All right, ladies and gentlemen, Hello and welcome back once again to the
only coast to coast radio show all about the information economy. It's time for
inside analysis. You're sure that the Eric Cavanaugh is here with the good buddy
of mine, one of my besties from the Pittsburgh 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, a Pachi Kofka 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 Kofka. It's now a huge
source and in fact it's a source of data for one of my other clients,
a company called Oceans. They just rolled out an announcement today the 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 conflut 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 longer 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 nw wor 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 others hard medium that you would carry around. And I was in
the printed uh 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 my portable discs I'd 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 disc
but I remember, you know, if you were in financial services, you
had multiple terminals on your desk, right, you had a Bloomberg terminal,
Tom and 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 when it came 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 an 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. That's what you would do,
and it moved pretty darn quickly, you know. Once I remember the
early days. We call them asps, right, Application service providers. And
I once had a smart guy in my show. I said, what's the
difference between aspns and suffer as a service and they go SaaS works. I'm
like, oh, I get it, smarty pants. But architectures changed,
and you know, 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, Apache Kafka is open source and
all the 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 recognize 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, THO 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, pull in Ukraine, you know,
the Czech Republic, et cetera. And what we did is we sent little
teams into each of those countries and they mine data. Literally we either we
got it somehow digitally or we went to copy machines and actually copied it.
And 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 database and they paid
per drink essentially, and so meaning that every time that they went when in
pulled information off, they would they would pay. It was a it was
a total gas. We had to put the first Internet Service Provider I s
P for all those who are kind of from a different generation, but we
had to put the ISP first I P and 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 have 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 so I wrote about this a
number of years ago, saying that it's a pretty unfair playing field if these
investment bankers can have 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 lake 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 came 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 four 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, 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 of out o 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 a thought. And that was a seven or eight year journey.
Wow. 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 as
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 will 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 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,
visit you in a high school, and all of a sudden, because
we are studying the data in your patterns, just like you said, we
see that that those activities raise your probability from twenty to eighty percent. This
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 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, GBT large language
models, which are of course are very very powerful. They are going to
require some efforts 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 that 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 through these. So if you're if there's bias in the data
or biased and 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 sub segment 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, I cannot unless you
do that. But you're hitting on something very important, which I think is
probably the most valuable proposition about a 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 ef 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 scientist, Mark Fordman,
he's actually developed what we call the automated data scientists. So does 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 Tavanaugh. Okay,
folks, back here on Inside Analysis with the one and only Andy Hannah for
teen 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,
I'll just throw one fun observation out there that I've 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 think. 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 that technology, which is the 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 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
the past 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, of the top companies
in the country using that data to understand behavior and to change that behavior to
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 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 Ver, 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 because it's a place where we can all dive
in and start 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
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?
Yeah, 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, you
get right down to it, these models are so incredibly complex that you could
offer complete 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 Grock. 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
suits 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, we're 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 tall 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
for by how the distribution of capabilities, the distribution of knowledge, and there
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 of 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, 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 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 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 iPhone, 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 crying 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 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 flat things, move things around, see it in motion,
you know. But that's where 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. See, 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, different models to make decisions. I mean, so they're they're
graduating with with a language that most people at the organizations that they're going into
only casually understand. I find that as a very interesting sort of You have
this group of really fluent in technology new hires and then up to levels people
are their 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, but
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 Eppermansion,
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 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 and
embrace the change. I suppose you know. I try to do it on
these shows by talking to smart people. Of course, you are in the
universities base, 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 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 know, understanding the data understanding. As
you've said, the use case is a base camp of use cases, and
that's of course what you're providing with fourteen eighty six labs, right, Yeah,
so that's the idea. You know, we're very fortunate we have a
company named Liaison based in Boston, an ed tech company really really focused on
helping the student journey and become successful. That solved the value in O thoughts
and they purchased A 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 those use cases. Yeah, it'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 database is 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 will be right back.
You're listening to Inside Analysis. Welcome back to Inside Analysis. Here's your
host, Eric Tabanac. 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 on 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 Lllmsing
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 fider for another show, we'll do a show in a
couple of weeks on that, but let's drive really dive into the quality conversation
because quality is so important with data. Think about when you get an outreach
and 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 time and liss 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 anomally 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 a 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 attribute.
So it is 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
expert opinion about the database or 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 and
you you you 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 you're kind of hinting at or alluding to observability rights.
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 and too 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 you know, 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 pen my existing database. I get that,
But why do I have to buy the whole anchilada when I just want
you know, the you know, the uh, you know, the guacamole
or whatever. It might be. What a 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 we
really don't have that. That's not the way that data and information has been
sold in the pass, and so we have to move to that to that
type of granularity, to the individual level 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's 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
next level of value from data. That's very interesting, and I was mentioning
before you're seeing early indicators for this kind 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 used to have
to keep track of yourself, and now you realize, oh, 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 for
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. 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 technologies, 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 I think it's going to be amazing. I think that all the tumblers
are align 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 this have been spectacular. I mean, the
quality of the 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 there, 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 god, no, it's not a
dirty city. It hasn't been thirty for 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. Well. Podcast bonus segments coming up next. Bot.
Don't shut that out. You're listening to Inside Analysis, all right, folks,
back your time with the podcast bonus segment here with Andy Hannah of fourteen
eighty six Labs and oh 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 sport? Is that right?
Tell us about that? That is right? So 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
and 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 in those real problems come from industry. So
we're focused on retail, finance, and healthcare and what i'll call civic applications
of this technology. That's pretty cool, 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 ourn 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.
You 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 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 it
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 this advantage 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 of business on.
Yeah, you just reminded me of emanual 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. 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, the 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 and
the techniques that we use to determine whether or not somebody could be treated unfairly,
the combination of those two things very powerful m HM. 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, for 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. It is nothing like the law
of unattended consequences to give you a Rohm Dinger, or do 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 that all of them? 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. 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 com. You can 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
data that can drive analytic engines. I love it, folks talking next time
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