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Who is truly Eric Cavanaugh is here with the good buddy of mine, one of my besties from the Pitts region, Andy Hanna, who has quite a pedigree. He's done a lot of really cool things. We're going to talk about the origin story for Blue Street Data. I came across Andy on LinkedIn, which is obviously the ideal networking platform in social media for business. Those folks have done an excellent job. They've also spun out some great technology.
In fact, Apache Kafka, which is now everywhere under the stewardship of a company called Confluence that came out of LinkedIn. That's the engine used to run LinkedIn was called Apachi Kafka. It's now a huge source and in fact it's a source of data for one of my other clients, a company called
Osceans. They just rolled out an announcement today, landed another forty nine point four million dollars of investment and extension of their Series B and Chris glad when their CEO was telling me the Confluent is one of the big sources of data for their platform, so LinkedIn good stuff. That's how I met Andy through one of his master's students at fourteen eighty six Labs. But he's got a long origin story than that, and we'll kind of dive in. It'll be
fun to remember where we've come from and how we got here. It's kind of hard to really wrap your head around the changes. They've been so significant and so compelling over the last two decades. But here we are. So with that Andy Hannah from fourteen eighty six Labs and the University of Pittsburgh and oh thought, well, welcome to Inside Analysis. How's it going, Eric, Thank you so much for inviting me. This is so much fun.
I'm so excited to be on the show this afternoon and can't wait to chat with you about you know, the past really thirty years and what's changed and how we've evolved over that time. Yeah. And I remember a friend of mine came over to my apartment in New Orleans and was all purpose driven and was like, you are going to get on this thing. I'm like, what are you talking about it? And it was the Internet and I was
like, all right, I had heard about it through Prodigy. In fact, when I was a newspaper editor in Lamont, one of my assistants was all into Prodigy and it was like, oh, that's that is pretty interesting. And of course it took off very very quickly. But you'd have to think for a second, how did we get information before the Internet? And the answer is on floppy drives and you know, other hard medium that you would carry around. And I was in the printed US business, so you
couldn't use floppy drives. You had to use these big, like portable discs. And I thought it was so cool to have by portable discs that I carry around because that was cool. But you were there too, so you remember the early days. How did we get information around back then? Sure, I mean you're exactly right, floppy disk, but I remember, you know, if you were in financial services, you had multiple terminals on your desk, right, you had a Bloomberg terminal Thompson Reuter's terminal. You had
a terminal from Moody. So it's like you couldn't even combine the sources or the you know, the systems. In order to have one screen. You had to have multiple screens. It was a really inefficient time into retrieving data. I mean, I'm sure you remember if you wanted to give somebody a file, you had to stick it somewhere out there on the Internet. Literally, you couldn't attach it to an email. Remember you had the FTP it, right, and then I have to say, hey, Eric, here's
the secret passageway to where the information is on the Internet. Go out there, grab it and download it, right. I mean, that's what the early nineties were like, whenever you were trying to share information. Yeah, and it's changed a lot. We're watching Young Sheldon now on Netflix and they were just joking about that, how Young Sheldon was on an FTP transferring files and that's what you would do. And it moved pretty darn quickly, you
know. Once. I remember the early days, we called them asps right, application service providers. And I once had a smart guy in my show, I said, what's the difference between ESPN and suffer as a service and go SaaS works. I'm like, oh, I get it, smarty pants.
But architectures changed, and there's tremendous innovation that came out of companies like Yahoo in the early days, like had Dupe that whatever to Google and really started this or I should say kick start at the open source movement into very serious data territory, right because Linux was the foundation of open source, and then ten odd years later you had this whole revolution in data management with had Dupe and a whole ecosystem around that. Of course, a patche Kofka is
open source and all. So the channels from moving information have been rapidly advancing. We've got Snowflake, now we've got data bricks. We all have all these amazing companies that are allowing the use of analytics. But you and I have been there from the word go, so we remember what it was like to try to cobble this stuff together. And really you have to constantly change your mindset about things and recognize that the times have changed and recognized you have
to be using the new platforms as time goes by. And I guess that's an epiphany that you had as well, right, yeah, yeah, that was in the mid nineteen nineties. I would say right around ninety five. There's a really visionary guy's named Gary Muller, wonderful guy who was doing research in Poland. And this is remember not too long after the fall of the
Soviet Union and the former Soviet countries all wanted to privatize their assets. So what Gary was doing was doing research about a particular industry and what he was finding is that there was a dearth of information. And so a bunch of us got together and said, okay, let's build this information infrastructure about the former Soviet Union. So countries like Russia, Poland, Ukraine, you know, the Czech Republic, et cetera. And what we did is we sent
little teams into each of those countries and they mind data. Literally we either we got it somehow digitally or we went to uh copy machines and actually copied it. And uh, I'm serious that because it wasn't in existence. We had to go to banks, companies, analysts, and then we would consolidate it and all in digital form. And we needed a way to distribute this information. We weren't going to do it over Bloomberg or Reuters and so we
said, hey, there's this really cool thing called the Internet. You know, Yahoo seems to be making it work. And so we literally put you know, we probably created one of the first SaaS based applications out there. We put it on the one and we got banks and large companies wanted to
investor do business in the former Soviet Union. They connected into these these this database and they paid per drink essentially, and so meaning that every time that they went it went in, pulled information up, they would they would pay. It was a it was a total gas. We had to put the first Internet Service Provider ISP for all those who are kind of from a different generation, but we had to put the ISP, first ISPN poland out there.
We actually owned the first ISPN poll wow, And so that was what it was. It was crazy. It was a wild time, and it was it was pre infrastructure, and so we developed our own accounting systems, we developed our own distribution systems. It's kind of crazy. It's a lot of fun. Yeah. Well, and you were at the very forefront of what is now called alternative data. And you and I have talked about this.
I've done a lot of research into it, and you know, like you, I think down the road like, hmm, how is this going to pan out? And clearly, the amount of alternative data being bought and sold today is just vast. It's stunning, and a lot of people don't realize that credit card companies sell what they call exhaust data, which is then
bought by a whole variety of firms, most notably investment bankers. And I wrote about this a number of years ago saying that it's a pretty unfair playing field if these investment bankers can all that data at their disposal, because now they can see, for example, who's going to meet or beat market estimates on revenue because they have the actual raw data and they can compare it to
a baseline. So why don't we have some publicly facing consumer data leak with my suggestion, so you could level the playing field and so the rest of us could all benefit from that. And you're kind of seeing the start of that in sort of in different places. And of course you also have a history in the machine learning plus data space through this company called O Thought right,
tell us about that? Yeah, sure, I think one stop before then I'll talk a little bit about sort of that transformation from data is powerful to data plus analytics or AI is more powerful. And that was the time that I spent A good friend of mine started a company called the International Institute for Analytics. His name is Jack Phillips, along with Tom Davenport, who
we all know is sort of the guru and business analytics. And you know, about fifteen years ago they together and said, you know what, we need to be the leading research firm about how firms are using analytics, looking at the maturity of companies in terms of low versus high, and then whether or not they outperform companies that actually are less analytic. And you know, the research holds true, the more analytic you are, the better you use
data the top of the heap you'll be. So good evidence of that is if we look at the top ten companies of the s and P five hundred. You know, these are the Metas, the Googles, the Microsoft's, the Tesla's, you know, they're using data in a very different way Amazon, and they represent forty percent of the total value of the SMP five hundred.
While so they learned they're the early adopters. They understood power data power of analytics together and I feel very fortunate to spend time with Jack and Tom and do some consulting some very large companies and understand this transformation that was going on. And then me and a guy named John Evattico, a guy named
Jeremy Garvey started said Hey, we're going to do this. So twenty fourteen we started this company called Othought and the idea about oh thought was, can we at scale take data about students, high school students and help universities colleges enroll the best fit students who are going to persist and graduate, and how can we help those institutions get do better, get more graduated on time,
get better jobs, et cetera. So that's where we took machine learning coupled with all the rich data that high school students provide to universities, combine those two together, and we build othought. And that was a seven or eight year journey. Well well, and just to explain to our audience here, what you're able to do with data at scale and analytics and especially machine learning
is, of course, identify patterns. But then the real magic is once you've identified a pattern, let's say, a pattern of a successful student. Once you've identified that pattern, then you can sort of distill it, understand it is almost like a recipe, and then apply it to the rest of the data and see. Aha, So these forty out of one hundred students actually fit that pattern of behavior in terms of interests, in terms of writing
style. There are all sorts of different bits and pieces you can cobble together. And that's really the power of this analytical technology, right, is that you can understand what works and what doesn't work. You can model both and then analyze current data to say, Okay, according to our model that we've built, these forty students are going to do very well. These sixty probably
not so much. And you'll never get it completely right. I mean, there's I think a bit of a misconception that, oh, with all the best analytics, you'll always be right. No, you won't always be right, but you will greatly increase your chances of being correct, and you'll create
what's what's called lift right one hundred percent. So that's so let's let's even maybe easier for the audience to understand, is if we're looking at the probability of somebody to enroll, right, So what let's say that Eric, we want you to enroll at the University of Pittsburgh. We see that you're twenty percent likely to enroll because that's what the machine learning has told us, and
that's because of who you are and how you behave. So we know all the data about where you went to high school, your grades, you know, the activities, et cetera. And then we see your behavior. Right, so you're hitting website, what you're reading, you know, the visits that you go to. Are you an engaged individual? So you land at this twenty percent, Well, we want to do if this you know, we want Eric, we want to be eighty percent. So we look at
the prescriptions. What can we do that's going to increase his probability? So, for example, we might send you a particular marketing campaign, give you a particular scholarship. This is you not high school, and all of a sudden, because we are studying the data in your patterns just like you said, we see that those activities raise your probability from twenty to eighty percent. And this applies to every industry. This happens to be higher education that we're
talking about. But this is how Amazon gets you to buy all the products that it gets you to buy. To that recommendation engine. Yeah, and it's what's cool is that you can learn about the students, you know, So it's great for improving efficacy, but it's also great for educating the people who are involved in the process, right, because when you play with these models, when you play with the data, that's when you start to understand
how it all fits together, how the tumblers align. And I think we're at a very exciting time in our business world and the education world as well, because we've reached a bit of a critical mass in that regard, meaning we have the compute power now, we have tons and tons of data, we have smart people who have methodologies, who have published these methodologies that we can follow and understand. So it's really all coming together in terms of being
able to leverage this stuff. And then you look at artificial intelligence CHAT, GPT, large language models, which are of course are very very powerful. They are going to require some effort to govern and to manage responsibly. But I think we're up to the task now, right because of all this experience, because what we have is so valuable, and I think the transparency and ethics are really going to come into play here in the near future. What
do you think hugely, hugely important. I mean, one of the things that you said that people don't often get is it's not just about predicting the future. These models that you build allow you to diagnose where you might have issues. So you can detect bias right through these models. So if there's bias in the data or biased in the interpretation of the results, you can look at it and you can do some reverse engineering and say, hey, what if I change the gender. What if I change does the results?
Do the results change? Oh? Wow, this particular subsetgment is disadvantage because of that, because it just just by changing the gender, which means that we have a problem. Right, So then we've diagnosed it. Now we can fix it. So and I think that that's an area that we're really just starting to dive into so that we can understand how to fix the problems that exist. And by the way, we'll never fix all the problems. We just got to put on the big ones. Yeah. Well, and
it's a journey, right, it's a process. And of course when you fix one thing, other things break. It's like painting your house. You paint one wall, you got to paint them all, right, Talking nuts unless you do that. But you're hitting on something very important, which I think is probably the most valuable proposition about AI and machine learning is that it helps you get to understanding the problems. That discovery side is very challenging because
it could be anywhere. I often use the example of you lose your wallet. You're looking for it all around the house. Is it even in the house. It's very frustrating because you don't know exactly where it is, don't know where to look. That's kind of the way it is with large sets of data too. You have to start filtering it and taking it a hard look at things. Like you said, let's flip out the gender see if things change. Oh wow, they change dramatically. That's part of the process
of working with the data, of trying to understand the data. And it takes effort. I mean it's we're getting better and better with the technologies, but still it takes human beings in the loop it analyzing the data. I'll give you a fun observation before a first break. Come e for in a minute, what good buddy of mine who also runs an analytics institute. He goes, Yeah, machines don't have the ability to go, huh, that's kind of weird, but human beings do, right, Yeah, I mean
that's a good point. You know, I thought our chief data scientists, mart Fordman, he's actually developed what we call the automated data scientists. So the automated data scientist does exactly what you're talking about. It scans results, it scans data, it scans changes, you know, model drift, and it says, aha, it tells our people, aha, we got a problem over here. So it's like a member of the team, which is how I think that we need to start thinking about AI as a member of
the team and not an adversary. That's a really really good point. And we're up to our first break here, but don't touch up now, folks. We're talking with Andy Hannah all about machine learning and data and the origin story of blue Street Data. Will be right back. You're listening to Inside Analysis. Welcome back to Inside Analysis. Here's your host, Eric is Tavanaugh. Okay, folks, back here on Inside Analysis with the one and only Andy Hannah of fourteen eighty six Labs. Or get to that in just a
minute. And of course, oh thought, and in the break there, we're just chatting about the fact that data is changing the nature of data is changing, and what we do with it is changing. You have these cycles basically, you know, I'll just throw one fun observation out there that I
seen in the recent past, which is observability. This whole space blew up called observability, and basically it's just windows into feeds of machine data that's cruising around of things that are happening, connections to data sets, for example. And within like a year to two years of observability becoming a thing, there's already the company that sits on top of your observability data and allows you to
manage it. So there's already a meta solution We had them on the show last year that allows you to It's called Mesmo, and they allow you to transform and process and correlate data streams from observability. Right, So these are new ways of dealing with data. It's not all unstructured data like human texts and images and things that they thination. A lot of it is machine data
so as they talk to each other, it's capturing that information. But the point is this is all grist for the mill, as they say, and what data analysts and business people in general need to appreciate these days is that it is changing and it continues to change. So you have to kind of change the pace of your own processes and workflows and understandings to be able to leverage itself. And you'll never be completely up to date, you know what
I mean. You're never going to be riding the crush of the way for any significant time because everything is changing all around us. So you kind of have to be very agile and very open minded and by the way, put some governance into place. Right. Governance, I think is going to be a hugely popular topic for many years to come because we're in such a turbulent
time and because the outcomes are still relatively unknown. Well, if you don't have governance and you flip the switch of aon and your aion and your organization, you're going to get some crazy stuff flying on the other end. It's not going to work well for you. But give me your thoughts on that. The changing nature of the data landscape and how you have to change with
it. Yeah, So we're I think we're really lucky that we've seen the evolution of this technology and the source of the power of the technology, which is a data over to pass forty years, right, So if you go back forty years, we know, you know, Yahoo had just been going public, you know, or just just formed. So we've come a long way since those first days. And even if it's only two decades since Yelp and Twitter have been around, and really the growth of that and the use
of that data is over to pass decade. So the ability and the examples that you give are wonderful are but the ability to think about the use of that data that didn't exist, that user level data did not exist until a decade ago. And so the ability for and we see that as we mentioned to the top companies in the country using that data to understand behavior and to
change that behavior, the increase probability of buying those types of things. How do we you know, how does the rest of the world, Maybe this is a good question for you. We know, it's like the rich getting
richer and how does the poor move up? Right? Because these laws, large companies are the ones who are pushing the edges of technology and the use of data when the eighty percent of the companies that are out there are still trying to understand digital transformation and what can I do with that data they're learning
about the use cases and the applications. You know, are we going to continue to have this huge divide so that the you know, the true value that's driven by AI and data is really housed in a small number of companies. What do you I mean, what do you think, Derek there, I mean, that's that's a very very big question. I think transparency is
going to be the key. I think this idea through at the top of the hour of a consumer facing data lake basically would be interesting because it's a place where we can all dive in and sort of better understand what's happening with this data and ownership. You know, data ownership is a big issue these days. You're starting to see some of the bigger companies at least give lip service to this, but allowing you to own your data, giving you control
of when your data can be used to train algorithms, for example. There are lots of people coming up with business models to allow you to own your data and control that. But it's hard. It's going to be hard to unseat the Amazons and the Microsofts and the Googles of the world. But I will tell you that the big guys are nervous too. There is tremendous tension
at Google right now. They are very nervous about what just happened with chat GPT in particular, and then Microsoft stepping in to buy it basically or invest heavily in it, I should say. And then what happened. The board for open Ai kicks out Sam Altman, and over the course of the weekend, Sata Nadela offers to hire him, and you're just like, Holy Christmas,
what is going on? Ye, we do need transparency, and I think that while I fully respect the concept of intellectual property and having to protect these sorts of things, to get right down to it, these models are so incredibly complex that you could offer plet transparency and I don't think you're jeopardizing your business model really much at all. And in fact, I just heard
today that apparently Elon Musk is going to open source GROC. Of course, Mark Zuckerberg open source Lama or Lama too, So you know, again you kind of see these these movements, and I just read today that who is it? I think Google is following suit and allowing you to exit their data centers, right because they're all these exit fees. Basically that we're just killing people. I mean, that was the big joke is you can put all your data in the cloud for cheap, but if you try to take it
out, those egress fees were going to hammer you. I do think there's pressure on the big guys, but I think you're right that we need to be very careful. And you know there was concern what if this kind of LLLM capability is restricted to a handful of people, Well, that would be a pretty bad situation. I think we want we all want access to these technologies, and I think it's good that we have competition at the highest levels
that's pushing towards that transparency. But I think transparency and education are the two massive keys to success to give power to the little people out there. What do you think? Yeah, I agree with that. I think that that's
a tull order. I think you know that we were talking earlier about data warehousing, and you know there's still there's still lots of companies trying to figure out what's my single source of truth as opposed to thinking about what's the latest in architecture that I can use both external data and internal data to outcompete those
who are in my space. Right, So it's I just I look across the spectrum of companies and I am just floored by how the distribution of capabilities, the distribution of knowledge, and there are so many companies that are just at the beginning of this journey, and you and I, you know, understand this the end of the journey because we work with the larger companies are on the edge. You know, we understand retail media networks, and we
understand data clean rooms and data fabric and all this. But you know, most most companies are going to Gartner's annual conference to learn about what the heck these things are, right, let alone being able to gain value out out of this technology. So I completely agree with you, you know, the transparency and the education. I just don't know how it's going to happen without mechanisms to say, okay, here's how you can get value, you know,
and then let's help. Let's help each other, Like on fraud as an example, we can share knowledge about fraud detection. Right, It's it's do we want to compete on fraud detection? I hope not? Right, hope, that's something that we want to share. So why don't we have a common way of looking at fraud and helping each other in every industry understand where there's fraudulent customers and or customers that don't even exist. And so I think we have to figure out a way to get people to that that base
camp of use cases and understand how they work. I like the concept the base camp of use cases. You're absolutely right about fraud. I've talked about that on this show even fifteen years ago, with the same exact thought. It's like, let's share threat detection data at least within industries and the financial services, in healthcare and insurance and different industries where they're the same business model. Let's share information about threats because they can spread very quickly and they can
bring companies down. I mean, you were hearing all these stories about ransomware and just you know, terrible things that are happening with people stealing your data, stealing access to your data and causing all kinds of trouble. But I will say the good news is that the young people today, they're all on their iPhones, they're on their Samsungs, they're on their different devices, and they understand technology as as a first class citizen. Whereas a lot of us,
you know, middle aged folks. Let's say, you know, we've had the whole journey of knowing what it was like before even computers for crime out loud, before that was mainstream, where like reading books, and you know, you do have to I think every so often gut check yourself and mind check yourself and say, all right, do am I still lingering in these old habits and answers? You probably are, So how do you shake
out of that? And I think you do it by listening to people and just by going to events and going to webinars and things, and just taking a moment to hear what other people are talking about and to see and to watch how they interact with data. You know. That's why I'm so excited about transparency of data sets. And it's really come a long way in the
last i'd say ten years. And you guys, of course are part of this, but transparency data because it's only once you really start playing around or as they say, munging the data that you start to get a better understanding for what that process is like and how it really does take time. Even if you have the best technologies, it takes time to play around flatten things
move things around, see it in motion, you know. But that's when you start to get it and you're like, oh wait a minute, and that's the beginning of a career, right, Yeah, it's you know, it's interesting. We're you know, a lot of people don't know this. We now have a major, an analytics major at the University of Pittsburgh for
undergraduate business students. So these students are coming in in their freshman year and they're learning to program in Python. They're learning how to mind data, they're learning how to use that data and you know, uh, different models to make decisions. I mean, so they're they're graduating with with a language that most people at the organizations that are going into only casually understand. I find that is a very interesting sort of You have this group of really fluent in
technology new hires and then up to levels people are there. Their their level knowledge is what they've read, you know, from McKenzie or from you know, something from you know, towards data science or something like that that they are getting in the nuance of it that they haven't lived yet. So how do how do we deal with that? In these companies that divide between sort
of three level I'm not talking about eppmansion. I'm even talking about two or three levels up in those new hires, and that that that ability to translate what one set knows versus what the other doesn't. Yeah, well, I'll tell you one of the lessons of life that I've taken away is you've got to realize that you do have to reset and recognize there are a lot of things that you don't know and embrace the change. I suppose you know, and I try to do it on these shows by talking to smart people.
Of course, you are in the university space, so you're talking to the next generation of business executives. I mean, that's who these folks are. If you take a master's in in analytics, you know you're going to go somewhere in business. I'm quite sure you're going to have a pretty good career path ahead of you, and it's going to be constantly changing. So you
understand the data understanding. As you've said, the use case is the base camp of use cases, and that's of course what you're providing with fourteen eighty six labs, right, Yeah, so that the idea. You know, we're very fortunate we have a company named Liaison based in Boston, an ed tech company really really focus on helping the student journey and become successful. That solved the value in O Thoughts and they purchased O Thought about two and a
half years ago. I still remain as a president of the AI division, really looking at the next generation of products and so, you know, with that, I also have a lot of flexibility to be creative. And so what I wanted to do in combining sort of what Liaison does in creativity around the data and analytics side and the resources, especially the young sort of very energetic and very knowledge thirsty students, you know, put together an entrepreneurial lab
where we will develop companies at the intersection of data and analytics. And that's you know, fourteen eighty six Labs is all about that. So we have twenty one interns right now and they're building our first company, which is called Blue Street Data. And Blue Street Data is aimed directly at how do we make it easier for the buyers of data to understand what they're buying and what
they need to buy in order to make these use cases work? What powers those use cases, how do we understand it, and how do we get the right best, highest quality, lowest price data to power those use cases. Yeah, and that's really important. I can tell you that in my industry, I've tried a couple times over the years, and I was nervous each time to buy data sets of contact information. It's a very dicey thing to do. You typically get very low quality data and it's just hard to
know. I mean, you get all these different companies offering it, it's like, well, how did you get that data? Where did you find it? These days, there are technologies like seamless AI that we use somewhat extensively, which are quite compelling. It's almost like an engine that spins up a real time Yellow Pages for you to find contact information. And it's real time. It's not just a repository like a big data set. It's a
set of algorithms that in the moment will reach out and capture information. And they do persist a lot of it. But you have to ask yourself the question and like how good is this data? And that's the service you're providing, is being able to act as a liaison or a shepherd or a schirper or something to help people understand what are the use cases when do you use this stuff for? What purpose do you use this stuff? How do you use it? You have to be careful about how you use these things.
But I can tell you buying email databases, buying email lists very bad idea. Like you cannot just throw that stuff into production. I mean, if anything, you can use it as a starting point to start looking around and finding people. But there's a lot of work to be done, folks. There's a lot of time and effort that goes into this, and you have to be careful about how you spend your time. But don't touch that. That'll be right back. You're listening to Inside Analysis. Respect, Welcome back
to Inside Analysis. Here's your host, Eric Tabanac to share all right, folks, back here on Inside Analysis with Andy Hannah of fourteen eighty six Labs and oh Thought and the University of Pittsburgh and of course Blue Street Data and Andy, you were just staying in the break there that you are now looking at data quality through some new lenses. That's one thing that machine learning is very good at, by the way, is finding mistakes. You know,
people are looking for use cases for lllms. Finding mistakes in your code is really good stuff. You can just throw a bunch of goats, say where's the mistake, It'll find it. So there are lots of things these engines do that are not just text or image generative in nature. That's for fodder for another show. We'll do a show in a couple of weeks on that. But let's let's drive really dive into the quality converse because quality is so
important with data. Think about when you get an outreach they misspell your name or something that's not a good customer experience. What are you doing to improve quality and to vet the quality of data sets. Yeah, so I think this is a really interesting topic because when we first started to attack this angle, we went to all the typical sort of metrics around quality, the consistency, the comparability, the timeliness, et cetera, everything that everybody thinks about.
And as we were exploring it, we came to realize that, hey, so much of the quality of data is dependent on the particular use case and how important that use case is to the business. So it becomes very individualized, it becomes very difficult. Like you said, Eric, it's there's great technologies out there to do anominally detection, right or to define missing values or whatever it may be. But you often can't use those techniques unless you get the full data set. You can do it on a sample, but
as we know, sample rarely represents the full data set. So you know, one of our advisors is Malcolm Hawker, who you know well, and we started thinking about how do we need to approach quality? And this takes me back to sort of some of my days in the company called Plextronics, where you look at the supplier, right, and you start to evaluate what is the process that the supplier has in order to ensure that it's delivering a
top notch product, right, So it is the processes behind that. How many sources do you use before you say, hey, I have a valid sleeve of data related to a particular attributes, So it is that that as a transparent do they tell us how they do it? What's the source of the data, is an ethically sourced data, what's the quality of the company itself? And then of course we look at the you know, expert opinion about the day database sort of the data set or the data product, depending
of people who've actually used it before, the pros and the cons. So when you put that sort of how we make the product. Together with expert opinion, you have a much different view of quality than we typically look at when we're trying to find these anomalies or problems with the data sets. So it's like, to a certain extent, it's an expert crowd sourced view of quality because it's not just random let's say, Yelp reviewers, and many of
those can be fake. No, you have trusted sources that you rely upon to give you sophisticated analysis of data sets. Right that coupled with are they transparent in the process that they use to make the product? If you know, if you think about the material science world, you know you have your the spec of the product has to be on and you go in explore the manufacturing process of the manufacturer. That's what I say is all about. We
need that ISO type of concept here with data production. It's just like thinking about a product that any company might produce. Well, that's interesting because you're kind of hinting at or alluding to observability. Right When we talk about observability in the data world, what you're really talking about is getting windows into streams of information and then being able to correlate those to understand what happened. So this is what I love about open source. That's why I always tout open
source and I'm a huge fan for lots of different reasons. One is because many eyes make few the errors or as the old expression goes bad, code goes away. Right, that's one of the benefits of open source. Now there are downsides, which is it's hard to make money, Like if you're open source in the code, how do you ensure that you get the money. We were joking a couple of years ago that Amazon was strip mining the
open source community and not doing a lot for it. They have since changed their tune somewhat and now they kind of understand, which gets us back to ethics, right into being ethical and how you run your business, how you run your operations, and you know, there are these sort of competing virtuous and vicious cycles, it seems to me, and we want to put the balance of power in the virtuous circles where good virtue breeds good virtue, which
breeds good virtue and it keeps going up. But there is always this downward pressure of the vicious cycles. And how do you solve that. I think you solve it through good ethical constructs and transparency and just ongoing work. Basically, it's never going to be solved. To keep hammering away at it, right. Yeah, And the transparency, as we've talked about, I think every single segment here is critically imparent. It's transparency and what the data is,
how you're using it, how it's produced. But you said something that's kind of interesting that I think a lot about, which is you said use the words sleeves of data. And the more that I talk, you know, I probably talked to a dozen people every week in this in the industry about external data, and one of the most common things are that I have to typically as a buyer, buy bundles of data. I want streams of
data. I want particular data elements. Yeah, I have. I'll have to find a way to make sure my matching algorithms are able to to pen my existing database. I get that, But why do I have to buy the whole anchilada when I just want you know, the bup, you know, the uh uh, you know, the guacamole or whatever. It might be one of bad analogy, but you get what I mean is it's like, so you want that, you want the ability to slice down into the
individually into the granular level, and that's we really don't have that. That's not the way that data and information has been sold into pass and so we have to move to that, to that type of granularity, to the individual level of data element. And that's going to be hard. That's going to be hard for a lot of the existing companies who are doing it the old way to transform. But if we do it in the right way, we're going to be able to say, oh, for this use case, here's
a particular element you need to buy. You can buy the best data and it says a streame off of this company that's never sold data in the market before. Because so we give that opportunity for those types of companies to come to market with their data with the particular use that's better than anything else that's out there. That's the way to think about restructuring of the data markets, which we desperately need if we're going to move to the next level of value
from data. That's very interesting I was mentioning before, you're seeing early indicators for this kind of of technique or of standard, if you will, and one of which is like Google with your timeline and how Google will now give you your timeline, right. I mean, I think Twitter allows you to
download your tweets. Other platforms allow you to kind of download things, and there is value to that, but being able to reflect back to these Amazon is actually pretty good at this at being able to capture all of your transactions very quickly show you where they are so you can go find things. So you think about all this stuff you used to have to keep track of yourself, and now you realize, okay, well they're going to keep track of that for me, like a web portal for the hospital, for example,
I don't have to write down everything from my wife's appointments. I know it's up in the cloud, so I can go there. And I think the more we learn to trust those sources and rely on those sources, the more we'll be able to focus on what we're trying to do and not worry about trying to do all the other stuff that other people are doing for us. Right, Yeah, and it gets down you're right. So the way that
we capture information has dramatically improved over the past decade. The way that we store it so that we can track it down to any individual is what gives us the power to be able to do this. So it's going to open up a whole bunch of different revenue models. It's going to open up a whole bunch of different companies. I mean, we're really flooded with Jenai companies
right now. But I think the underlying sort of hidden gold is what companies are doing very creative things with the data to make it much more usable. And when you can combine that value of that usable data with these technologieses, just like we've seen over the past forty years, you're going to companies will merge that will disrupt their industries, will disrupt the other companies that they compete against. Yeah, no, that's right, and it's I think it's going
to be amazing. I think that all the tumblers are aligning right now, and it's kind of wild, you know. I think the more people get access to useful data sets like a data sleeve, as you're talking about, just a little piece to fill in a gap, the more success you have with that, the better. The problem is that people do get burned. I mean, one of my partners does content syndication, and I trust this guy as much as I trust any human being in the world. He's incredibly
professional. He's been around a long long time, and the projects they've done with us have been spectacular. I mean, the quality of leads is amazing. But it's still hard to sell. Why because people have been burned in the past, and once you get burned, you know, once bitten, twice shy. Basically, it's hard to kind of move past that. I mean with chatbots, you're kind of seeing this right now. Chatbots are coming back with some vigor. They first came out, I mean it first came
out like fifteen years ago. In the earliest days, they were just dreadful, right, it was just horrible. It's like, what is going on here? These people are making me crazy. So it's like they're you know, well, it's like Pittsburgh still to this day to people who have never been here, like, oh, it's not a dirty city. I'm like, oh my god, No, it's not a dirty city. It hasn't been thirty from it was from the steel days and that ended, you know,
in terms of the toxins and all that stuff. That's this distant past now, but it takes a long time for these things to die. Will podcast segment's coming up next but don't chut that out. You're listening to Inside Analysis. All right, folks, back your time of the podcast. Bonus segment here with Andy Hannah of fourteen eighty six Labs and All Thought and the University of Pittsburgh, and we're talking all about AI and data and ethics and
speaking of everyone's talking about responsible AI and ethical AI. And you know it's good because we need to have these conversations. And you, my friend, were recently given the chair of what is it, the University of Pittsburgh's Responsible Data Science Board? Is that right? Tell us about that? That is right? So we're we're really fortunate at the University of Pittsburgh where we have a lot of individuals that are focused on data science and artificial intelligence. What
we're focused on in a responsible data science community is three things. How do we make sure that the infrastructure behind all these models, etc. Are responsible From a curriculum perspective, from a research perspective, from a workforce development perspective. So we want to lead the nation on how we think about the deployment of all these technologies, regardless of whether you're in academia or you're in business.
My responsibility as chair of the Advisory Board is to bring industry into the conversation. So that very very thoughtful approach that says that we can't do this in a vacuum. So if we're working on then we literally use the words we're working on use cases and data sets to show how you can responsibly use the techniques that we've been talking about and solve real problems and so and those real problems come from industry. So we're focused on retail, finance, and
healthcare and i'll what i'll call civic applications of this technology. That's pretty cool. Well, you know when I think about policies, and I'm glad that you're saying you're bringing the business world into this, because that's really what it takes, because you know, you and I can have all sorts of theories about how things operate in a particular industry, but until you sit down with people from that business, you're probably not going to be able to figure out
some of the really critical components of processes and workflows. And they will tell
you. I mean, I remember, just as an example, many years ago, I was on as at a meeting I used to represent the downtown development district of New Orleans and we're in a meeting with the new executive director and a whole bunch of other people's stakeholders are there, including a lieutenant from the police force and a bunch of other people, and the new executive director was like, yeah, we decided we're going to come up with our own
police force and they're going to handle these stuff downtown. And you can imagine the lieutenant of the police force in New Orleans said, excuse me, what are you going to do? That's our responsibility. Why what are you talking about here? They've never even broached the subject with them, and it's like,
that's kind of a problem. You know, if you sit down with people in private first and have conversations or even have hearings, for example, to vet these issues, that's the way to get there and make sure you have the stakeholders at the table because they're the ones who are going to recognize those red flags. It sounded like a great idea in the other boardroom, but when you share it in the bigger context, you realize, oh,
I guess there are some problems with that. That's the point of having these meetings, right, yeah, one hundred percent, And you know, we have to look at it from every angle in terms of responsibility. And so the lens when I think about that concept are responsible. I really think about it from three different lenses. The first one is purpose. You know, why are we using this technology? What is the big reason why we're using
this technology? Is it to solve organizational societal issues? Or is it to cause some type of catastrophic harm? Right? I mean, that's the easiest way to think about it. So purpose is number one, So how that that perspective. The second one is all about ethics, So it's what is what is the intent of the individuals involved in the project. Are they looking to benefit the society or benefit the company, or benefit or everything that they're
doing. Is it is leaning towards the good as opposed to the bad? And then the last piece is about you avoiding bias or having anyone treated unfairly. And that's a really interesting piece of it because that's where we go back all the way to the beginning of our conversation on a diagnosis. Where are
people being disadvantaged because of this technology? And so if we can combine that purpose and ethics and fairness together, we're going to have a very powerful and reliable and trustworthy set of technologies and data that we can then build the next generations business own. Yeah, you just reminded me of manual cons categorical imperative.
You may you may recall act only on that maxim which you can at the same time will as universal law, which is a nuanced approach of do as you would be done by. Basically, it's like, if you were going to engage in a policy, ask yourself, would it be reasonable for
every other company to engage in the same policy? And if the answer is yes, then ethically speaking, you're on pretty solid ground, right m h. And first, you know, I like the physician's oath, right, So first we do no harm, right, So if you literally have that in your mindset, and so I think that's you know, the diversity of
people around the problem is a really critical one. So if you have a bunch of people that are the same gender, same ethnicity, you know, same level of wealth, whatever it is, trying to solve a problem,
you're not going to get a diverse view of the problem. So the people around the problem than the techniques that we use to determine whether or not somebody could be treated unfairly the combination of those two things very powerful, and you have to kind of maintain an ongoing balancing act, right, because you can also overreact to things. And that's something we see a lot in our world is pendulum swings one way and then it swings the other way, and it
swings one way and it swings the other way. You know, for every action there's an equal and opposite reaction, So you kind of have to take all that into consideration and just be reasonable. Right. There's something to be said for being a reasonable person or understanding there are differences and you know, not everyone's out to get you and all these kinds of things. You do
have to keep that all in mind. But I think the real key is, as you suggest, bring an ethically oriented mindset, what are we trying to do, how are we trying to do it? And then honestly gauging the success is it working? And it is nothing like the law of unattended consequences to give you a real ham dinger, or you think you're doing the right thing and it all falls apart. I mean, have you ever seen the video of Yellowstone when they let the wolves back in? Have you seen
that video? No, I'll have them mind. Look it up, man, it'll absolutely blow your mind. They did a study and they brought wolves back to Yellowstone National Park and they said the results were absolutely mind blowing. You have to get into it to understand, but basically it reset the balance of power amongst all the different animals that were there. The creeks came back, all sorts of things happened that absolutely blew people's minds, which shows you
it's kind of like the butterfly's wings effect. Right, One little change can have a very big impact, but you have to be open to the change, and you have to be open to tracking the change and just being realistic. And that's what business is all about. Right In the business, you have to be pragmatic, like if you go too far in one direction, you're not going to be in business anymore. But final thoughts, how do
people find out more about Blue Street Data and fourteen eighty six Labs. Yeah, so the easy go to www Dot blue Street Data dot com or www Dot fourteen eighty six Labs dot to learn about us from fourteen eighty six, how we're building next generation companies with really great students, and of course Blue Street Data, how we facilitate the purchase of high quality, right price NBC News on CACAA Lomel sponsored by Teamsters Local nineteen thirty two Protecting the Future of
Working Families Teamsters nineteen thirty two dot org. Thanks for tuning in for disposition of Justice Watch with Attorney Zulu Adli. I am Attorney Zulu Adli with a Justice Watch crew Rosa Nunez, Michael blau Clark, doctor Kilbasher, and Andrea Rohdeman. This week, like every week, we'll be discussing critical legal and social justice issues that are impacting our community. This week we'll be talking about the O. J. Simpson actually and the actual legacy of actually O J.
Simpson. And I think most of