Into his death is underway. The United Autoworkers Union is expanding its strike against
GM, shortly after the union announced a tentative deal with Stillantis. Yesterday,
the UAW said it will order four thousand workers to walk out of GM's spring
Hill assembly plant in Tennessee. The move comes the same week UAW reached a
tentative agreement with Ford. The auto workers have been on strike since September fifteenth.
I'm Chris Caragio, NBC News radiomor or on board kcaa's Inland Extress KCAAA
M Linda ten fifty am, the station that needs notice Here behind, the
information economy has a ride. The world is teeming with innovation as new business
models reinvent every industry industry. Inside Analysis is your source of information and insights
about how to make the most of this exciting new era. Learn more at
inside analysis dot com, insideanalysis dot com and now here's your hope to Eric
Cavanaugh. All right, ladies and gentlemen, Hello, and welcome back once
again to another episode of Inside Analysis, the only coast to coast radio show
in the US of A that's all about the information economy. Yours truly,
Eric Kavanaugh, here and what a treat. I'm actually dialing in from way
across the pond. I'm over in Vietnam, big thanks to FPT Software.
Their headquarters is right here in Hanoi, Vietnam, so I'm here for their
big annual events what they call their own cees if you will, and they're
doing some amazing things. They're gonna announce a big partnership later on tomorrow with
Landing Ai, which is a huge AI company. So just like lots of
other firms like Data Bricks and Amazon and Microsoft, these folks are going all
in on AI. Google of course as well is doing the same. Oracle
as talking about it, IBM is talking about it. Everybody is going all
in on AI and why shouldn't they? And for today's show, we're going
to take a little bit of a twist. I'm going to be interviewed myself,
that's right, yours truly will be the subject of an interview with DJ
Murphy of our ex Global. They are the folks who recently took over the
Big Data London conference in of course the UK, and they're also planning right
now Data Universe, which is going to be a nice big event on data
and AI and analytics and so forth in New York City next April eleventh and
twelfth. I'm very excited about that because we missed the Strata conference and these
folks have come along to fill that void. And my good buddy and longtime
friend and colleague, Mike Ferguson is the one who got me connected with this
group. He was the conference chair over at Big Data London, and I
can tell you it was a huge success. They had something like twenty thousand
registrants and fifteen thousand people show up, and they were very clever. They
gave the tickets away to the delegates. I don't have to pay to go
into Big Data London. Anyone can go in, and from what I heard,
the audience was absolutely fantastic. A lot of senior level people, a
lot of practitioners out there learning about different technologies. What's going on in AI
and what is not going on in AI? My goodness, it's everywhere these
days, between these large language models, so called foundational models, and the
transformers. Of course, I've got a thread going with Jurgen schmid Huber.
He's the guy who wrote the papers on the transformers, and you'll hear about
that in this interview with DJ Murphy and you hear a lot more. But
if you want to find out more about Data Universe, send me an email
info at insidanalysis dot com. And with that, here's my interview with DJ
Murphy. Take it away. So I'll give you a quick background on DM
Radio. I call it the longest running show in the world about data.
We've been broadcasting since two thousand and eight. It was first just online and
then about nine years ago, I guess we cracked onto the actual airwaves and
brought the show to radio stations. It's we're at about twelve or fourteen markets
around the country, some big one San Francisco, La Chicago, Atlanta,
et cetera, a bunch of different markets. And the whole point is to
just talk about issues related to data as a resource. So we talk about
data warehousing, analytics, data ingestion, data movement, data cleansing, data
quality, anything to do with using data at some significant scale. And we
typically get three guests per show, and the whole idea was to create this
roundtable style discussion where each guest gets their own segment of about eight to nine
minutes to talk about what they're working on, what they see in the marketplace,
and then we all do the roundtables at the end, so it allows
everyone to get their perspective out within the context of this conversation, and then
the roundtables allow us to kind of tie it all together. So we're in
year sixteen. Next year is going to be our seventeenth year, so we
talked to lots and lots of folks. A lot of things have changed obviously
over the years. And then we have another show, inside Analysis, which
is really all about the information economy, new jobs, new roles, new
titles, new business models, new ways of doing things. Anything along those
lines qualifies for that show, so it's a bit more broad and focus.
And then about four years ago, I guess I got nudged into creating a
TV show out of this stuff. We have a show called future Proof,
which is really just repurposed clips from the radio shows. So we have a
student in Chicago the records. They do all sorts of different camera angles to
make it feel like a TV show, and then we just carve that out
and put it onto about six or seven markets now around the country, including
Washington, DC. So we just got our own time slot in DC on
Channel ten. Which is like one of the cable companies there, Channel ten.
We got our own time slots, so we're pretty excited about that.
That's crazy. Yeah's great. These are all things that we can used to
promote the show and obviously get speakers, get luminaries, advisory board members,
really whoever. That's fantastic, what a platform it is. And I'm we're
so really so happy that you're you're able to help out on this. Yes,
it's great. Are you based in Chicago? No, I'm from Chicago,
but these days I'm based in Pittsburgh's okay, I asked, we were
just my family was just out there last weekend. My son is a freshman
at De Paul, so oh, congratulations. Yeah, No, it's great.
Where I am. We're in New England, so it was kind of
a trip for us. But I love it. I went to school in
the Midwest, so familiar with Chicago and loved and I was really happy when
he decided to let's agree that. Yeah, it's great. It's an awesome
neighborhood, love the area and so forth. So yeah, so you kind
of got into this just a little bit there. Do you do you have
sort of a basic message or fundamental themes that you hit when you you know,
each time you get on the air, whether it be radio or TV.
I mean, what yeah, okay, go ahead. The standard approach
I take is that we do topics so and I can send you if I
haven't already, I'll give you a list to the edcal And we actually just
folded the last five years of Bettertal calendars into one Google sheet, so you
can kind of go back in time and see what was last year, what
was two years ago, three years ago, all that stuff and all the
guests. So if there's anyone you ever want introductions to, just let me
know. Connected to probably almost all the people who've been on these shows.
But we do specific topics, so we did yesterday we did a show on
the modern data stack versus hyper scale data warehousing. Right. We had the
guy from OC and Tom, we had an analyst on. I know a
lot of these independent analysts too, so we can get those guys involved however
you want. I know, Stephanie said that you're not doing a call for
speakers this time. It's all going to be curated, which is probably smart.
You know, go with people you know and people you can trust to
do a good job. So I'm happy to make some introductions there. But
basically the shows all have roughly the same calculus, if you will, and
that we define a topic, we go out and find speakers who can speak
to that topic, and then I just tell guests, think of two or
three key points you want to make on this topic. We'll discuss them in
the pre call, which is about fifteen minutes, and then I'll use those
as guideposts and navigate through a natural conversation. So we never want to thing
scripted. Obviously, we do want to know what we're going to talk about,
and then what we find is that there's always something interesting. So a
lot of times we'll pivot halfway through because we just pick up on this interesting
thread. And what I figured out and this I was hoping this would be
true and it's been very true, is that when you get several experts in
the same domain on the same show, they get excited about that because usually
they know each other. I mean, I can't tell you how many times
I've had people go, oh, I've always loved your work so and so,
and it's great to be on the show with you and stuff. And
what's interesting is that to me one of the real values is just the experience
itself. So we're kind of in the experience management business, and that we
just give really cool audio visual experiences to experts in data, to experts in
technology. We get them all excited about stuff and then go on about our
day. Right, right, Why data? Way back when, when you
were sort of putting this idea together, why did you land on data or
you know, what was your experience at the time. Sure, So I
worked for a company called Damon Consulting back in two thousand and one, and
they were a data warehousing consultancy, so doing ETL jobs, extract, transform,
load, filling your data warehouse, et cetera. And the analyst in
me just thought, I mean, this is going to be huge because there's
so much information. And it's funny because I knew it would be big,
and I've been blown away by how big it got. I mean, I
didn't even realize because I didn't back then see where the nexus would occur between
data and AI. Right, And that's really the bottom line is that to
train models, whatever model you want to train, whatever you want to do
with AI, you need some kind of data as the foundation to train it.
To use it to execute it. So data is going to be crucial.
Well how do you get to how do you understand that data? How
do you get to data literacy? So I saw that back in two thousand
and one at Damen Consulting, and then I went on my own for a
couple of years and I figured what I wanted to do is represent consulting firms
in data because the consultants are the intersection between the clients and the technologies,
right, so they have to understand business speak, the business problems, the
challenges also the data, and then also know that which technology is to use
and when. And that obviously is a very challenging situation because you can look
at a vendor's website and still have very little idea what they do. I
mean, I've had many experiences where I'm like, oh, you sound like
these guys like are you competitors? Like no, we're partners. I'm like,
all right, did not get that from looking at your website. So
you have to get into the weeds a bit to understand. So I focused
on doing that for a couple of years, and then I wound up getting
the Data Warehousing Institute as a client, and then I wound up working for
the Data Warehousing Institute as an employee and help them launch their webinar series back
in two thousand and five, which has been going ever since. And with
that we took the same formula, right. I love having formulae for creating
content because that way it's consistent and people know what to expect. And so
the formula back then was, Hey, you guys have this whole roster of
consultants who come and teach at your shows. They're not employees, but their
partners. I said, let's get abstracts from them of what they're working on,
what they want to talk about, and then we'll shop those abstracts to
the vendor community to matchmake basically because TWI is a big list, and these
vendors they all want demand gen right, that's I mean, they want content
and they want demand gen. There's pretty much two things they're always going to
want. So we just created this beautiful marshaling area, if you will,
for conversations around data and technology and that's been rocking ever since. And then
after that, I actually pitched them rady to them. They didn't want it,
so I was like, Wow, I'm going to do it. You
know or know you want it. So I jumped ship and wound up partnering
with Source Media, which is based in New York. Media company you know,
I know Source Media, Yep, American Banker, all these guys.
They were my partner for the first I don't know, four or five years
or so, and then that went away and I worked with the Diversity for
a while and now we're just kind of on our own. But that's where
the data journey began, was like twenty two and a half years ago,
and it's just going full full speed ahead. Well you you were way ahead
of that. I mean, it is all about what you foresaw at that
point. So let me and you talked about AI and how they relate is
that. I actually had two questions here. One was like, what's the
hottest topic you're talking about on the show right now? I get the feeling
that it's AI. Right. Is AI sucking all the oxygen away from other
important topics or is it just that important? Or how are you managing you
know those discussions both? YEA, yes, it is absolutely sucking the oxygen
out of the room, without question, also sucking the money out of the
DC right. Really, because I was talking to a guy who was a
really sharp guy. I would recommend as a speaker Brian Raymond from Unstructured Io.
And what they do they call it et L for LLMS, so extract
transform for large language models, and all this stuff is really geared around embeddings.
And embeddings, as you may know, are in human terms, they're
like your memories. They're like what you've learned, right, y'all walk around
with this whole stack of embeddings in her mind. And as you can imagine,
the embeddings from your earliest days are the most powerful because they're formative.
Right as you're born into this world. You just have all this capacity to
learn and figure stuff out, and your embeddings it's like you're schooling, right.
Those are embeddings, Like children go to school and they get embeddings all
day. And I'm kind of driving this point home. So with AI,
it's the same kind of thing. They have these large language models, which
are absolutely fascinating. I mean, there's like fifteen or sixteen of them now,
and they are these modern era deep learning neural networks basically, and so
what they can do is fabricate text. And you probably know this, but
all they're really doing is predicting what text they think you want based upon your
prompt. Right. The problem that they have is that they will make stuff
up, and they make stuff up because that's what they were designed to do.
They're basically designed to fuse vectors of information into a string of text or
into an image according to your instruction. Well, how do you avoid the
hallucinations is you train the models on your data, on your trusted corporate data.
So one of my fun topics here, and I'll even throw this out
is potential session is large language models represent a second chance for data. And
what I mean by that is that we have spent forty to fifty years now
doing all sorts of calisthenics around trying to organize data, analyze data, integrate
it, curate it, do all these things in lots of systems. And
there are, of course databases, there are analytical systems. There are data
catalogs, which are a fairly recent entrance to the market, well fairly recent
fifteen years or so, twelve years, and there are all these wonderful things.
But it's a mess out there, and there are all these different ways
of doing things. And if you were to look at the if you were
to appreciate the corpus of data for any organization, it's a mess. It's
all over the map, it's in all sorts of different formats. It's in
your email, it's in your powerpoints, all these different things. Well,
Brian, what unstructured does is they will take your powerpoints, your PDFs,
your word documents, your email, whatever, They'll extract the semantic meaning out
of that and then publish it as an embedding in a large language model or
in a vector database. Basically, so they are tackling that side of the
equation. Right. This is not a perfect metaphor, but if you think,
let left brain, right brain. Let's say, let's say the left
brain is your ability to talk and to have vocabulary into no words. Your
right brain is everything you remember. It's the basically, it's the repository of
information you use to answer questions. And that's what we're seeing here with these
large language models. So what you want to do is use the LLM for
its text generative abilities, for its image generative abilities in that case, but
you really want to make sure that it's leveling your trusted data. So what
does that mean? It means all these companies have to find a way to
get their data into embeddings, and what I'd suggest is only put the good
stuff in. It's like, you know, I'll break my own rule and
letting our kid watch TikTok. We try to make sure we know what he
sees. But those are all little embeddings that are coming in. They're picking
up all sorts of little things, and it's like, you want to be
careful about what goes into that that matrix. Right. So he is a
good guest, and he used this term that I just absolutely love. He
said these embeddings are anchors of truth. And I was like, oh man,
yeah, that's good, right, because you want they're basically moorings.
They're gonna they're gonna bend the wave, like bend the light wave toward them
such that it'll pick up the interesting things and give you good content on the
other side. Right, So, and then other you know. So just
the other day on a radio show, I learned half of what I know.
On these radio shows. We had a Bill French who talked about how
Google's next thing is they have built a vectorization layer underneath their entire architecture such
that if you're using bards. It's not in everyone's account yet, but I
think it's coming. You can type at drive and then at Gmail and it'll
automatically point to your corpus of pre vectorized data in your g drive and we'll
pick that up right after the break. You are listening to Inside Analysis.
Welcome back to Inside Analysis. Here's your host, Eric Tabanaugh. Okay,
back here Inside Analysis, and here is the continuation of my interview with DJ
Murphy of RX Global and Data Universe. So here Unstructured is trying to make
their money telling you, hey, we can vectorize all your content. Google
is like, if you're on GCP, we already did it for you.
Don't worry about it. Like what I mean, think about how crazy that
is? So long story short, Yes, LLLMS and AI and the Foundational
models are absolutely sucking in all the energy. And it was Brian Ramy who
told me that there's like thirty billion dollars in investment in AI related to LLMS
in like the last nine months or something. So that's clearly because you know,
the vcs are always trying to predict the future, right, and they
always they always move in packs. There's usually a first mover and then all
the rest go in behind. And you just saw this with Amazon throwing four
billion at Anthropic. How much that's a lot of money to throw it at
something. So they all see it, and I see it too, I'll
tell you honestly, even though the hallucinations are a problem. And again,
and that's when you don't have good enough moorings, don't have enough angers of
truths, that's when you get the hallucinations. My personal opinion, there will
probably be people who disagree with me on this. I don't think there's anything
that's not in the crosshairs right now. What gets done because when you get
down to brass tacks, access to data. So data retrieval, data persistence
are crucial components of every application, no matter what you do, any software
you use must have data otherwise what is it doing. You know, it
needs data as it's fuel. It has some sort of form of data as
its output. So what's not in the crosshairs. You can use these llms
to write your emails, you can use it to write articles. I mean
you have to fine tune it. I think if you're going to use that
responsibly, you have to be careful about that. But even in my opinion,
and this is very controversial, I think a lot of the value that's
being wrought from data warehousing environments, which is very expensive stuff, can be
delivered via these new foundational models. So, in other words, in data
warehousing, they put tremendous amounts of effort into extracting data from source systems,
transforming it into a schema that can fit into the warehouse, then managing the
warehouse doing analysis on that aggregated data. Well, you know that's very expensive,
it's very time consuming, and the bottom line is you're in terms of
what gets actually used, what actually generates value for the business is probably five
percent of that data. So in other words, you're theoretically wasting ninety five
percent of your time in this old model. Now, the one person if
you look at the big companies, you've got Terearra Data, You've got Alterics,
you've got Data Bricks, you've got Snowflake. These are all the sort
of big data warehouse data analytics vendors. Of those, The one who seems
to really know what's happening here is Ali Gatzi of Data Bricks, because he's
got who threw one point two billion to buy Mosaic whatever. It was six
seven, eight months ago, and the week that deal cleared, I had
Navin raw on Damn Radio. So I interviewed the CEO of Mosaic like that
week, and he's very smart guy, as you can imagine, and he
was saying that you know, right now it is the marketing is the content
creation stuff. But I'm here to tell you that is just the beginning.
And then interactive AI is what they're really talking about. Because I'll throw this
out at you and then I'll be quiet for a minute. If you think
about the complexity of these models learning to reflect back fully syntactically correct, accurate
pros, it's pretty complex, well machine to machine conversations, even though it's
very difficult for most people to understand what they're doing. They're much less complex
than that, which means a foundational model should be very powerful in terms of
optimizing your current information and systems architecture in my opinion. So what does that
mean? I mean you're already seen a couple of companies do interesting stuff here.
Hammer Space is one. I was on the Data Unchained podcast just earlier
this week, and they're doing They are the company that is training Lama too.
So the new model that's coming out of Facebook. They're actually doing the
training of the model with their data, and they do this hyper efficient data
movement where they only move this piece of data or that piece of data,
which is a stark opposition to ETL. In the ETL world, you extract,
transform, and load huge sums of data into your data warehouse. And
the point I'm making here is that I think we're going to see crazy efficiency
gains using foundational models in AI to get just to the nuts that you want,
so you don't have to shape the whole tree, gather all the nuts,
move them across town to a warehouse, then package them, then ship
them out to people. No, you're just going to use teeny tiny little
little drones to deliver eight acorns to this address in ten acorns to that address.
Wow, it is moving. Well, it's certainly moving faster than I
have any sort of conception of I'm glad there are folks out there like you
that have some sort of conception of it. And that actually leads me to
kind of what I wanted to ask next about business users and kind of the
state of their expertise or literacy on average, Like what you see in across
businesses. Are people ready to use this stuff and leverage these advancements? Yeah,
yeah, even the ones that are available now, and not even what's
coming down the pike. I mean, just rough aest me. I'm a
big fan of the Peretto principle, right, the eighty twenty rule probably,
and there were always breakdowns from there. Right, It's like the twenty percent
of the twenty percent, so probably four percent of workers are all in now.
They totally get it, they're working on it, they're focused on it,
and they're they're excelling. Overall. I'd say twenty percent are open to
it, they've kind of tested things out, and the other eighty percent are
probably just too busy doing other stuff, right, And that's the problem,
right, is like, one of my favorite people in the world is a
guy who got me in fact, a guy who signed the forum to get
dm Radio launched financially back in two thousand and seven. I guess it was
David Greeno. He had this term, and I just love these expressions because
they communicate so much. They're so pithy. He referred to the tyranny of
urgency. I was like, it's painful to hear that you know, and
that's what most people are dealing with, is the tyranny emergency. They got
to get through their emails, they got to get through all the stuff they
have to do today, you know, then they got to get the kids
to Google and all this stuff. And so that's what's sort of driving the
reluctance or the inertia on being able to leverage these technologies. But I think
that is that's going to change pretty quickly. I think it's going to be
you know, thirty seventy within three months. And you know, Microsoft is
a reason for that. Google is a reason for that. You know.
The fact that you don't have to go out and buy some software and install
it, well that's big news. I mean, honestly, if you if
you wont my honest opinion on one of the most important things to have right
now in the software world for survival. It's called install base, like just
people who are already using your stuff. Right. So, I got an
email from some girl at Google yesterday saying, Hi, I'm your account executive.
I've never heard from any account executive Google first time ever, and she's
like, oh, prices are going up and this is why et cetera.
Et cetera. But she pointed out that if you're logged into your Google account,
you're just going to get all this new functionality. Right, So the
fact that you don't have to go out and buy something like you used to
in the on prem days, that's a big deal. So that's going to
reach the tracks to usage. Now the question becomes, you know, how
useful is it. Have they sorted out the guardrails? I mean, that's
one of the issues, is the guardrails on this stuff, and so what
are those? Well, to a certain extent, those are embeddings. You
know, you can do things different ways. There's this RAG model that came
out of Facebook and it has to do with how you actually persisted. So
retrieval augmented generation is what it's called IF the strategy that helps address both l
and hallucinations and out of date training data. So basically, you use you
go into the language model, you pull out your stuff, and at the
last minute you're like, all right, let's check and make sure that's right,
and you know, let's throw another guardrail, if you will, into
the picture. And so guardrails are going to be really important. But the
problem is everything is moving so fast. It's like people just want to go,
go, go and use this stuff. You do have to be careful
that you know, it's not hallucinating, it's not making stuff up, because
you know, for example, chat GPT. The first time someone searched me
that I saw, they showed me what it said and it was very accurate.
I was like, wow, that's pretty impressive. The second time I
did it, it said I'd written three books. I was like, are
you sure. The usually don't write a book and then black out, but
you know, yeah, So it's just you know, that is going to
be a real serious issue going forward because you know, think about it.
We've had to deal with fake news and now we have this issue of hallucinations
where these powerful engineers is making stuff up. And when I was watching the
Data Bricks conference, there was a girl doing a great demo about how they
are allowing the training of these models. And that's what you do, is
you train it basically. And here's one of the sort of dirty secrets,
if you will, well of old deep learning modules and old AI and new
as well. I had a guy in the show. I'd have to look
up his name. He's a very smart guy. To Boston somewhere and he's
one of the experts on deep learning and AI, and he told me a
stat that blew my mind. He said, when you're training this stuff,
and that's when you have human beings just saying yes or no. You may
have seen the thing is as a blueberry muffin or a dog, that whole
thing you you come across that because like the AA engines have a hard time
person figuring it out. He goes, As it turns out, very few
people are good at that task because it requires you to be very focused and
to always answer correctly. It's like those stupid captures that you'll get when you're
trying to log into a system and like, okay, which are these pictures?
Has a motorcycle in it? Like really, what do I And that's
a fun story too. Did you know that you're actually training models when you
do that? I did not know that. Yeah, that's Google and they're
doing They had this crowdsourced effort basically to train models to identify stuff. So
you're when you do the capture, you're actually helping train a bit model for
the future. Do you think that's become its primary purpose and not not the
security or whatever you know, that's a very very good question. Honestly,
I absolutely detest those captures and I understand why they're it's necessary, but they
always force me back, I don't know it's it's and they're they're vague sometimes
to to the to the point of of you know, them using it to
maybe that's what they're experimenting with as far as the training goes. But right,
But yeah, so that's kind of interesting. So like, aside from
AI, where are do you see trends or anything where people are investing their
time as far as professional growth goes for businesses and employees within these businesses,
what they're kind of looking at, Well, clearly they want to educate themselves
on AI. That's that's got to be the biggest thing. But well,
data is data can the important thing. So data lineage, data quality,
data movement, data pipelines. That's a very very hot topic because data pipelines
are these as the name would suggest, connections from sources to targets. So
what used to be just ETL extract transform load is now much more sophisticated where
you'll pull from this system and what they do is they'll use a graphic interface
where you have different nodes on a pipeline, and one note will be to
access the data. One no will be to check the quality of the data.
One note will be to transform the data into a different scheme of for
example, one note will be to enrich the data, so add some information
from another source. Basically, data pipelines are really really important. Real time
is really important. That's a big deal. There are other technologies too that
are still important even though they're kind of on a back burner. Blockchain is
still interesting things going on in that space around durability but also immutability is a
big one. But they blockchain has had trouble, you know, finding its
its relevant use cases. But I think it's it's still out there. It's
still an important thing to have and we'll probably come in handy. And then
just analytics in general, being able to number crunch hyper scale is an interesting
topic in and of itself. We had Chris Gladwin of Oscient on our show
yesterday, and they're based out of Chicago. In fact, he's from Lamont,
where I used to be the reporter and editor for the Lamont Metropolitan newspaper.
That was my first job right out of college. They stumbled into a
newspaper job. But they're doing hyperscale data warehousing where we're talking, and they're
using these new ENVM cards which are so much more powerful than the old ones.
So he was talking about how when you retrieve data it's in packets,
it's like, you know, four bits basically, Well these they can two
thousand, so you're talking about five hundred x. You're talking about orders of
magnitude greater. And they're just doing crazy stuff, these huge, huge data
warehouses petabytes of information, you know. So hyper scale is an issue.
Cloud is an issue. Of course, security is always going to be an
issue, you know that. And with that, will take a quick break
and we'll pick up the conversation once again. Right after these messages, you
were listening to Inside Analysis. Welcome back to Inside Analysis. Here's your host,
Eric Tavanaugh. Okay, folks are back here on Inside Analysis in Media
Race as it, were talking to DJ Murphy about hot trends like hyperscale and
cloud and security. And here are my thoughts on security and passwords. I
think passwords are the biggest pain in the freaking universe and I'm still not entirely
sure why they haven't solved it. And they're starting to with the biometric stuff.
You started to see more apps on your phone leverage that. So,
you know, security, even though the audience is pretty small, it's always
important. And governance. Governance at the other really big important topic. How
do you govern access to these data assets? Because in the old days you
had two options. Basically, you could govern it at the database layer,
meaning you're allowed to access the database or you're not. Or you could govern
it at the application layer meaning okay, user, your role is X,
you're a manager or whatever, I'll govern it that way. Both of those
are very inefficient ways to do it. Certainly the database one is incredibly inefficient
because you only want your database administrators touching that. And you know, if
you have four thousand people who are allowed to use this analytics software, you
know, having Bob go in there every day and take out Susie, Okay,
go out, I mean, you know, automating that, that process
of governance is huge. So I want to turn to the event a little
bit. You're a broadcaster. You want to get your message out to as
many people as possible. How important do you think like a micro sort of
situation like a face to face event is or it's like the propagation of knowledge
or or whatever. I mean, you're obviously in this, you want to
you're helping us out and you're advising us on this. How important are the
are the face to face events? Oh, they're hugely important, you know,
And we're seeing that come back now, roaring back. Obviously with COVID
everyone went into hiding for a period of time and for lots of different reasons,
for good reasons, obviously. But virtual will only get you so far.
And I'm a virtual guy, so I do virtual stuff all the time.
I love virtual it's really important. But person to person, shaking hands,
talking of people, sitting down, it's always important. It's always going
to be important. And these these focused events are are really crucial, you
know, because I've gone to a bunch of different conferences over the years.
I've been involved in promoting conferences and speaking of conferences, et cetera. And
the sort of broader consumer technology ones aren't so interesting to me because I'm a
data guy. So you have to have a very focused conversation. And this
is what trade press is all about. And you know, frankly, trade
press, in my opinion, is dying. It's been dying for a number
of years now because largely of the Internet, because trade publications used to make
all their money on print dads in magazines and now those are gone. So
take an industry and just rip the financial carpet out from under their feet.
That's a problem. So you know a handful of being able to pivot to
webinars, which are one of the biggest ones, and obviously events, but
absolutely events are huge, They're very important. The session design is always important,
you know, making sure you've covered all the bases of you know,
who wants to learn about what? But I mean no doubt in person events
are awesome. Do you think there's a takeaway for this particular event that's going
to be important for the people that attend. Granular knowledge, you know,
workshops like have a combination of sessions. Round Tables are always fun. I
love roundtables and I'll have to moderate roundtables in part because you get the chemistry
of the group involved, right and with a single presenter it's just one person,
even two. But when you have a round table, especially if they're
you know, open minded to how that gets managed, you can sort of
build up a momentum and a chemistry that you can't get in other sessions.
Now you have to have people who moderated effectively obviously and really know what they're
talking about. But that's that would be one of my suggestions is have numerous
roundtables that are really interactive and dynamic right where you go in. I always
tell people, I always joke, we'll talk about what we're going to talk
about, but once the bell rings, all bets are off. Yeah,
it's live conversation, so you know, we'll see where things go. So
I'd be sure to do that. And then you know, meet and read
sessions and free cocktails. That's always that's the best part of a live event,
isn't it. That's what I tell my wife. I go to these
events for the free food and the free cocktails and like joking, half joking,
but it does helps people, you know, especially at the end of
the day, like the six o'clock thing, the seven o'clock thing, you
know, that's when everyone has been talking all day business. They want to
go, you know whatever, drink wine or even a coke or something and
just chat about what's going on. All that stuff is really important. As
someone advising on the program, What do you hope that this event will deliver
to the community that you communicate to on a you know, daily or weekly
basis, that may they may not be getting elsewhere. That's a hard fact,
you know. So Mike Ferguson, he's the guy who suggested that you
all talk to me, who's an incredibly knowledgeable guy. Like getting a dozen
folks of that caliber too. I won't call them fact checkers because I don't
like all fact checking thing, but they what's really cool is that when someone
when these vendors know that there's someone in the room who knows all their weaknesses,
boy do they behave they just know we learned this from experience that you
know, if you get a round table. When I get the end,
I always want an analyst on the call. If I can three vendors like,
it's amazing how constructive the conversation becomes because number one, they don't take
pot shots at each other. Number Two, they don't talk about stuff they
don't have, you know. I mean I often joke that talking to especially
a bigger enterprise software executive, is like talk to a politician. I mean,
they have their messaging down, they have their talking points, and you
got to find some way to get them off with that message, to get
some gritty detail, and that's usually a curve ball of some kind or you
know, some question that pops up and you have to wait and let it
kind of flow out, as opposed to you can't badger someone, right,
this is all it's all friendly media. I don't do sixty minutes type stuff,
right, I'm always doing friendly media, But I want to get to
the meat. I want to get to the hard facts that people need to
know, like, oh, you want to use this technology that's out knowledge.
You know, they don't actually work well, or they are maybe they're
very complimentary. You don't know that stuff until you've talked to someone who has
used it, who has built the solution, you know, who's worked with
it. That's where you find the bugs and the sort of rough edges on
things. And that's that's what I would love people to get more of,
is you know, what are the rough edges in fact that could even be
an interesting show or an interesting conversation is you know, what do people think
is the case versus what really is the case? AI? And it's like
we learn that with that New York Times guy who interviewed chat GBT when it
first came out, and I try to tell people he is responsible for that
conversation going sideways because he misunderstood what the tech is. He's treating it like
a sentient being, trying to, you know, get it to say colorful
things, which it clearly did. But again, all it's doing is reflecting
back from the corpus of techs that it was trained on what it thinks you
want to hear. So you know, prompt engineering, for example, is
absolutely do workshops on that all day long on prompt engineering, because that is
it's really crucial to get what you wanted of something, to get very precise
instruction, and it will take that instruction like. That's the thing is,
it's like a little assistant who will do everything you ask it to do as
best as they can do it, you know, And I always tell it
please and thank you always. It won't filter out your worst impulses. It'll
just always be nice to the machines. You know, they may wake up
one day, I don't know. There you go, There you go,
Eric, thank you very very much. What I'm gonna do is I'm going
to go through and do this up sort of as a Q and a I
will shorten your most of your answers tragically unfortunately, but I am going to
send it to you before we publish, just in case I've edited something the
wrong way or whatever. And let's shut. Okay, Thank you so much.
I appreciate you recording it. Send me the transcript when you have a
chance. And this has been great, and hope to have a chance to
meet so. And that concludes my conversation with DJ Murphy from our ex Global
Read Exhibitions. The folks who are putting on the Data Universe conference in New
York City April eleventh and twelfth, be sure to check that out. We're
working on the speakers right now, working on the themes, working on all
that kind of fun stuff. Should be a fantastic show. And look for
yours truly to be walking around. If you want to offer some advice on
what you think the show should cover, by all means, send me an
email info at insideanalysis dot com. You know, I'm always keen to find
out what folks are looking to learn, and these days it's going to be
a lot about those foundational models. It's going to be a lot about how
to train large language models. It's going to be a lot about what else
is in the crosshairs, And to be honest, folks, I don't think
anything is not in the cross hairs of these foundational models. We're going to
find out. But at the moment, according to Navin Raw of Mosaic mL
and several other experts I've talked to, yes, it's mostly marketing copy.
It's mostly copy for blogs and articles and emails and all sorts of other things.
So a lot of that stuff's going to be going on, but I'm
telling you other things are in the cross heres. Interactive AI is going to
be huge. Interactive AI is where we're going to get much deeper in terms
of letting AI run systems and optimize information architectures, optimize how things get done
in the real world. That's coming soon to a an enterprise near you.
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