07:52 Chris For business processes and the business applications support. 00:07:56 Chris Work, transactional processing or even non transactional if you think about context of a lead to opportunity to customer type of flow where you start even earlier in the process, but it is it's process automation and exception management and workflow and approvals and can go to the NTH degree of complexity. But but it's biz apps in that categorization. 00:08:16 Chris So as opposed to scientific apps or development tools or games, for example, right, we're focused on businesses, not on residential applications. 00:08:26 Chris That makes sense. 00:08:27 Andy It does, yeah. 00:08:28 Frank Do you have any questions Andy? 00:08:30 Frank This is so awkward being on on this recording, but not being live. So for those that are watching or listening or maybe watching. 00:08:35 Andy Yeah, I don't work. 00:08:40 Frank Go ahead. 00:08:42 Andy Yeah, I'd like to apologize to Chris, especially thank you for coming on here, but we're a little off because of the video. I I I never shaved but. 00:08:50 Chris No worries. 00:08:55 Andy I would have I. I have a face for radio. I think in audio, so that's what's throwing us just a bit. Although Frank and I do live streaming and stuff, can you tell us a little bit more about about what your company does, how how your team implements these biz apps? 00:09:14 Chris Sure, so so turnkey is a is a direct Microsoft distributor, so we distribute dynamics licensing and then we provide 100% of the professional services to to plan to implement, to convert, to customize, to integrate. So really, if you think about there's two parts of business, there's the licensing side and and then the services side, which we've got about 60 team members. 00:09:35 Chris These days, and as you think about, you know, delivering an implementation once the solutions been kind of articulated as project management methodology, really drive the success of the project, and so there's an intimate, detailed plan. 00:09:48 Chris Where we use Microsoft project and and we plan in very very low levels of detail for project execution. Everything from you know, the initial phases of analyzing and requirements and then through design and then through development and through deployment. And then we take people live, but it's a very predictable methodology that's used for project delivery. 00:10:08 Chris Around the ERP products, so that's that's the first spot, so. 00:10:12 Andy You know that sounds an awful lot like what what we do when we're doing data warehousing as well and and similar work I I would say. 00:10:22 Chris Absolutely the disciplines or you know, are kind of agnostic when you think about project methodology. 00:10:27 Andy So just curious, what is a typical engagement about how long does a typical engagement last? 00:10:34 Chris So and again, we sell 2 flavors Dynamics, 365 business Central. We talk about small, medium and those projects could. 00:10:41 Chris Maybe three months for somebody small coming off of a QuickBooks. It's just financials, but it could be six months for somebody that's implementing distribution and manufacturing, for example. So depending on complexity, three months is normally the least amount of time, and in 6 plus months for the business, central on the finance and supply chain, which was acts as a much larger applications targeted at, you know. 00:11:01 Chris Mid market enterprise organizations. Those projects are typically not being delivered in less than six months. Frankly, and I've got some that we see them take 12 to 18 months for example so. 00:11:13 Chris No, it's gonna save us the the project management requirement. Because of that duration and scope and girth. You really people get lost right? Anyway, I'm sorry please. 00:11:23 Frank Oh no no no no no so. 00:11:26 Frank The the, the ERM or the ERP systems like Dave require. So Once Upon a time I was working for a large German chemical company, right? And of course it's a German company, so they used SAP and they hired a bunch of consultants and consulting firms to build out the system. But when they. 00:11:46 Frank Rolled out SA P R2. I think it was. 00:11:49 Frank Is. 00:11:50 Frank Apparently there was not. 00:11:52 Frank A lot of project discipline now I'll just. I'll just leave it there. 00:11:57 Frank And it basically shut down some of their plants because there were just things were not coming in the way they were supposed to. So So what? 00:12:06 Frank How? 00:12:07 Frank How does ERP systems? How do our ERP systems? Kind of you know if they're done wrong? It sounds like they can create a big mess if they're done right, they could really optimize operations. 00:12:19 Frank Uhm? 00:12:21 Frank I mean is that, is that true? 'cause I I kind of saw, I mean. 00:12:23 Frank That was just a Horror Story. It's true. 00:12:24 Chris It's true, it's true. It's like me giving you lousy directions to drive from Florida to Washington, and it takes you six months and it should have taken you three days. There's a great example. I poor guidance and poor methodology, is it stretches it out. Things are missed, costs are out of control, but again, if it's done correctly through a thorough analysis. 00:12:34 Frank Point 00:12:45 Chris Then you really define scope. 00:12:46 Chris And you know you have good business process visualization. Then you drive to deliver those processes to that point and and it's. And again, if you do a good job and my team does a great job of of really articulating business process, you gain great efficiency and Moreover you get capacity to turn up the juice again, you can take more volume and you've you've articulated. 00:13:05 Andy Nice. 00:13:06 Chris And really gotten the inefficiency out of the process, and Even so, you don't touch every transaction you touch the exceptions so you get a lot of leverage. But then you're correct if it's done right, if it's done. 00:13:16 Chris Wrong, right? It maybe it's broken, maybe it falls off the table and somebody has to pick it up and has a manual step because they didn't think through it thoroughly. And and now you've got a bigger problem, right? And sometimes companies don't retire the old systems and they're working in two places, so there is a lot of negative that can come for people that don't manage and plan and and deliver these systems. And and again, we've been doing this. 00:13:37 Chris For 27 years, so we're pretty pretty good at it so. 00:13:38 Andy Will you? 00:13:40 Andy Wow. 00:13:42 Andy Well, you you mentioned Chris that you know when you were giving examples of the typical projects links that customers are are often migrating from other platforms. Other ERP ish. Perhaps they do some of the ERP work and then you're moving them into dynamics. And as you say. 00:14:02 Andy Once they get in there, they're going to be able to scale. It sounds like horizontally and and cover more territory than the system that they were using and also scale vertically. Like you said, turn up the juice and and just grow is that. Is that...

In this livestream, Frank and Andy talk about geothermal energy, solar power, and why you should never say not to Clippy.

In this episode of Data Driven, Frank and Andy speak with Peter Voss about Artificial General Intelligence, Personalizing Personal Assistants, and MotorcyclesSponsorSponsor: Audible.com - Get a free audio book and support DataDriven - visit thedatadrivenbook.com!Guest BioPeter Voss is the world’s foremost authority in Artificial General Intelligence.His company Aigo (https://www.aigo.ai/) has created the world’s first intelligent cognitive assistant.Aigo was funded with a personal investment of $10 million dollars. They currently manage millions of personalized customer service inquiries for household name-brandsNotable QuotesAigo is Peter's company. BAILeY's Introduction (00:00)The east coast has been blanketed with snow. (01:30)The Expanse books (03:00)Coding for curiosity? - Frank (11:50)"Models don't dynamically learn." - Peter (13:00)Three waves: Logic programming, Deep learning / neural networks, cognitive architecture / intelligence (14:00)Intelligence v. sentience? - Frank (15:50)What about bots being "led astray?" - Andy (18:30)On programming morality... (21:30)AI Safety is a better description - Peter (22:30)Asimov's three laws of robotics - Frank (23:15)On delimmas - Peter (24:15)"Morality should be about human flourishing." - Peter (25:15)Are we using digital means to do something analog? - Andy (27:55)Peter is trained as an electronics engineer. (28:05)"Context is always super-important." - Peter (28:30)"You need a feedback system." - Peter (30:00)AIGO is Peter's company. (31:00)The three meanings of personal. (34:00)"Exo-cortex" (33:50)On context switches (38:30)Did you find AI or did AI find you? (41:00)"I took five years off to study..." - Peter (43:00)What's your favorite part of your current gig? (44:10)When I'm not working, I enjoy ___. (45:00)I think the coolest thing in technology today is ___. (45:30)I look forward to the day when I can use technology to ___. (46:25)Something interesting or different about yourself (47:00)How Not to Die (48:00)Where can people learn more about Peter? (49:00)Book reading / listening recommendations? (49:00)The Mind's I (50:00)Peter's articles on Medium (52:00)Get a free audio book and support DataDriven - visit thedatadrivenbook.com! (00:00)TranscriptThe following transcript is AI generated.00:00:01 BAILeYHello and welcome to data driven.00:00:03 BAILeYThe podcast where we explore the emerging fields of data science, machine learning, and artificial intelligence.00:00:11 BAILeYIn this episode, Frank and Andy speak with Peter Voss, peterboat.00:00:15 BAILeYPeter Voss is the world's foremost authority, an artificial general intelligence or AGI.00:00:21 BAILeYIn fact, he is the one who coined the term in 2001 and published a book on the topic in 2002.00:00:28 BAILeYHe is a serial.00:00:29 BAILeYAI entrepreneur technology innovator who has for the past 20 years, then dedicated to advancing artificial general intelligence.00:00:38 BAILeYToday he is focused on his company, IGO, which is developing and selling increasingly advanced AGI systems for large enterprise customers.00:00:47 BAILeYPeter also has a keen interest in the interrelationship between philosophy, psychology, ethics, futurism and computer science.00:00:56 BAILeYI think you will find this interview a fascinating look at the future of AI.00:01:01 BAILeYNow on with the show.00:01:05 FrankHello and welcome to data driven, the podcast where we explore the emerging fields of data science, machine learning and artificial intelligence.00:01:13 FrankIf you like to think of data as the new oil, then you can think of us like well.00:01:18 FrankCar Talk because we focus on where the rubber meets the road and with me on this epic virtual road trip down the information highway because we're still locked in quarantine.00:01:29 FrankAs always, Andy Leonard.00:01:30 FrankHow's it going and?00:01:31 AndyGood Frank, how are you?00:01:33 FrankI'm doing well.00:01:34 FrankWe had a bit of snow.00:01:36 FrankWe're recording this on Monday, February 1st and the East Coast has been blanketed in some snow.00:01:37 Peter VossYes.00:01:45 AndyYeah, we got more than we've gotten, probably since 2018 or so. About four inches here in FarmVille and then almost an inch of ice on top of that, which always makes it fun, right?00:01:58 FrankYeah, the ice is worse than the snow on.00:02:00 FrankBasically so I went out, walk the dog today and one of the dogs and it was crunch, crunch, crunch.00:02:06 FrankSo there's a nice layer of ice over everything which is going to make driving later fun, but I do have.00:02:13 FrankI do have the an all wheel drive car which is fantastic.00:02:17 FrankI will never not own one of those again.00:02:19 AndyNice.00:02:21 FrankYeah, you've seen it's the CRV.00:02:23 AndyYes, yeah, it's nice you did well.00:02:26 FrankI dubbed it the Rocinante.00:02:31 AndyIn case our listeners are not familiar with that, with what Frank is referring to, it is not the old novel.00:02:40 AndyFrank is not tilting at windmills instead.00:02:44 AndyAnd if I got that reference wrong, correct me.00:02:46 AndyI'll just edit that out.00:02:47 FrankOh, you are right, it's from this AM Oh my God, I forgot new book on Cody.00:02:48 AndyNot sure.00:02:51 AndyDonkey Quixoti wasn't.00:02:53 FrankYeah yeah Cervantes I was gonna say from Cervantes book and I'm like oh what was the name of that?00:02:53 AndyYeah so.00:02:59 FrankWhich is the opposite of how most people think, but that's what I do.00:02:59 FrankOK, good.00:03:02 AndyThere we go, but it is actually a reference to both the books and a series, The expanse of which Frank and I are great fans, so.00:03:12 FrankAwesome, but you know who's not covered in snow today.00:03:13 AndyI like it.00:03:15 AndyWho is not covered in snow their guest.00:03:16 AndyOur guest.00:03:18 FrankWho lives in?00:03:18 FrankYeah.00:03:20 FrankI'm assuming sunny or Smokey I guess depending on the time of year California Peter Voss Peter welcome to the show.00:03:29 Peter VossThank you, yes, it's we've got snow on the mountains here, but it's very sunny.00:03:36 Peter VossIt's it's nice and we have a lot less smog these days.00:03:41 AndyVery good.00:03:41 FrankNice so you are the.00:03:46 FrankOne of the world's, or if not the world's foremost authority in AGI or artificially artificial general intelligence, and I believe you are the one that coined the term.00:03:58 Peter VossYes, correct and 2001 myself and two other people. We coined the term artificial general intelligence AGI to really distinguish the kind of work we were doing from, you know, specialized narrow AI which is.00:04:18 Peter VossPretty much what everybody else is doing.00:04:20 Peter VossThe original dream of artificial intelligence was of course, to have systems that can think and learn the way humans do, but that turned out to be a lot lot harder than people thought.00:04:31 Peter VossSo over the years, AI really turned into narrow AI using human ingenuity to figure out how to solve one particular problem, like playing chess or.00:04:41 Peter VossContainer optimization or medical diagnosis and then to write a program or to train data to do that to solve that particular problem.00:04:51 Peter VossBut it's really the external intelligence of the program or the data scientists that is then encoded.00:04:58 Peter VossTo solve that problem, whereas we wanted to get back to the original dream of having a thinking machine that it can figure out how to do these things and and learn more humans do so.00:05:09 Peter VossThat's why we felt we had to.00:05:12 Peter VossYou know, coin a separate term to distinguish it from narrow AI.00:05:16 FrankInteresting.00:05:18 FrankSo for years, AGI has been.00:05:21 FrankKind of thought the stuff of science fiction.00:05:24 FrankI think there was a lot of optimistic people like you said that thought we would have it by now.00:05:29 FrankI know this is kind of a loaded question, but one do you think we'll ever get there and two, what's the sort of time frame we're looking at?00:05:38 Peter VossYes, it's an interesting question, so absolutely, I believe it's it's.00:05:42 Peter VossPossible, and in fact the reason we got together. We wrote a book called Artificial General Intelligence. As I said in 2001 was because we believe the time is ripe to get back to this original dream that the technology had advanced enough. Both hardware and software technology and cognitive psychology. Cognitive science.00:06:02 Peter VossThat we now understood enough and had fundamentally had the tools in place to tackle this problem and to say.00:06:11 Peter VossSo I I absolutely believe that it can be solved soon, and in fact we will leave.00:06:18 Peter VossWe are on on the way of solving this problem now in terms of time frame.00:06:24 Peter VossNormally the way I answer this question is I don't measure it in time.00:06:28 Peter VossI measured in dollars.00:06:31 FrankI like that time is money, so I guess.00:06:34 FrankThat's a reasonable correlation.00:06:35 Peter VossYeah, and and the reason I do, I say that is because.00:06:39 Peter VossStill, today almost nobody is working on AGI. You know, 99% of all the effort in artificial intelligence is still on narrow AI, so if this continues, it will take a long long time for us to reach human level AGI. But if that changes.00:07:00 Peter VossAnd you know the kind of funding that's going into deep learning machine learning suddenly was applied to AGI.00:07:06 Peter VossThen I think it could easily happen at less than 10.00:07:09 BAILeYYes.00:07:10 FrankOh wow.00:07:11 AndyVery cool, so I'm curious is there any like lead in does?00:07:16 AndyDoes time and money invested in deep learning and narrow AI?00:07:23 AndyDoes any of that help move the cost?00:07:25 AndySay further the cause for AGI?00:07:29 Peter VossSlightly, I believe, you know.00:07:32 Peter VossObviously, any advances in languages and data collection in hardware development and the general experience.00:07:42 Peter VossIn that sense, it does help it.00:07:44 Peter VossBut in another sense, it's actually the opposite.00:07:46 Peter VossIt's actually hindering it because a whole generation of software engineers and data scientists are now coming into the field, believing that deep learning machine learning is a way to do it.00:08:00 Peter VossAnd all we need is more data, more horsepower and will solve this problem.00:08:05 Peter VossAnd that's I think barking up the wrong tree, and it's a it's a dead end.00:08:10 Peter VossSo in that sense, what's happening today with deep learning?00:08:12 Peter VossMachine learning is actually counter to achieving.00:08:16 AndyGI interesting very interesting.00:08:20 FrankWas it always that way or it's just the way the market kind of went frenzied over just narrowed AI?00:08:26 Peter VossWhy?00:08:26 Peter VossWell, we've had several windows of AI.00:08:30 Peter VossYou know the the disappointments over the decades.00:08:33 Peter VossYou know, when we had expert systems, people believe that you know they would really, you know, show real intelligence and then it kind of fizzled out.00:08:42 Peter VossAnd so we've had.00:08:43 Peter VossWe've had various windows, and but of course, deep learning machine learning has been so spectacularly successful in several areas.00:08:52 Peter VossYou know, image recognition, improving speech recognition, and you know various other fields that just, you know, it's the only game in town as it has been very, very successful.00:09:04 Peter VossBut people are also starting to realize what the limitations are of it.00:09:11 Peter VossSo yeah, it's it's kind of at the moment.00:09:14 Peter VossThe only game in town, and it has really been successful in many.00:09:17 AndyAreas, So what are those limitations?00:09:20 AndyAnd how does AGI addressing?00:09:23 Peter VossYeah, so fundamentally when you think about intelligence, you know if you think about just common sense.00:09:32 Peter VossIf we talk to a person and we judge them to be intelligent or to be totally non intelligent, the kind of things we expect is that they can learn.00:09:43 Peter VossImmediately that when you say something a, they understand what you're saying and they integrate that knowledge with their existing knowledge so you know if you say my sister's moving through Seattle next week or something.00:10:01 Peter VossThat knowledge needs to fit in somewhere.00:10:04 Peter VossYou know you know the person who's talking.00:10:06 Peter VossYou may know who the sister is, or you may not know who the sister is.00:10:10 Peter VossYou probably know what Seattle is.00:10:13 Peter VossYou may have images of, you know, rain pouring down all the time or whatever, but so you integrate that knowledge.00:10:21 Peter VossAnd if you're not cleared, my maybe the person has two sisters, so then you would ask her, do you mean your older sister you know your younger sister?00:10:30 Peter VossAnd so we expect an intelligent human to basically do.00:10:35 Peter VossYou know what's technically called one shot?00:10:37 Peter VossLearning?00:10:38 Peter VossYou hear something once you see an image.00:10:40 Peter VossOnce you learn that and you integrate it into your existing knowledge base.00:10:46 Peter VossAnd if you're not sure how to interpret it.00:10:49 Peter VossThen you ask clarifying.00:10:50 Peter VossQuestions until you know what it what it is.00:10:54 Peter VossSo you have deep understanding you have disambiguation.00:10:59 Peter VossYou have learning instant learning, one shot learning.00:11:03 Peter VossYou have long term memory.00:11:05 Peter VossYou remember that next week you you know if you paid attention, you will remember that and you have reasoning about.00:11:12 Peter Voss30 now deep learning machine learning as it's done today, really doesn't offer any of those.00:11:20 Peter VossSo if you if you had a human and you told them something and they didn't remember it, they didn't understand that they didn't ask for clarification.00:11:27 Peter VossYou wouldn't think of them as being very intelligent, would you?00:11:33 FrankNo, I mean, my kids are smart, but when I tell them to bring the trash cans back from the street, they'll conveniently forget.00:11:39 FrankBut I, I think I know where you're going with that, yes?00:11:42 BAILeYAll right?00:11:44 FrankBut the question I have, it sounds like you're trying to and I know this is going to be not really good analogy.00:11:50 FrankOr maybe it is you're trying to code for curiosity.00:11:54 Peter VossThat's very much part of it, but you know even deeper is understanding.00:11:59 Peter VossBasically, when you have some input, do you?00:12:02 Peter VossDo you understand you know what the implications are, how it fits in with the rest of the knowledge that you have?00:12:08 Peter VossAnd you know, even that, that's sort of more even more fundamental than curiosity.00:12:13 Peter VossBut yeah, curiosity is then wanting to gather more information, so this is inherently an interactive process.00:12:22 Peter VossYou know, an intelligent person would ask follow up questions you know they would want to kind of.00:12:29 Peter VossFill in the pieces of the puzzle and you know that they can be more.00:12:33 Peter VossIn fact effective in their communication on their or their job.00:12:37 FrankRight so.00:12:37 Peter VossSo yes, that's definitely part of it.00:12:40 FrankSo calling back to your example of someone's sister moving to Seattle you you would ask, you know, I didn't know you had a sister or how many sisters do you have or how many siblings do you have and.00:12:51 FrankWhere is she moving to?00:12:52 FrankWhy?00:12:53 FrankI guess that's kind of.00:12:55 FrankI guess it's all about building that knowledge map inside.00:12:58 FrankYour head or then your head being could be a program I guess.00:12:58 BAILeYExactly.00:12:59 BAILeYOK.00:13:02 Peter VossYeah, and deep learning machine learning really doesn't allow for that at all.00:13:07 Peter VossYou know you accumulate masses of data and you train a model, but that model is then static.00:13:14 Peter VossIt's a read only model.00:13:15 Peter VossYou know, it doesn't dynamically learn, so it may have a sort of a knowledge graph, but even that knowledge graph is.00:13:23 Peter VossIs very opaque, it's.00:13:26 Peter VossYeah, it's not scrutable you know and and this is this is such a big problem with deep learning machine learning that you don't know why it gives a certain response, which is a huge...