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and inside Analysis dot Comside Analysis dot com. And now here's your host,
through Eric Kavanaugh. All Right, folks, hello, and welcome back once
again to the only coast to coast radio show in the US of A.
And it's all about the information economy. It's called Inside Analysis. Yours to
the Eric Kavanaugh here, and we're with our good buddy Eve Mulkers today,
the founder of seven W Data over there in Belgium and I are kindred spirits
trying to understand what's going on in the data biz, and there's a lot
going on. There's some news. I heard our friends at Okaira have been
acquired by Data Bricks, So I'm glad to see a happy ending there for
the Okira folks. That's actually a very interesting space in and of itself.
Even maybe I'll just kind of comment on that and get your thoughts on it.
But this concept of dynamic governance is kind of interesting. So what the
Okaia folks figured out is that to manually handle all access to information systems like
sensitive data sets, for example, it's just too hard to do. It's
going to take too long, and it's a really menial task. It's just
basically assigning privileges to someone. And it's more complicated now than it has been
in the past because there are lots of different data sources and you can't just
easily tie an access level to a role. So a lot of times people
would connect to active directory or eldapp or some of these other solutions to ascertain
okay, this is a director or a manager that should only be seen by
a VP. That stuff sounds like it can solve the problem, but usually
it's just not granular enough. And so what Okia did, and there's also
Immuda and previous Sarah, there's a couple others. Now there's a whole space
kind of opening up right now. And what they decided was, look,
we're going to come up with an engine that will dynamically determine upon query time
whether or not this person should see this data, which is pretty clever.
So Data Prince has now acquired it. From what I understand, Okira,
it's okay, E r A. But what do you think about this space?
I think it really does speak to the maturation of the industry that the
old fashioned way of controlling access to data systems manually is just not going to
cut it these days. But what are you thinking, Yeah, I joined
that that division. Security has always been a problem, an issue maintaining it.
From my developer days, we always wanted to have access to reduction data,
for example, and then it's give me the admin rights because well,
you know, you trust me as signed the security documents, so we're pretty
fine on that, but maintaining it because people enter companies, leave companies,
you're working with partners, how does that all tie into your ld up?
Like you're saying, that's that's pretty hard to maintain that, And with the
variety of sources what you're getting, how do you maintain that and pull that
into a data product. So definitely that's a market that has a lot of
potential on getting that finally in a certain way, right in an easy,
maintainable way as such, because like you say, before, was okay,
director's day can see all of this level of type of data. Financial sensitive
data, personal data was only reserved to HR departments, but how do you
set up this matrix before you have it finalized, it's already outdated and that
was a big challenge on on maintaining all these roles and responsibilities and rules in
place. So I think, yeah, if you can do that and really
at real time, it's always an advantage to have that in a more agile
way where you can say we deploy a new source and the security is handled
by various dynamic rules. Maybe you have a fixed set of rules as well,
but on the other hand, you can define which makes sense that a
financial director can see more financial data than somebody else who doesn't have anything to
see with that financial aspect, and the saying goes for marketing or sales type
of data. So yeah, I think Data Bricks made a smart move on
acquiring a CARA. If you see, they are kind of being positioned of
the central data leak as such, and they're making big kicks in moving forward
on that. Yeah. Well, you know, it's funny you think about
governance as controlling access to systems by people or from people, but really there's
also the algorithms and so as AI really takes off, and I don't think
it's going to stop this time. We've had a couple of AI winters over
the many years because the ideas showed up and even the algorithms, but we
didn't have the processing power. It wasn't inexpensive enough. And that's no longer
the case. Now we have tons and tons of processing power. It's very
cheap and you have all this data, and as companies look to leverage AI,
you have to be careful about which data you give that algorithm access too,
because you know it as models train. If you just open up your
kimono and let it see anything, there's going to be PII in there,
there's going to be bad data. I think it's really a case now where
it's almost like we're throwing down the gauntlet on data quality and on finally cleaning
up the mass because and so data catalogs are part of the storyline we've had
to show recently with data dot World. We're gonna do a show soon with
alation. Data catalogs came along and really helped sort of identify and begin the
process of defining business terms and creating this dynamic business glossary for example, and
educating the users. I think you told me that data literacy is a hot
topic on SEVENW. Data and a data catalog will help improve data literacy because
you're getting definitions of things and you're also governing the data. But I think
the real key is that you have to have some kind of system that will
scan your entire network and find every data source and every data targets and begin
to map out what that is. Now, you do that in the security
space. Like there's a company Extra Hop, one of the coolest companies I've
come across. They and this is gosh, probably ten years ago that it
took a briefing from them, but I've taken briefings since, but not terribly
recently. But what they do, it's very interesting, is they will basically
siphon off your network traffic and they use a copy, so they create a
copy of your network traffic, and they use that copy to populate a landscape
of all your systems, all the data that's flowing from place to place,
which is extremely valuable. And then of course you have to go along and
catalog these things and manage it. But to me that's really important now because
because of AI and because of the pressure in the marketplace, etc. I
think it's time that organizations really took a hard look at what they have and
make sure you've catalogued what it is. Where is the PII, what's your
policy you're going to be around dealing with that all that stuff. Really it's
a must have. Now what do you think? Yeah, the data catalogs
to what we see Since five years and some focus has been given to data
catalogs and people start to understand and information management to all the data catalog can
bring to the understanding of your data. But you have the meta data level
the data about the data, and then you have as well more the business
is loss of where you understand where the data ties into your business strategy and
your business processes. Now with a low language, models and tools like for
example, Cynoscope is one of those unique special products that looks at the data
in a different way. It looks like if you look at it. You
have these tools like you look at your data on your disc and see the
heat map of which files are have the biggest size and type of data?
Do I have more music on my disc than compared to word documents? And
Cynoscope is these kind of tools that look at the data like an emma and
eyes can from your body. But for your data and so that helps you
out identifying which is a sensitive data, which kind of data can an archive
in such a way. And I think a combination of a large language models
and a tool like a sinoscope could be a real great visualization understanding tool to
build these smart type of data cantalogs remembering the times when you were doing When
I doing more business analyst roles, it tooks ages before you understand the business,
that you understand the rules in place, that you understand where and how
to find the data and trust the data what you're looking at. And by
having a more dynamic and smart type of data catalog this will be so helpful
for businesses getting really insights out of their data. And think it's it's just
starting with the large language models that will help building that understanding. Now,
the challenge still is next to the contextual type of the thouses that it needs
to have a business specific list of all the concepts in your industry. But
I see a lot of companies popping up as well that have that built already
for the finance industry, for the tech industry, and let's go along with
a pharma and more specific type of industries as well. So yeah, it's
it's really exciting on how all of this is developing right now. Well,
and then there's this company Praxy Data. We just started doing some work with
those folks, p r a XI Data and this. So you've got Andrew
On Andrew Turner. Andrew Turners a long time expert in this field. He's
a he's a real seasoned veteran. And Then Andrew On has some great experience
from over at Horton Works. He ran a patchy atlas which was this metadata
management technology basically, and they've figured out some interesting stuff, one of which
is that the expert curation is key to their success. But they also focused
on specific industries and specific business models as a way to really expedite the process.
So I think that makes a lot of sense because you know, the
lms, from what I can tell, they're filled with tremendous amounts of information,
but it is pretty generic. I mean, you can get into the
weeds on some stuff pretty quickly. But I think there's going to be there's
going to need to be some way to purely feed your corporate data into an
LM and sort of like a single tenant kind of situation. And I'm not
sure if that's coming, as someone told me that it is coming, that
they're planning that open Ai, for example, is planning to offer the sort
of single tenant engine because now everyone's just using the same engine, so all
that content goes in there and you lose control of things. And if you
add code to that, then now this model has code if as any proprietary
have to be careful about that. But I think that if you look at
how powerful they are and what they're able to create on demand very quickly,
it's like, wow, I had I had to chat GPT write a business
plan for me, a three page business plan, and I was really impressed
with how good of a job it did because it picked up on stuff that
I had not mentioned, but we had been talking about in the conversation moments
earlier. So I'm like, I don't think it hurt me by any means.
But what's interesting is that it picked up on a certain aspect of the
industry because it's about a music music concept that came up with this thing I
called a place to Play, and the ideas that you would have these community
owned music venues where all the booking and everything is done online, because the
hard party. You're a music fan yourself, right, you know. For
musicians, the hard part is getting onto a stage somewhere, in part because
the people who run the venues can be difficult to deal with, and of
course there are other factors. Will look at this. This is the prompt
I gave it. I said, to write a three page business plan for
an array of crowdfunded, community owned music venues where all the booking is done
online and all the back office technology is bundled for franchises. The organization is
called a place to Play, and the ideas to enable communities to own their
local music venues, which will also double as music studios and recreational facilities.
Well, listen to what it did here under the need. So it wrote
executive summary and that was good the the need. This is wow, he
writes, this is chet GPT. The live music industry has been severely impacted
by the COVID nineteen pandemic, with many music venues struggling to survive due to
prolonged closures and restrictions. And here's the interesting part, it writes. Additionally,
traditional music venues are often only and operated by large corporations or private investors
limiting community engagement and control over these spaces. I was like, oh my
god, that's the message I wanted to say. I didn't put it in
the prompt. It just figured that out right. You can get the excuse
that the chet sput manship debt, but that's exactly what I was thinking.
If you see big concerts they're in Beligum for example, the big festivals,
that's the same thing happening over here. And you get some smaller groups that
are within the same booking agency. So a lot of the other artists are
really wead out of this kind of monopoly in acertive way. Yeah. Yeah,
so I mean you look at that. I mean in the data industry
and the analytics business, there is a lot of rotwork that has to be
done. I think chat GPT is very good or generating content for tweets.
For example, you've used it for tweets right where you say give me ten
tweets to talk about the power of artificial intelligence for data and that that that
that it comes up with stuff that you wouldn't expect, and I think that's
the pool part, right, Yeah, and the advantages that you can it's
quality test mechanism in a certain way. You can throw out a few things
and see who responds to what, and that's helps in doing research as well,
so other much you would have needed other ways of testing it out,
So you can just be ovocous and try something and throw it out and see
how the responses are online against a certain statements and see how that works.
And that helps you identifying as well, what are people looking for, what
do they want to discuss, what are the problems are there? And that's
just an amazing productivity tool in using into in such a way. But I
was thinking where you're saying everybody puts their data into chet gput into the single
tenant instead of the multi tenant that we've been talking to able as well,
where they are training the models on top of the company's data in that reserve
tenant, in that private tenant. And that's definitely I think aware where we
are going having the algorithms tree on a generic data set and then have it
applied onto your onto your company data in your private environment. Yeah. Well,
and they can also scan through large amounts of data, So I think
the you know, one of the more exciting things is that you could use
this kind of technology in conjunction with a data catalog. For example, you
find me every case where I've got PI a lot in my information landscape now
and it goes and does it and gives you a list here's all the stuff
that we found. I mean, it's really kind of crazy what's possible.
But the key, I think is to couple it with other technologies and other
processes and just be careful about what you're doing. Right. Yeah, it's
like you say, careful of what you're doing. You need to understand if
you feed certain data in the large language models, where is it going?
And then we come again to a certain to a topic observability and transparency.
And that was already we've been discussing that already five years ago, about the
transparency of AI models, of just statistical models. Understanding of we put something
in it, something comes out. But what is the rule and how did
you come to decide this this this particular thing, and how do you integret
it? And if you have that understanding, then you can trust and the
data and the algorithm. And that's why we we we are going I think
with these with these large language models. The complexity is that our trainoma well
newest models on a few billion penalmeters, so that's going to be a difficult
box of building that transferacy for the business. Yeah, I mean basically it's
not there at all, Like so you can't be careful about using chat GPT
for any kind of regulated industry because it's just off the rails. I mean,
they're you know, they're they're not able to explain it as the bottom
line. I mean I asked chat GPT which database it uses, and it
wouldn't tell me. And that's not explainability. I mean explainability is you need
to tell how it happened. So I'm not sure why they've they've baked in
this opacity, but that's clearly what it is. I mean, they have
a set of rules of things that's not allowed to talk about, like how
it works basically, which I mean that is explainability. If you can't tell
us how the darned thing works, it is not explainable, right, Yeah,
And I think we've been trying to do that in the entire systems and
and and systems before AI to get to term. That's the explainability in there.
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Eric's having off but nowly so. It's talking to Eve Bunkers of seven
W Data talking about large language models, the lms like chat GPT. Of
course, Data Bricks rolled out their own version Dolly two point zero. I
saw the other day. That happened pretty quickly. Those folks have some good
developers and some good discipline for what they're doing. And of course those shows
are coming up soon, the Big Snowflakes Summit and the Data Breaks show,
which are at the same time in different places, different cities. So you
know, a lot of the analysts were complaining about that, but I read
a post from Aligadzi who said, hey, we didn't try to do this
intentionally, just the way it happened. So there you go, and that
is really where everything is centering around these days. Are these engines, these
analytical engines. Right, You've got data bricks, you have Snowflake, You've
got Tera data still out there, there's Vertica. You know, there are
lots of these platforms. There's dremeio for examples, another way of doing it.
But they're all trying to accomplish the same thing, which is to allow
you to get value from your data. And you know, this lakehouse concept
is evolving. Now. We started off with databasis, that we had data
warehouses, that we had data lakes. Now we have this federated sort of
data lake at this lakehouse architecture. It gets pretty confusing and I think,
you know, pretty esoteric at a certain point. The bottom line is you
want to be able to get value from your data quickly. And now we
have these low code data pipelines too. There's a company, Prophecy dot Io
that we're doing some work with. So you're seeing all of these sort of
steps to go along the way. But I mean, you've built pipelines before
and you know that it takes time and stuff breaks. So like any pipeline
you built, you're gonna have to maintain, right, and like you have
to and this gets it's almost like the new batch window. What do you
think, Yeah, it's it's still the same problem and the problems only bigger
in a certain way. Before it was structured data. Now you have more
unstructured data. So understanding of what you can what you can expect in your
data pipeline, that's an important part, and that's really only focused on the
technical part where you the better business understanding what you have about your data.
That's why you can anticipate what you can get in and how it should look
like to massage it for the data scientists, for the business intelligence people,
people building reports and so on and so forth. So really having that data
sagliness of your data, that's an important skill. And the critical thinking alongside,
where you can question why do we have three values instead of four values
of five values? What do the values means? And really challenging the business
people that should have that knowledge in a certain way that helps in building the
better pipelines. But on the other hand, bringing their skill set to more
people so they can do more with the data in such a way. That's
a very important skill that we need to teach to a lot of more people
to make really data insights and that I mean a lot of this stuff you
could argue falls under the data literacy moniker, right, like understanding what goes
into a data pipeline, how to build it, what you want out of
the other side. They are getting easier to build. Five ten years ago,
we had data pipelines, but they were a bit more technically driven,
so you had to have engineers build these things. And the ideas that you
want to be able to allow regular business people to build their own stuff.
I'm not sure if we're there yet, but we're at least getting close to
being able to build these things. And again, what's the purpose? What
are you trying to understand? I mean, you've had some experience trying to
hack through all these pipelines in the marketing world, and I think the challenge
is that the bigger world out there on LinkedIn and Twitter and Instagram, face
Book and all these social engines, it's very complex, the data, the
rules are always changing for these guys, and like, this is what I've
kind of determined here is that you know, all of these engines, they're
designed to make money and to keep you engaged. That's the point they want
you to stay on LinkedIn and just go do stuff there to receive advertising or
whatever. But because the rules keep changing, it's a difficult thing to manage.
Right, you have to maintain these pipelines, see what's coming in,
understand if there's anything of value. You know, it's like sifting for gold,
right, I mean, you're what you want to do is blow some
dynamite on the side of the mountain and then use your look, your sifters
to find those gold nuggets. But what do you think, Yeah, that's
that's a different as what we see with the model data platforms they have.
That's that's collection catalog where the collection developers day they take care of all this
abspection where you say, if something is changing on LinkedIn or any of the
abayes, they take care of that so you can have more an extracted way
of implementing that ABI and things don't get broken when you're developing the data pipelines.
Things have become simpler, like you say, with the low code and
then low code platforms could I was just thinking it compares to how back in
the days business people were preparing what you need to build as a developer.
So they've been exploring the system, building huge enormous improve performance, complex spl
statements, and then gave it to a developer that had to transform it into
a data pipeline. So if you can stick on the same platform from a
business perspective and give them to the people where they can build low code low
code data pipelines to explore it in a fast way and iterate and understand if
the results is what they really meant to have, and then talk to the
people that the data engineers are data architects that can really design that in the
correct way with the guidelines, but on the same platform. I think that's
the way to go, but I haven't seen it that much yet because the
skill set of various people that varies so much between between an engineer, between
a business person somebody from marketing. They all have different backgrounds and different and
there are different tech savvy to build this type of solutions. So that's that's
the challenge to get a more homogenious proof that can work together on that same
platform in the future. But already, what I see very often that a
lot of people are not able to build a simple pivot table. Well I
call it simple because it's it's it became kind of general onlooking at data and
aggregating it and trying to understand what question do I want to see answered and
why you pull the things together and build the aggregates to build that understanding.
But even in marketing platforms that I'm using, they give me the clip true
rate and then I think, wow, these look really insane, and then
I do a different type of aggregation and then think what type of data am
I looking at? Because this is so much lower? How are you building
these numbers? What do they what do they tell me? And I'm not
able to understand that. I'm checking with the help desk, but they're not
able to explain that because in a certain way, like you said, Eric,
they're trying to to hide that for me in a certain way. For
me, it's the numbers I see and need to trust the numbers and if
you can tell me how they are calculated, I have a hard time understanding
the numbers and trusting the numbers. Yeah, well that I mean, this
is my point. And I've seen when I've advertised on LinkedIn, when I've
advertised on Twitter, when I've advertised on Facebook and on YouTube. I mean,
you get these crazy numbers LinkedIn is probably the most conservative about them.
But on Twitter, for example, you throw ten bucks that's something, and
you go from one hundred and thirty impressions to four thousand impressions. It's like,
okay, who are these people? Like, how did you calculate this
number? And you know what's interesting to me is thinking, I feel like
we need more transparency, Like, first of all, and even from these
email marketing engines, how are you calculating? What is the mechanism of action
for you to tell me that you know the email was opened? Well,
a lot of them were using the pixel. There's a little pixel that gets
called like okay, that pixel was called. But then you have email clients
that don't load images, right, so they will intentionally not load the images.
They're protecting you for that, And I realize it gets complicated, It
gets, you know, hard to understand what's actually happening. It's hard to
design these systems too. Right. Then you've got like Google with Gmail coming
up with their new rules of like I've seen when that happens, they'll roll
out some change and all of a sudden, the numbers kind of go sideways
for a period of time. But I think we really do need transparency,
and I don't know that we're going to get it, but you know,
you look at some of these numbers from somebody social sites, not to name
any particular names, but you got to scratch your head like are you sure?
Like what is this that i'man looking at? It's this concept of vanity
metrics, right, they're sort of playing at the vanity metrics such that the
marketing person go, look and see this great result. But did anybody buy
your stuff? Did anybody register for the webinar? I mean there's a particular
goal, and it's not just impressions, right, it's some action like clicking
on something or buying something, and man, it's hard, it's just hard
to do. It's it's the critical thinking. I think that that becomes important.
And in such a way if you say we can get you visibility and
we get you a hundred thousands impressions, and you don't think further what you
want to achieve with these hundred thousands depressions, it's fine, Eric, you
both the one hundred thousands depressions. You paid me money and you're happy because
you got to, but you forgot to told me that you want to have
a conversion of two percent, and people buying ten K products, you're making
twenty k. So that's an O brainer. But it's like I have this
this news letter platform and they say, I say, it's pretty strange.
I see a high number of click true rates and if I look at the
Google analytics, it's a kind of only ten percent of what you're telling me.
Oh yeah, but very likely there are bot clicks in there. Yes,
I know they're bot clecks, but how can I separate them? Because
I don't care about the bot clecks. There are systems in places that look
at your email and they click on there to see if it's spend email or
whatever, and that's how the systems work. But that's the transparency where you
were talking about that. It's only when you dive into a certain domains around
that data you start to understand how the systems and the technology works, and
then you can ask these questions. So definitely we need a lot of people
that can explain that and not only selling solutions as such, really helping you
what you want to achieve, and then tie in that what you want to
achieve building the solutions of finding the data and building the transpellency and talk of
that, so I think we're still on that level, a long way from
home getting there. Well, you know, it's interesting from my perspective as
a marketer, as a digital marketer, as an email marketer. You know,
the last few years that all the talk and all the rage has been
around social media getting impressions and followers and all that kind of stuff. And
you know, from what I've seen, gosh, I mean, I would
say that one thousand emails opt in targeted emails are worth a hundred times a
thousand Twitter followers, you know, if not a thousand times. I mean,
email is still a transactional system. People trust email to get an email.
They'll click on a link to look at something. It's different than social.
Social is much more shallow. It is good for getting eyeballs content.
But you know, we've been using the daylights out of Twitter and LinkedIn for
several years now and get less than one percent of registrants for webinars from social
less than one percent. And I've noticed sometimes the people who registered via social
have already registered by emails, so it's like we're not getting get new people.
We're getting people who did registered by email who just happen to catch it
on social So it's like, you know, there's just these really weird divides
in terms of perception versus reality. And it's changing too, It's all constantly
changing. So it's I mean there's a lot of effort that's required. And
when I look at what's happening now in our industry and as you know,
almost every company in our industry folks. And when I say ournistry, I
mean data analytics, I mean information architecture, data management, all the people
who are helping you capture, process, analyze data. All those companies from
the databasis to the data lake companies and all the integration people. That whole
industry just took like a ten percent cut in terms of personnel. I mean
there are companies. I know that there's a you have four hundred people that
fire twenty percent of their company, the eighty out of four hundred. I
mean, wow, that is a significant hit. And to even know what
other people were working on, right, well, what campaigns were you working
on, what were you doing? Like even figuring that out it's gonna be
hard even if you use systems, and you're supposed to use some kind of
system to do this stuff. So at least some of that knowledge is baked
into the emails that have been sent, who received them, what the lists
are, There's stuff like that. But to me, it's a real sort
of come to Jesus moment, as they say, in our industry. And
I think it's going to be interesting to see who shakes out on the other
side. But what do you think, Yeah, it's a dissipating very likely
they're anticipating the large language models, and you can ask what the twenty people
that have been fired, what have they been doing? What all? Maybe
that's that's in their heads. But it's already so so difficult to find information
within our organization because there is a lack of documentation because people are so precious.
That's the big challenge. I don't know. In States, it's apparently
easier to fire twenty percent of your labor force. Here over in Belgium it's
it's much harder. You have to be a bit more smart to do that,
and even then you have the unions that jump on there. That's a
whole different dynamic. But I do understand from time to time that entrepreneurs try
to scale their business have a lot of people which are kind of duplicate roles
and so on. And that's the way of what you say, we cut
twenty percent of the workforce and that's how we grow in a certain way.
But I don't think that's that's anymore in that that smart objective where you say
we overdid it, we hided too many people to be able to scale,
and then turn it down. It's a way of working with scaleops. But
there is something else going on, like you say, even they're looking at
being acquired or they really only want to cut costs, And that's that's the
question we still have. What is influencing the tech companies that are cutting down
on so many people. And it's strange. I mean, yesterday I looked
at for example, and an AI dout of this which holds the tools which
are AI based. They're up to four thousand tools based upon AI for marketing,
for visualization, name anything, productivity for more than four thousand tools,
and I thought, I thought I knew already about six hundred of these tools
that are more than four thousand out there. So it's insane how the technology
companies are growing, and just small companies as such. So I think what
we saw with the data management space a lot of rationalization which has happened over
the last ten fifteen years already. This will happen in the new technology companies
as well. For folks. Don't actually, that will be right back the
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That's eight five, five three two five of eighteen twenty one express. Welcome
back to Inside Analysis. Here's your host, Eric Kavanaugh Flie. You don't
make it's a experts but click buying talent is it read a big deal right
now? They're in the negotiations. I think it's basically in the regulatory phase
of the acquisition, just making sure they can clear all those hurdles. I'd
be surprised if it does not go through. So that's probably going to go
through, and that's going to change the industry. So you look at where
are the consumption points? Where are people playing around with data? While you
can play around with your data in a data warehouse for example, or a
data fabric as talent is able to deliver, that's one way to do it.
But then the consumption is really the front end, right And what excites
me about all of this fun stuff is the discovery side of the equation.
So I get very excited about discovery and click is very good in the discovery
department. So it's a company called thought Spot. In fact, thought Spot
is having their events. It's virtualed again this year because of people not being
really quite entirely ready to go back to conferences, they are happening. Like
I said, I want to click world, So that conference is on.
But thought Spot is another vendor that has a very interesting approach to enabling discovery.
And what they allow you to do is just type in a natural language
query and then it goes out that it grabs some data and it gives you
some sort of visualization to play with. Now, this automated components is everywhere
these days. A lot of companies are using this kind of tactic to get
the ball rolling, basically, to give you some view that you can then
amend and augments and play around with to get yourself a better understanding of what
is going on out there. Well, there are lots of other places that
you can go to play with data right, and of course all these engines
are running up against the big cloud vendors and what they want to bring to
the table. So you think about the data ware housing world, Well,
you've got Tera Data that's still still doing very well. You've got, of
course Snowflake that took off. Now their stock prices taken a beating in this
strange market place. But I can't imagine Snowflake is going to go anywhere.
But up you've got data Bricks, You've got Dremio. There are lots of
different ways that you can organize your data and leverage these technologies. So there's
a lot of competition. But if you look at like a Google and big
Query, Google is getting serious. I heard that Google Cloud is making money
for the first time, so that's good news for the folks at Google.
And let's face it, they've got some pretty deep pockets even though Google is
laying off people too this entire industry. Look at alter Ricks, for example.
I know alter Ricks just had a ten percent layoff. One of my
good buddies got laid off there. And what does all that mean? So
when I'm looking at this industry and seeing the number of players, well,
what are you gonna get? You're probably gonna get some consolidation. You're probably
going to get some leaner times for a lot of these vendors, because pretty
much across the board in the data and data management and analytics industries, we're
seeing layoffs. Ten percent is a pretty fair number. Twenty percent one company
I know that was doing some pretty cool work in the analytics space, very
targeted analytics player. There are layoffs. So what does that mean? While
it's going to mean a whole lot of re orchestration figuring out who's doing what,
Like, how do you even know what people were doing? Now?
If you use some kind of a portal or you use some standard tools like
HubSpot. Obviously for the marketing folks is a good one. Email marketing technologies
are all over the place these days, so a lot of the processes and
content are captured in these systems, and that way a new person can come
along and kind of look through and see what has been going out the door
and figure out what they can do better, how they can optimize things.
I can promise that chat GPT is going to have an impact. Chat GPT
is a very compelling solution for marketing language. Now you have to be careful
because it does have hallucinations, as they say. I find that rather amusing
that we talk about chat GPT having hallucinations, like it's you know, taking
drugs or something. But it does. It creates a reality, right,
because the thing to remember with large language models is they are basically predictive engines
that are trying to deliver the language that you want based upon your prompt.
So this gets to the concept of a prompt, right, and you have
to be very good at prompting something like chat GPT if you want to get
good value from it. Really the question lies in the or the value lies
in the nature of the question or of the prompt or what you tell this
thing to do. And the more specific you are, the better the results
you're going to get from using these technologies. But it's a lot different from
Google. Remember with Google, even though Google has been changing lately, and
you think about when you're querying Google's indices to find a website, Well,
one thing I noticed a couple of years ago. Now is the Google started
pointing you when you give a question to some source that it is determined is
reliable to give you a definition of something. So a definition of an accounting
term, for example, a definition of a business term. You can just
Google something and it'll go and give you a quick view of what that definition
is, and then it links, of course to the page where it found
that information. Well, that's a little bit similar to what chat GBT is
doing. But with chat GBT again, this this large language model that's trying
to interpret your question and then deliver text that it thinks you're gonna want to
see. And for tweets, it's great for LinkedIn sort of short posts.
It's also very good. It's good to get a start with something. It's
good to kind of get a skeleton or a rough draft, which then you're
going to want to go through and fine tune, just for kicks. I
use chat GPT today to see about generating an abstract for a show live this
week on Data Match and Data Fabric and it did a fairly decent job.
And it's doing is it's leveraging this repository of content and of relationships and models.
Really, these large language models, there are models in them that are
trying to mimic what different kinds of content look like, and it comes up
with some pretty compelling stuff. I mean, it's really remarkable what it can
learn. But remember you're doing this sort of as a ProForma or a rough
draft of something which you can then fine tune with details as an expert would
need to in order to deliver highly targeted content. So it's not just gonna
do everything for you. In fact, over the weekend I had a bit
of an epiphany and just thought to myself, no job does itself. There's
no job anywhere that's going to do itself. But we have our tools to
help us do the job. That for sure we can get from these different
technologies and chat GBT just like some of these other large language models Dolly two
point er from data Bricks, they are designed again to to predict what you
want to hear, what they think you want to hear, and they're very
good at that kind of thing. So people are going to be using chat
GPT I guarantee to develop skeletal blogs, skeletal articles, even business plans.
I was musing earlier on the show how I used it to write a business
plan and it was pretty darn good. It comes up with interesting angles on
things, and what's kind of cool there is you can almost get a little
taste of the zeitgeist, right, so you can play around with stuff and
just see what is the LM's view of the world on this particular topic.
Again, watch out for hallucinations, but it's pretty useful stuff. And there
are lots of other things we have to watch out for these days. I
mean, in a somewhat related manner. We have these bots on engines like
Twitter. I'm starting to get some more of them on LinkedIn. I think
LinkedIn has some tighter protocols around logging in and creating a profile and so forth.
But there are fake profiles on LinkedIn. There are lots of fake bots
on Twitter. And if you don't believe me, you have a Twitter account,
just and tweet about anything controversial, anything political. Just say this person
is great or that person is terrible. Choose your politician to fill in the
blank and see what happens. Or challenge someone and see what happens. You'll
give this whole array of Twitter bots liking your stuff, criticizing your stuff,
tend to line up on two sides of an issue and kind of go at
each other. And they've gotten fairly clever. In the recent past. That
used to just be insults. So someone would insult you, Oh, you're
an idiot. Dotis on that and saydaha, they're sweeping up the floor with
this idiot today. That kind of thing. And these are bots. I
promise you, if you get one of these coming at you asking you questions,
ask them for their LinkedIn profile. That's my trick Like that, I
say, oh, let's let's connect on LinkedIn. What's your LinkedIn profile?
And need to get nothing because there's no LinkedIn profile. Now someday they may
get sophisticated enough to do that, and this is a challenge with a generative.
They are quite frankly, and I think in our podcast Bonus Set and
I'll talk about some of the interesting conversations that we had at click World,
which were absolutely fascinating. I mean, the folks over there at Click and
some of their clients like Harmon, Harmon, Cardon, like the radios.
I learned a whole ton in a short period of time by talking to those
folks. They have a whole consulting firm. I didn't know that, but
Harmon Cardin is its own consultancy that goes out and consults with clients and helps
them build solution. Podcast bonus segment, Folks, send me an email,
I do want to know what you want to have on these shows. Info
at Inside Analysis comes right to me. We'll be right back. You're listening
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I'm Chris Garagio. President Biden want states to help strengthen background checks on
gun buyers younger than twenty one to slow the pace of mass shootings. Biden
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