As First Lady, she was an active part of the administration, serving as the President's personal emissary to Latin American countries and even sitting in on cabinet meetings. Intense negotiations continue on a possible deal to release dozens of hostages held by Hamas. Appearing on NBC's Meet the Press, Deputy National Security Advisor John Finer
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The information economy has arrived. The world is teeming with innovation as new business models reinvent every industry industry. Inside Analysis is your source of information and insight about how to make the most the exciting new era. Learn more and inside analysis dot com. Inside Analysis dot Com and now here's your host, thro Eric Kavanaugh. All Right, ladies and gentlemen, it's time but the only coast to coast radio show that's all about the information economy. It's called Inside
Analysis. Your host here, Eric Kavanaugh, and I am so excited to have an all star cast today. We've got the Malcolm Chisholm, who's got the receipts on LinkedIn promotions. We've got the data Scott Taylor, who of Meta Meta Consulting. He's amazing, and less but not least, we have Kidstration Data Kate. She knows where you live, she knows where your data lives. She will find you. I suggest you not to make her unhippie. Unhippie. That's what my Russian boss used to always say about the people
who he had stiffed on bills. They are unhippie. That was his favorite term. We're talking all things data. Today's to be a fantastic show and let's not waste any time. I saw this hilarious quote from one of my favorite people in the business, Steve Lucas said data is the new sand I thought that was hysterical because everyone talks about data is the new oil. You have to refine it. It doesn't aval with your find Oh okay, we know all that. But data is the new sand. It's like just gritty
gets on your shoes, winds up in your car. You can't ever vacuum it all the way out. It's just forever with you, little tiny bits of sand. But it makes sense because if you refine it, you can. If you have a last blower, for example, maybe you could do something with that and swip that sand. But let's throw it over to Kate and Stretch of Dedicated. Tell us a bit about yourself and what you think about the importance of data. Yes, absolutely, thanks for having me on
the show. Hello everybody. I'm Kate Strashn. I can't do the accent as good as aerage cant for Russia, but I am the founder of Dedicated. It's a media company that's focused on helping data, analytics, machine learning and AI companies reach their audience on link. Then I help out with brand awareness. I've got my own show called Dedicated on air, which is why I love Eric's background. That says on air reminds me of my show as well. And you know, Eric, I wanted to point out when you
were talking about sand. For some reason, I started thinking of how data is the new water, because I've heard of that, right, all the pipelines and cleaning the water and making sure it all makes sense. Then I thought of mixing sand and water. But then I remembered that I recently put together a video where I actually built a sand castle to demonstrate the importance of
data infrastructure. It was for one of my clients. They wanted to talk about how if you build on poor infrastructure, then all your apps go to crap basically, and it just doesn't work well. And so I built like a really bad sand castle, and then I scrapped it and built a much better one. I had so much fun, and my kids are like, you get to do this for work, and I'm like, yeah, this is what I do for a living. And I explained it to my family.
My brother's like, I still don't know what you do. And I'm like, well, I got paid to make a sand castle today, and he's like, can you just keep making them? Like I'm like, no, it doesn't work that way. It's all about the concept. But yeah, data, I think data is extremely important. I think your question was
what I think about data data is literally my life. I even have a license plate on my car that literally just says data, which gets a lot of attention from passerbys because they start asking like, why do you like data so much? So it's it's been fun. That's good stuff. And of course you help people get the word out about their own personal brands. So you're passionate about brand, You've got to find job branding yourself, by the way, dedicated, I mean, come on, that's good stuff, right,
dedicated? Yes, thank you, thank you. Yeah, I love I love talking about personal branding. I've got a course out on LinkedIn Learning on that topic as well. Or people. I think it's like a forty five minute course of everything I know about personal branding. I compacted it really really tight into a very short course. But yeah, I think personal brand is extremely important in what in any area of data, data management, data analytics, data science. Now AI, right, it's all about standing out,
especially with AI sort of doing all of our work for us. A lot of times, the more personal touch we can put on our work, the better. That's interesting. And you had mentioned before the show that we'd be remiss where we can not mention generative AI. So you've now hinted it generative AI, and I think the key to success with AI is going to be wait for it, data, right, your good quality data. In
fact, let me throw this at you and see what you think. I believe that AI, in a particular large language models represent a second chance for data. And what I mean by that is the first chance we did data warehousing and analytics and business intelligence and visualization and all that stuff. We moved it around. We use master data management to try to reconcile systems. We've done all these different things, essentially torturing the data to make it say what
we want it to say. But it's been very expensive. There has been certainly value for that. But I think that this inflection point we're at right now is not small. I think this is a very significant transformation and that AI will give a second chance to data, by which I mean, if you feed your AI model, your corporate AI model, I think every big company is going to have one. With the most trusted, carefully curated data, you're going to get a very good result, and if you don't,
you're just going to get some random nonsense. What do you think? Yeah, I was going to say it gives it a second chance, and hopefully companies and people take the second chance to feed it clean data and don't lose focus and don't just focus on, oh, look what we can do with
AI. Now. I think going back to data management and it's important, and making sure that you focus on data quality and data governance, and then that you're feeding this large language model good cookies, not crappy cookies, not junk food, and making sure you actually clean it all up. Going back to the sandcastle analogy. You know, if I use sand with twigs and random stuff and wrappers and cigarette butts in there, the sandtastle wouldn't be pretty,
it wouldn't hold up well versus if I used high quality sand. So sand and data could be a thing. Actually, you got me thinking, I like that. I like that, and sand doesn't need any branding. I think everyone knows what sand is. We've all walked on the beach. But to the point of my one body on the LinkedIn platform, they said, you can create beautiful artwork with sand if you are very careful about what you're doing to it. But again, like with data, it has to
have purpose, it has to have contacts. I think part of the challenge here, and I'll throw this at you and then I'll maybe bring in the whisperer the comment on it. But you think about the old days of data warehousing and how old habits die hard, and you had to strip out a lot of contexts in order to get the little bits of transactional data through thin pipes to slow processors and expensive storage. Well, now the pipes are fat,
the processors are blazing speed, you can parallelize them. Storage is cheap, So every thing has changed in terms of the data gain, which is the big is why we went to the data lake. But again we thought, oh, we're going to put it on in one place, which is probably a bad idea. I think data should live where it is best used and accessed as needed. But what do you think about this evolution and how
old habits die hard? How do you get your clients to make those old habits die Yeah, I think we're still evolving, and I think we are moving in the right direction, and the closer we can get to keeping that business context of that data as we allow access to the data is extremely important. So I'm all about data literacy, data accessibility, data democratization, making
sure people have the right access. But I think with the change in the progress in the data warehousing and cloud now we make it easier for people to actually access not just the data, but data in the right context for the right individual with personalization. So I think we're definitely moving in that right direction. That's good. Last thing I'll throw at you, can you to find data mesh data mesh? No good answer needs you that what you have?
My dog upstairs is trying to define data right now and he's not very happy about it. I have two German shepherds, and of course they chose this moment to chime in because they're trying to figure out what data mesh is all about as well. Well, let's let's hand it over to Scott Taylor, the proverbial data whisper of Meta Meta. And you've been talking about data and the business value of data for a long time, and the deal with data
Mesh is you want the business groups to govern their own data. To help with that process, But tell us a bit about your thoughts and the value of data on the data mesh. You hear about the data mesh, hear about the data fabric. Eric I asked you, what about the data spanks, the data spanks. Thanks like the clothing to tighten that integration. Scott Taylor, the data whisperer here, I help people calm data down. That's
what data whisperings all about. I'm thirty something years in the data management space and now I'm just a content creator, part of Kate's posse of the data avengers as well, out there trying to get people excited and fired up about the data management part of the space. You're talking about feeding stuff into data second chance. That's a nice way to put it, But are we just talking about the same thing We've always talked about. Garbage in, garbage out.
Put the bad data into llms, you get hallucinations. So put garbage into even Jennai, you can get garbage out at scale now, which is really well right Ben. Actually so one of my good buddies, Eugene Burke, who's been on the show a few times Digital Strategies Group, we're doing a bit of a stealth project now. He had the greatest line I think
about this limitation of large language models. He said, they don't have an epistemological barrier, which means for non philosophers out there, they don't know what they don't know, and it reminds me of I won't say which country, but there are certain countries where I was told don't ask people on the street for directions because they'll be too embarrassed and they'll just tell you something you know that they don't know. And that's kind of like what these large language models
do. If you don't have a good embedding strategy, if you don't populate either a vector database or your own model itself with enough curated data, and if you don't train it properly, then guess what, it's just going to make things up, Like when it said I've written three books, and I was like, are you seeing the future? So you're right that we need to curate carefully. But that's always been the mission. It's always been the
case. There's nothing new about that. There's nothing new about the need for a well structured, expertly stewarded, wonderfully governed data that you know you're gonna bring Malcolm on here has got metadata, it's got master data, it's got reference data, mdm rdm RIM, PIM, damn. All these foundational activities that enterprises must commit to if they want to leverage data across their organization. And what enterprise doesn't want to leverage data across their organization. So these are
the same principles we've been dealing with. We got to shine them up, We gotta rephrase them a little bit to get people's attention. But it's just maddening sitting in the data space for so many decades. Again, I go back pre to k seeing the same story over and over again with different characters. Well, I think part of the challenge is that you have this long tale of legacy software and hardware and mindset by the way, and it's very
difficult. So when new technologies come in, you try to move over to the new technology, but you still have this long tale of legacy to contend with. And in fact, we want to promote the Data Universe conference. It is coming April ten and eleventh to New York to the Javit Center, which the Javit Centerism does not like. He's not a fan of the Javit Center. We'll find that out in the next segment. Well, I'll be there, Ka, It'll be there. We'll let Malcolm decide whether he wants
to be there not. Given his it's going to be a party his feeling about that location, but I think it's going to be a blast. Yeah, well, so I wrote it. I wrote an abstract. I don't know if it'll get approved or not yet, but it said the the fourth word is in parentheses forever in your technical debt. That's what I wrote about. Oh and that's pretty cute to Kate laughs, so she likes it. The headline basically leads into the abstract, which has something like what's the definition
of a legacy system? And the answer is any system in production. The joke is that the moment you drive off the lot, you are accruing technical debt, Like literally the second you leave the lot, tacnical debt is like bare debt. That's a good way to think about it. Yes, well, you look at like doctor's offices. I mean they still want me to fill out pieces of paper on a on a notepad. I'm like, are you kidding me? I have to write my name seven times? Like you
can you just you know, could you just give me the iPad? And then they give it the iPad And it's like eight hundred and seventy five questions. It's like the people know anything about surveys, Like when you get to question five and twenty one, the quality of the answer goes way down because no one wants to write that stuff. But we can. We can port that to the data world and say ask just enough questions to get just what you need and move on. What do you think? I think so?
Yeah, it's just I hate that at doctor's offices as well, filling it and over and over again. It's like, haven't they kept anything? Don't they have any legacy data about me? Yeah? They do. It's enough folder that they put in the in the filing cabin in the back there. Somebody here. You have some interesting folders too, including folders on stratchedy me. I have folders, Yes, I've got all kinds of interesting stuff here. Do you want to talk about the puppets yet? Or you want to
bring Malcolm on first? Let's bring in a puppet real quick, show your puppets so we can see them. I think there is. This is about my character the cdo the Chief Dog Officer, and he partners up with If you haven't seen the data puppets on YouTube, you got to see them out
there. There's a preview out there. Journey to the Center of the single version of the truth the greatest data story ever told, starting the CEO, the Chief Dog Officer and also appearing the CEO Chief Elephant Officer played by Kate. There's a CMO. Guess what he is? A mouse? A CFO is a fish? I mean, just stuff rights itself. How has nobody else come up with that? Before? They hire a cat sultant from meol Kinsey who borrows their watch to tell them what time it is. Borrows their
watch. We've got a whole crowd of business users who will be out there trying to do but take a look at that. It's crazy stuff. I'm having a ball. This is what I'm doing now, Data puppets events, the funnest possible stuff And similar to Kate, if you can get paid for making sand castles or doing puppets, then welcome to our world. He gave me a segue to bring in a fantastic lyric by coy Le Roy or Coyle Ray. I think her name is. She says time is money, so
I spent it on a watch. Hold on, It's like what That's pretty clever And that's the semantic side of the equation. We do want words to go along with all these numbers. I mean the numbers obviously are the transactions and how many widgets we're selling and so on and so forth, or clicks and all this kind of fun stuff. But the words are the semantics. That's probably gonna be a good segue to bring Malcolm Chisn't into the second segment
here. But maybe we'll just finish up with Scott, and maybe I'll go ahead and throw a curveball and bring stra back into the conversation Gates the puppets. Why you play with games with puppets with your data? What's the question? Your accent is very difficult there. She doesn't take puppet questions. She doesn't. I'm not allowed to speak about puppets for our contract with Scott.
That's that's his thing. We can it's my area, all right. This is I had a comment when you were talking about the doctor's office and the iPad. So they do have iPad now, or they send you a link on your phone. But the worst, most, most most annoying part is when you have to fill out Let's say I recently had an appointment for my kids doctor check up, and they ask for the kids date of birth at least eight times on eight different within one form, like I filled it out.
You can't. You don't have the systems to just why. It's so compata model issue. It's a system's integration issue. It's a data model, right. I mean I remember with some of the smart people online they figured out, Hey, if we have someone enter the zip code, we can dynamically look up the town that they're in. I'm like, who's thinking, huh, who's thinking put the zip code there? It's like the people like usay figured this out. You call in, they say, I see you're
donallly from a number in our system. If that's the phone you usually used to be a press one, Like, yes, that's right, you know who I am. I don't have to tell five people in a row. This is Eric Cavanaugh. I'm calling about this. Oh yes, yes, that's called enter integration. That's from a lot twenty two years ago. Enterprise integration. We've come a long way, but data is front and center. I suppose data should lead the charge. Let the data talk to you,
let the data tell you what's going on. I mean, that's a standard thing to say in our industry. But folks, we're just gonna have a lot of fun here. On our show today talking to of course, Kate Stretch Dedicated. I'll never be able to say your name normally, just so you know, because I now know like you know, and you laughed. So come on. The guy gets a girl to laugh, just keep saying that stuff. Keep her laughing. That's the joke. Going to have you
introduced me at like conferences. Now, I say my last fact to bring Homer Simpson back into it. One time on an episode he made he got Marge mad and then he made her laugh. He goes, ahh, I made you laugh. I win. He want away. Oh we're not all that bad. Don't touch that doll. Folks will be right back. You were listening to Inside Analysis. Welcome back to Inside Analysis. Here's your host, Eric Tavanaugh's all right, folks, take us to the future. Indeed,
you're on Inside Analysis talking to an all star cast. Scott Taylor, the data whisperer, kid Stratch of Dedicated and next up, one of my favorite people in the business. He's just rocking and rolling on the LinkedIn platform these days with thought provoking posts about receipts. He has all the receipts and he knows about the metadata on the receipt one. Malcolm Chisholm of Data Millennium, Welcome back to DM radio. Tell us about the receipts post that got
three hundred thousand views. Thank you so well. That was you know, something I always wanted to do, which was I mean, you know, how do you bring home data to people and the different types of data? Well, do data analysis of a receipt. We all deal with receipts, I mean just normally throw them away or even refuse to accept them. But if you look at them, there's a lot of data on it, and
some of it, frank I don't know what it means quite frankly. It's all encoded, but a lot of it you can make out, and then you can classify its meta data. It's transaction data, it's mass to data, it's reference data, and it's you know, a useful way, useful tool for teaching people about data. I didn't think it would go very far, but it like took off and went viral, which is quite bizarre. So and it's still going strong out there in the linkedinn sphere. So there
you go. Gave me hope that stuff actually can go viral because I've learned some hard lessons over the years that a lot of what goes viral was paid to go viral, so to speak. Yeah, I don't think you paid any money to promote that, right, that was no, No, I paid no money. Well, I mean except for the lettuce and whatever else was I bought. It cost me about six bucks something like that to get
the receipt. So you know, the local supermarket and the folks of the supermarket were very happy because it served us three hundred thousand views of unpaid advertising for them too, So there you go. But I thought it was very clever because you were looking at the receipt and you pointed out, here's metadata
about the product, here's metadata about the skew. All this information is very important information, which is a window into the operational system that is actually generating the receipt right right, and all systems it's a composite that's all ending up in the receipt of all this data. So you can think about you would have to have different systems to handle different bits to it, but they're all
coming together there. So it's a view of data, you know, and something you know quite common, but it's it's it shows the importance of data yeah, and trying to understand where things come from. The thing I like about it is that it is a reflection of what is coming from the inside. There are all these systems that are tracking the price of products, of course, the date and things of this nature. If you have your your code or your you know, your rewards number, for example, I'm like
Bill Burr, I don't really trust the rewards programs. They're always changing their minds and pulling the string on things. But there is some value and all that stuff, and it allows you to kind of see what's inside, you know. I remember years and years ago I worked on a project where we
connected to Fanny May's desktop underwriting system. We're the first company ever to do that, and I remember that the developers having a hard time, and I thought to myself, you know, in the office they use a system, a desktop underwriter. It's an application. And if I just did something like you know, prints to PDF or something. I think PDFs are around back then, but it's year two thousand. I was like, look, let's just print out what it needs, and that those would be the fields you
would look to capture through this web form. So it's like, how the hell hard is that it's just the data stream. Now you have to securely connect to something, but in terms of the data itself, and that's what we did and it freaking worked. I'm like, well, duh, why don't you pay me a thousand dollars an hour or more? And just for those that are no desktop underwriter is a way to get mortgage applications approved so
that the government will securitize them and put them into mortgage backed security. So it's pretty important part of our economy actually, So yeah, that was that's a big deal. So you know, if you own a house, you've been probably been through it. And even if you don't know, yeah, it's it's painful. I remember the collateralized debt obligations and what was Bucket's comment, don't believe the expirying formulas or something like that. It's like, watch
out for that kind of stuff. Those are the days. Yeah, But the important thing is the data that's in these systems. And I'm curious to know your thoughts. You've been around for a number of years here that have seen highs and lows and movements like hadob coming along, everyone getting excited and
now of course AI but to our point, earlier. AI is not going to do much good if you don't have really high quality data, right, Yeah, I mean talking about the historical I always thought, you know, the slogan of it ought to be, we build the legacy systems of the future, you know, so that people truly understand the value that they add. That said, I mean, okay, AI data is going to be
important, but it's a different take on it. It's going to because these things work off of sentence form you're going to have to get It's much more about getting unstructured data, as we would call it into a shape that you can push through in data preparation into the into the you know, AI black hole beast basically, and that data preparation, having looked into it, is
an enormous number of steps. There's all kinds of things you've got to do, and there's things that AI doesn't do in terms of normal data management practices, like data attention. You forget about it once it goes in. That's it. It's not you know, it's like it's like Las Vegas. It's well, it comes out again, but I mean it's you can't make it forget. There's no forgetting mechanism so if you put something in like PII, it's not like you can scrub it. There's no bleach for AI. So
that's a problem. But this enormous set of steps to break up things into maybe propositional format. Yes it'll read some structural data, but mostly it's getting it into that format. And then on the other side of it, it's not like the thing in Star Trek where you ask it anything and it'll just tell you whatever, and it'll be like this super brain. It's it's going to have to be coupled to use cases, and those use cases are going
to have to be expressed as prompts. So you've got these prompt libraries that will turn you know, the gibberish question that Malcolm asks the AI into the closest prompt and give it the answer associated with that prompts together question answer their So all of that is a huge amount of data management, data preparation, and you better get it right because what I have found in the past, maybe this doesn't apply to AI is but with documentation is that as soon as
somebody finds a bit of documentation, they don't trust like one percent of some threshold like that, they don't trust any of it. Okay, So if you get garbage answers out of AI, then it's going to make people thinking. And although they probably you know, use the day, it's probably conditioned to think that AI is, you know, going to solve all their problems.
They'll maybe be a bit more generous with it than documentation, but they won't, you know, they'll come to a point where they don't trust any of it if this isn't done correctly right well, that that is actually a pretty interesting point, and there is a trust issue that we're going to have
to get around it. There's also a mathematical issue, because as I understand it, what these models have done is, first of all, they vectorize, by which they mean they take words and turn them into numbers, into a raise of numbers, and then they're doing statistical analysis on the numbers. And then when you sort of decode it on the other end, it turns
it from numbers back into letters. So you do have this very interesting transformation like reminds me of asking, remember asking, or it switches from letters and sentences and paragraphs and so forth to numeric values and then back. So the thing that kind of strikes me is the other shoot to drop. Here is going to be the cost of this stuff now? Right now, there are billions of dollars going in by Microsoft and Google another guys to train these things,
and companies are all going to want to train their own models. But I don't think we really have any idea what the actual cost is going to be to the user. And I think that's going to be another shoot to drop. But what do you think about them? Well, right, I mean I think that it depends on the apps that are made from them. So you know, I'm looking to this right now because I don't want to use one of the hosted ones because why should I give it my intellectual property?
So I'll get one of the ones I can have on prem Well, it's going to cost me about five thousand dollars to buy the equipment, you know, just to be able to run the you know, the base version of one of these. But then I could do things like, you know, create something around a curated set of knowledge where people would understand it, do that and give you answers to that. But I think a more interesting
set of use cases to me is one is knowledge integration. So you know, we talk about data integration like you know, you're talking MDM like integrating customer data earlier. Okay, but we've got knowledge as well, so I could theoretically think about nobody likes being a data steward, So let's have an AI one. So this AI data steward is told who looks after you know,
these reference data tables, these code tables. And then it's also let's say, coupled to the HR system, and it recognizes the feed from the HR system, so it says, okay, Eric is or Malcolm is the data steward for the country codes. Okay, that's good to know. And then Malcolm gets fired because he's useless and is replaced by Eric. But that's an announcement from HR. Now today you'd have to go into a data catalog
and manually some puschmucks data slash data steward has to update it. But now we can integrate these various unstructured data sources and it'll realize who is the true
data steward of the country code. So we've got that as a possibility to get all this integration of knowledge, which is something a little bit different to the structured data integration which is so that's a good one and having these you know data steward bot really you know agents out there doing this work for us so that we don't have to do all the donkey work which nobody wants to do. We keep telling them that they have to do it. No, no, they don't want to do it, so and I don't blame them.
So those are there's a lot of promise there too, if we can get this to work properly well, and you know, let me throw this curveball et you and then I'll bring in the other guests and throw it at them as well, or some variation of it, and maybe a hard slider.
When I think about the power of these models to upon prompt grab information from all these different sources and fuse it together to give you some narrative, which is what they're doing, I wonder to myself think about all of the heavy lifting that's done even still today for data warehouse and extract transform load extract load transform, all these batch windows, which in large organizations are in the hundreds, if not thousands, And you have to know that it's not all
change data capture. There's just a lot of bulk movement of data picking it up for naked over the year, pick it back up again, fork lifted over there. That's I think that's going to be looked at as a tremendous waste of time in the new world. But the key is cand these models short circuit all that and just get me the answer I want somewhat reliably,
so I don't have to move all that stuff around anymore. What do you think, I think you're on something that I think that one of the impetuses if you look away these the AI audents are coming from is to get rid of code, is to get rid of data. Engineers don't need them anymore. We don't want them. They're a huge expense, so could we replace them. So you've got you know, copilot and things like that. You've got assistance to generate code, But why don't let it do the code generation?
If you could just show it the data, okay, and do exactly what you're saying. I mean, how complicated is it? I mean, etl is you know, the thetail products are, or traditionally have been a replacement for the more expensive kind of programmer. This is just taking that evolution a step further. So then then you know, if you could like just get rid of the you know, legions and legions of coders that are out
there. You know it's not good for them, But and replace them with some skilled people who are driving this and who you know, constrain the AI and let it generate the code based on what it's seeing in the day. That would be that would be a massive you know, efficiency gain in the economy. Yeah wow, well and uh yeah, I mean I asked myself, we have a show planned next year, what's not in the cross heres of this stuff? And you know the fact is we have lots and lots
of legacy processes, legacy mindsets, legacy systems. But so just give you one example real quick before the break. Think about MRI scans. My wife who has to deal with MS which is a real pain. She has to go get these MRIs every once in a while. Those files are massive. I mean you have you can fit them on a CD ye on, but it's a huge file. If you vectorize something like that, number one, it's a form of compression. But number two, it facilitates statistical analysis.
So isn't that a use case for for vectorization to vectorize graphic images big ones into numbers which is much smaller and allows statistical analysis. What do you think, Well, I think we're we' I don't. I think you're right, But I think we're part of the way there already with the image processing features that we have that are available in current libraries which are pre AI. So
a lot of that can be done already. I've seen it done, you know, building data pipelines to do just that, like has the has this you know, tumor progress regress whatever, So so we go a lot of that, So that would be taking step further. Why not? Yeah, I mean I think again, what isn't in the crosshairs of these new technologies, you know? And I think also you you're in the process. We could have a you know, we could have an AI illusion of a host
who could ask the questions. Yeah, you know, you can get you can get Jen Jena AI to ask to you know, give you questions. And you couple that with one of the you know, the video generates the things that they have and they are you got a new personality. Yeah. I think in terms of its storytelling, it's still pretty formula. But it's you know, yeah, you have to figure it's learning, it's going to learn, it's going to get better and better at these things, and you're
going to have your choices. Maybe maybe they'll plateau. Yeah, hopefully we'll see. Well, don't touch the doubt books. We'll be right back. You are listening to Inside Analysis. Welcome back to Inside Analysis. Here's your host, Eric Tabanata. All right, folks, back here on Inside Analysis, talking all about all things data. Time to refine. You have to refine the data to get value from it, just like oil, just like water. I heard the water analogy at a conference this past week, and
I was joking, we're all thirsty. Look at the oceans. They're huge, but you can't drink that water. You got to desalinate it. You got to clean it out. You got to get the fishes out of there, you got to get all kinds of fun stuff out of there. So we understand that refined data. But I'll throw it over to Kate's stretch of data caated to to share with us her thoughts on on best practices going forward. We have a second chance for data. I think our audience has agreed
with me on that. What's your advice for companies to build their own AI model with refined data? How do you get that process started. Yeah, you know, when you said best practices, I was actually going to take it into the direction of data privacy because something Malcolm shared earlier when he was talking about receipts reminded me of a story I had from a couple of months ago. I went clothes shopping, So raise your hand if you've been clothes
shopping in the past few months, right in store, not online. And I think we share so much information online that when I don't really do in store shopping. But when I got there, they said, do you want to put your email or phone number into the system? Literally every store? And I'm like, no, I don't want to put my information in. I just don't like it. I don't like all the spam. It was
fine, and then I get to one store. I'm not going to name and shame them, but they're like, okay, do you want to receive After our whole back and forth of are you sure you don't want to sign up, I'm like yes, I'm sure, fine, Okay, So they're like, okay, so in order to get a receipt, you're going to need to give us a phone number or an email address. So I'm like, okay, Well, I guess I won't get a receipt then, and so they come back and they're like, well, you can't return your stuff
with that or receipt and these were shoes for my kids. So I'm like, okay, well, unfit, I'm gonna have to come back. So they sort of put me in this position where I won't be able to return it, but they're sort of forcing me into sharing my data. When you when you were talking about receipts welcome, that was all I could think about. I'm like, yeah, we have all this information that they're collecting, but sometimes I just didn't want that to happen. I ended up not getting
my receipt. But I think one of the best practices that I can think of for organizations and data is making sure that the people that are taking data from feel comfortable enough sharing that and sort of allowing them to opt out of that process if that's something that they want to do. Well. You bring up a very interesting point, which is getting forced into using a certain system,
and this I think is the dark side of digital transformation. You know, I went to a hotel in Austin, Texas last October and there was no one at the desk there was no one to talk to to give me my keys or anything. It was all this this online process of you in your phone and I had to like show it a picture of my driver's license to capture that. I'm like, how orewellinging is this weird situation? And
it was a real pain in the rear. I'm not gonna lie. It was like you really can't afford to have one person sitting there and like get paid twenty bucks an hour to take information, like that's how far we're going to go? Or I'll try to be you, Kate, and then we'll get the other gentleman in on this. The McDonald's chaosks where they want you
to go and find your food. Look, if you just do the guarden variety, something okay, But if you have special orders, it's like, oh my god, hold on this menu, over to that menu and this other now wait which I lost it. It's just a total nightmare. There are some things that should not be digitally transformed. Real quick, Kate, what do you think? Yeah, so real quick, I'll tell you. I think there are preferences. Some people, probably about half the population,
would want to avoid eye contact and people. Yeah, and they would love it. So I think just providing the option for someone like you who wants that social interaction of like, here you check my idea, I don't want to scan this. And then I think just just long story short, have both options, because I know that could speed things up if there's a really long line and you've got that one person who just maybe is new and does know what they're doing, or well system crashes. You have all these other
options of people doing it themselves. But I personally like, if I'm going to get do food shopping, I'm not going to do the self checkout when I see even if there's a line. I just why there are people a right there who are going to do it for you, Where my mom would insist on self checkout, even if it's a cart full of stuff. I'm like, you might you might as well work here now you're just you're doing it all, so there shouldn't at least a discount for that service. Yeah,
Bilber makes that exact joke. It's like, why don't I just check it out myself and I get a discount? Or what's going on? I will? Yeah, exactly when did I sign a dive an Intel insurance plan? What's going on here? Oh why am I working for you? Well, Scott Taylor, I'm sure you don't mind hitting all the different buttons in the menus. I don't. I don't mind that. I guess my big question is based on the conversation we just had. Kate, did the shoes
fit your kids? The shoes fit I did not have to return, but I'm not going back to this. Hew. Okay, that's good. I did. I had the opposite experience recently at a panera where they had a bunch of things on the menu and I was sitting there, going, I have to ask these people what's in this salad? Like three different salads, And they had a kiosk and they had the nutrition information and they listened to every single item that was in there. Was like, all right, that's
a lot easier. So yes, if you have a choice, but sometimes you feel like not because I don't want to have eye contact with somebody. But it just might be a smoother experience to go this way rather than that way. So as much as these things can augment our existence, that's good. But when it starts to replace it as an either or or that ridiculous story you're talking about kate of demanding certain data. Otherwise you don't get proof
of the transaction we just had. That's what it's crazy. That's I was wonder as well, because I don't know. But if you know, if you spend some time and you're not from Russia, you're from Tajika standard, I guess, so it's not not the same. You'll probably just say don't shop here if you don't like it, right, but I don't know, don't shop her if we can't get your data. Yeah, pretty much, that's that's pretty strangel Malcolm. I'll throw it over to you for Yeah,
I would. I would try that my favorite Soviet rule, which is the rules are there to be avoided. And you want my emails, okay, say a at gmail dot com. Okay, so I'm just lye or you know, we just said you were the most honest man a moment ago. You've upset the apple then no, no, no, no breaking now you heard it here first, just ripped it wide open. In this kind of ethical dilemma, you're forcing me to do something against my will, So this
is what you'll get. Well, well, Malcolm, then you you still you won't get the receipt which give will print it out and give it you even that print. Yeah, they don't have only email. Okay, in that case, what would It's a single point of failure. So this is like when they had a colonial pipeline ransomware a few was a couple of years back where they said, they said, well, okay, the system screw
up. Well why didn't you go back to managing this pipeline, you know, the mechanical way that you used to do it, because it's quite old. And the guy said, well, everybody knew how to do that is either dead or retired, and so you know we are. You can't read maps, you don't want to make eye contact with anybody or talk to anybody. Okay, this is okay until something happens with this great shaky edifice of technology that makes our lives easier in the sense that we don't have to think
very much or engage in too much physical labor anymore. Yeah, but what happens when you need to so you've got no backup, You're you know, you're making your civilization extremely vulnerable. So we will we will see. Well we were talking about in the break, right, So you have Jena I create the questions for an interview that you have, Jena, I create the answers for the interview that you have it, watched the show and generate a summary do we need as part of the show. There are bots that are
accounted as visitors, as viewers to my website. Look at all the viewers I have. Yeah, I had a nasty spell, an experience. I don't know, but I was contacted in by over LinkedIn by it was a legitimate company. I'm pretty sure it was a bot that was texting me on LinkedIn messages and then somebody another bot on email, and I think we ought to at least have transparency, say Hi, I'm a bot, I'm emailing you on behalf of you know, blah blah blah company. There's no there
is no transparency. It's like I had to prove I had to go through this LinkedIn certification where I did have to do facial recognition stuff to get LinkedIn to believe I was a human, whereas no bot has to declare themselves to be a bot on LinkedIn and doesn't have to prove that they're not. But I had. I had to prove I wasn't. I had to prove I was human. So that kind of irked me. Is that a rou Is
there a need for like a reverse Turing test? M. Yeah, you're onto something though, because you get these phone calls too, like Hi, it's Cindy, I'm calling you about the new online program. Like okay, I'm sure there was a Cindy who recorded that at some point in time, but you are not on the phone. No, No, there's not even a Cindy. It's totally generated. True. Oh, that's just bizarre. I think I like opening up one chat bot and then getting into a conversation
with the other chat bot. So you get two chat bots talking to each other. Yeah, and you still don't get your order through. But it's fun. It's you know, plenty of time with my hands in between puppet takes. Yeah. At a certain point, customer service does come into play and you will remember, as Kate will, not to go get that store anymore. But I have to shout out to old radio Shack because in the
nineteen eighties radio Shack had the foresight to ask you for information. They had a little form you'd fill out when you bought your batteries or whatever it was. It was actually very annoying, but in retrospect. I was like, wow, they were very forward looking, but they weren't. There was a comedian who did a bit during that time about having to give your because they forced they were really militant about getting your phone number. They were remember this,
and you need your phone number to buy batteries. It's a corporate dictate. Yeah. How's how's radio shack doing these days? By the way, so well, folks who've been listening to the only coast to coast radio show here in America about the infation economy. It's called Inside Analysis. Talk to you next time. All right, folks, time for the podcast bonus segment. We said you've gone and we can talk about it in different ways. We will get the information from it. I love Russians like I just love
the Russian accent. That just cracks me up. Hey, you're bringing me back to my bow ankle days here with your with yours And they finally, and what's Natasha's last name? And it's talking about trivia since the bonus you know Natasha Boris and Natasha what is Natasha Fatal? It's Natasha. Yeah, So I like, did not see me look anything up for that one? I know that one look trivia pursuit days. You are a walking, talking,
large language model. I've certainly hallucinated given my Berkeley background. That's pretty funny. I like that. Well, speaking of hallucinations, I mean, goodness, gracious, we do have to get down to business here and get serious about these AI models. And this is my prediction that every major corporation is going to have their own AI model. Many of them will working at
it already, a lot of them aren't. In fact, you'll probably have multiple models where I get multiple data warehouses and I get back to is anything not in the crosshairs here? I'll throw it first to Scott and then over to Malcolm. You know, if you launch one of your own models, your private, single instance, if you will single tenant AI model and start feeding it with your data, you're going to be able to get interesting stuff
from that. From a senior executive standpoint, you'd be able to ask who's working on this right now? Up? Up, it's just going to pull this stuff in. It's kind of crazy how well it works. I don't fully understand it. I don't think anyone does. But Scott, I'll throw it over to you. I really think that every last bit of legacy technology is to some extent in the crosshairs. Now, what do you think, don't you hope so everybody complains about so much of this stuff, you hope
that it gets transformed in a way. Meanwhile, more than you know, half the world, if not more, is still tied to Excel and things like that where they got to go kind of do their own little skunk works. But you know, I hear about what I kind of want to remind people of is all this stuff's cool, But how's it driving the business regardless? How is this enabling the strategic intentions of your enterprise? How is it
helping you your company get to where it needs to go? And that's still the nature of business, right, human interaction, bringing value to your relationships through your brands at some form of scale, right, that still has to happen, right, And and and I think you know, while Malcolm was talking about coders, I mean, it wasn't that long ago, right that they were saying, okay, what you got to do. Everybody's got to learn to code, And now it's you know, that's the equivalent of we
all have to learn how to use a slide role. So just you know, having the creative aspects. And what I would counsel anybody going to the space is make sure you, you know, you beef up your creativity interesting capabilities there because that is going to be tough to to replace. Yeah, that's despite what's going on in Hollywood and that you know, discussions and during the strike about the use of AI and that sort of thing. Uh, it seems to be okay to tell a kind of mediocre story, but it
can't. Hasn't come up with great stories yet, has it? Yeah? I don't know, Man, this is interesting, Malcolm. I'll throw it over to you. You've been tracking this industry for a long time. There are countless inefficiencies in how we manage data, how we move data, what we do with data. I think these new models, these new AI models are going to be truly revolutionary in terms of information persistence and retrieval. But
we do have to be careful because they do make stuff up. So how do you know, how do you build an epistemological barrier into these things? I don't know. Do you have any ideas? Now? I'm not sure, but I would I think one of the things that we we're going to have is a bit difficult to conceive of. Is it's going to replace legacy. I mean, what we have is an accumulating layer after layer after layer of legacy technology. There's going to be a new one on top of that.
It's kind of the law of primitive survivals. You've still got you know, I looked at jobs for cobol programmers on LinkedIn a couple of days ago. You've got people, you know, still writing cobalt and it's not necessarily going away. So we're getting this layered architecture built up with these things on top of it, which is becoming increasingly difficult to manage. It's not like we've had this before and you generation your technology is supposed to replace everything else.
It doesn't. It just becomes another layer. Look, you know what's happened to her do But that's legacy now. So I think that's part of the The other part is this is different in some ways. It addresses a different set of use cases, and as you know what Scott was saying in terms of the value it brings, I don't know, but I think it's like one of these moments where everybody's paralyzed with fear again that if they're competitor to get hold of this thing and does something with it, they're going to
be driven out of business. So we got to do something too, and we'll spend money on it up until the point where we've disproven the technology that it isn't a threat to us. Okay, like you know, the books and mortar crowd then if you remember, rebelled against the dot com people back in the early two thousand once the dot com bubble burst, and they took a pretty terrible revenge on them. Now everything's been sure since then, but it kind of feels a little bit to me like that again, Well,
everything's got a label now right slapped on it now with Jennai. It's just like you know now with extra Zest, it just becomes this productization for a lot of these technology companies. You know, they ripped the data Mesh label off their box and put Jennai on it because that's the hottest thing now. So there's it's hard not to be cynical about some of this stuff if you've been in the space for long enough. Wow, what a fantastic show here, Folks who want to be on the show, send an email info at
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