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Analysis dot com. And now here's your host, Eric Kavanaugh. So,
yeah, soks, welcome to the future. Indeed, as my hero William
Gibson says, the future is here already, It's just not evenly distributed,
at least not yet. And that's the purpose of our show is to distribute
the future, at least tell you about what's coming down the pike. And
I gotta say, folks, I have never seen the future come faster than
it has been coming in the last few months. So here on our show,
of course, we talk all about the information economy, new jobs,
new roles, new technologies, new business models, the ways of doing things,
and wow, we kind of hit the inflection point, I guess,
not five six months ago. And really you can peg it to chat GPT
as a large language model. It's not new. Large language model has been
around for a while, for a couple of years at least, but this
particular version came out mature enough and big enough and wild enough to really shock
the business world. I can tell you that people are kind of freaked out.
Even the folks at Google are nervous. And of course they have their
own large language model called Bard, which is different than chat GPT in a
number of different ways. But the bottom line is that this form of artificial
intelligence AI is absolutely shaking the foundation of how we do business. It's not
perfect, and it really is important to understand the limitations of these things and
understand the use cases when you use them, when not to use them.
And there is all this talk of so called hallucinations. Well what is a
hallucination? Well, for those who don't know yet, Chat, GPT,
BAR, these large language models, they are basically predictive engines for words,
and they will deliver words based upon the prompts you give them. They've been
trained on millions and even billions of points and tons and tons mountains of data,
and so they can reflect back in pros all kinds of different concepts.
You can use chat, GPT or BAR to write a business plan, or
to write marketing emails, or to write articles or blogs or short stories or
all kinds of stuff. But it is generative in nature, which means it
creates new stuff. And basically the way it works is it fuses together vectors
of information based upon its model, its training, and also your prompts what
you give it. Well. Very quickly in this new world, we started
seeing people talk about embeddings and trying to figure out ways to get these engines
to be more accurate in specific domains of content, so in banking, or
in healthcare, or in retail or marketing or whatever. Embeddings refer to actual,
real corporate data that you either embed into the model itself, which is
kind of complicated, or most people are talking about embedding them in a vector
database, which is then adjacent to the large language model. And what happens
is when you give your prompts, the engine will reach into both areas,
will reach into your vector database to see your trusted data, and then it'll
reach over into its corpus of language and understanding to kind of fuse together some
content that is reflective then of your corporate data. This is called training the
models, and you do have to train these things for them to be very
good at what you want them to be good at. But they are very
good right out of the box and all kinds of stuff. But what's fun
from my perspective is that when we start talking about pointing these algorithms at your
corporate data will, you have to be careful about that because a lot of
companies really don't know everything that they have in terms of data, and historically
it's been very difficult to figure that out because what would you do. Run
some query and you know, just run a report. Okay, here's our
data. It's a very difficult thing to break down, to parse and to
demonstrate, and frankly, it's just hard to crawl across all those objects,
all those documents in your share point instance, or wherever it else you have
information stored. So it's important to remember that for basically every organization of any
significant size or age, you're going to have a ton of data in the
form of spreadsheets, in the form of PDFs and word documents and all kinds
of different things. So you're gonna have all this information that can be very
useful, but there may be sensitive innovation, then there may be wrong information
too. So this training is really important stuff. And we're going to talk
to a company today that can help you figure out if you're at the starting
line now this is not all that they do. But when I was talking
to them about their technology and their services, I thought to myself, Hey,
you guys have a really powerful story to tell in this whole large language
model narrative is unfolding right now. And so to that and end, we've
got Lewis wyn Jones on the line from Think Data Works, and of course
we have our buddy Eve Mulkers into Wings. He'll be joining us in a
second. But Lewis, tell us a bit about yourself and Think Data Works
and how you are able to help companies determine if they're ready for large language
models and then kind of go down that road. Thanks so much, Eric,
and and thanks for the sort of primmer. I think that you hit
the nail on the head. There's so much excitement right now, and a
lot of that has been because of what GPT has done in the past few
months. My company is, as you sort of suggested, is more in
the sort of data management space, and that's definitely a less sexy place than
what you're sort of seeing when you when you plug a prompt into chat GBT
and see what comes out. But the reality is, if you scratchually hard
at the surface, you start to realize that you do need to have well
structured data in order to get well structured responses coming out the other end.
I think that what we see with chat GBT is the generalization of large language
model and generative AI tech, which is why it's so exciting, right The
fact that there's something out there that I can go, when I can plug
a question into and get a pretty good response back, that's really exciting.
But the reason it's exciting for me is because I haven't been working in AI
for the past, you know, ten years, trying to build these models,
and the fact that my grandmother can go and do this is really sort
of the exciting part of this. I think that if you want to start
to have that tech embedded into the internal systems of most companies, there's a
lot of work that needs to be done to get you to that place.
And the things that get you to that place our management of your data assets.
I mean, you kind of mentioned it. Most people have data all
over the place. They don't They've got a better inventory of their office furniture
than they do of their data sets. And there's a lot of things that
are required to make good on the promise of AI and actually start to generate
value at the back end of your business. And so that's really where where
our company sits on the VP of product here, and we've been building a
data platform for the past roughly ten years. We were born out of the
open data space actually, which you know, for those people who are listening
who don't remember what that was all about, but it was governments that were
sort of giving data into the public domain and saying this could be valuable.
The problem with it was that it was all over the place, it was
a mess, there was no standardization, and you know is file formats from
the nineteen seventies. So what we did is we would sort of sit on
top of all this data, standardize and make it accessible to people. And
that's when we really realized that the thing that was happening with open data globally
was also happening within the enterprise itself. So the data is all over the
place, it's a mess, it's you know, you don't know how to
get the value out of it. And so the tool that we built to
sort of standardize all this government data, we sort of turned towards business data
and all of the tool and functional functionality that go along with that, which
really does support it's sort of that data refinery in order to get the AI
analysis coming out the other end of it. So it's I would say that
it's a prerequisite to start to get the benefit of large language models or any
other kind of a modeling that you require. Yeah, and I think your
background and kind of where you all came from is perfectly suited for this challenge
because you were out there trying to reconcile, classifying, organize this vast amount
of data, unstructured data, all kind of different formats, very unwieldy.
So that was your you know, kind of how you cut your teeth on
that environment. And now you're able to do the same thing and help organizations
again understand what they have, which for those who haven't tried, I promise,
is a very difficult and challenging task to undertake, but it's very important
for lots of reasons. I mean, I kind of think that this is
the sort of come to Jesus moment, if you will, for enterprises and
all the data they've been sweeping out of the rug for the last thirty years.
It's like we've known that we had to do something about it. But
now you really really do have to do something about it, and you guys
can help with that process, right, Yeah, absolutely. I mean I
think that it really depends when you're talking to because in certain boardrooms people are
saying, well, we want streamlined analytics, we want to make data driven
decisions, we want all this up, and they don't understand that in order
to get there, you need to have your house in order. And it's
a step that you can't actually skip that sort of data operation step, the
metadata management step, the good indexing, the good tagging, and the secure
distribution throughout the enterprise. It's not a nice to have, it is a
prerequisite. And what we're seeing across the enterprise is that people who have skipped
that step and maybe set up very good data and analytics divisions, who are
trying to unleash the power of AI on the enterprise, what they're doing is
they're generally sort of building one or two products a year. This is not
fully automated, and this is not actually a system that is going to scale.
Well, it's a system that under the voot if you scratch it,
like, if you get underneath it, it's a lot of ad hoc work,
and what everyone wants to do to really maximize the benefit of this stuff
is to automate those processes. And the way to automate them is to have
your data environment set up in a way that will facilitate sort of the pulling
in of data, the pushing back, the sharing, the sort of collaboration
and the management of that data. Otherwise, what you're going to be doing
is basically manually curating data sets and pushing them into Tableau. And that is
not AI as far as I'm considering. But that's here laughing, I'll bring
you into comment on that. I mean, I think lewis to sit the
nail on the head there. You really want to be strategic about this process,
and it could be extremely valuable going through the inventory to understand where you've
come up from. What's happening, right, go aheady, Yeah, what
I see. I think back of my years of doing software development, and
this is what we see now in data management. But during it, back
in the days, you wrote a piece of codes as such, and then
some libraries came along you started using those libraries to be more proficient and more
productive in creating your applications and your software and this is now happening with all
the libraries. For example, if you take pythons that are available to access
APIs, to access the large language models. But still, like you say,
Lewis, you take these things, you take one product, you build
one product. But people don't think about the architecture to be capable of really
scaling the products. And that's something where I see that is sometimes a misconnect
between more technical people, especially in the IT department, compared to business people.
Why they see the magic shining object and they think, well, it's
like you say, it's upload band. We've got our dashboard. Yeah,
next time something changes, how faster we're going to Are you going to change
the dashboard? How much effort will it take? You? Do you understand
where your data comes from? And that is what when we talk about architecture,
that you need to put these really solid foundations in place to be able
to manage it. And that's very oftentimes for god and so I'm quite happy
that I've seen it through my career as well. These are the things we've
been building over the time, and now all of a sudden it gets attention,
it gets a name. Like for example, if you talk about observability,
Well, we need to know where the data comes from. Yes,
we've been interrogating the method that are over time into our systems to understand how
it looks like to Atto made it to deploy it in a continue look at
software development as well, where now we have continuous integration and continuous development where
this was not possible twenty thirty years ago, and I see that happening now
with especially with the large language models and all the AI tools that we are
definitely looking at that. So once we've get this really into a continuous development
mode where off the hook, I think it's going to be even more amazing
and the speed is going to increase even more than we over the lost few
months with the large language models. Yeah, and you know, I'll bring
Lewis back into comment on something in particular. You mentioned architecture, and Lewis
that's really important, right. What Eaves is talking about here is building an
information foundation that will allow you to then build apps more quickly, as opposed
to just building one off bespoke apps which may solve a business problem for now,
but will not scale very well and won't be optimum in terms of allowing
you to do other apps that use the same data. For example, we're
talking about data products here, right, and so if you build the foundation,
well then the individual products can be spun up pretty quickly and efficiently,
whereas if you don't, if it's all the spoke and spaghetti wire is going
in between them. Again, it may solve a particular problem, but it's
not going to last for too long and it will amount to technical debt before
long. Right, yeah, And I mean the thing is is this isn't
theory. This is we've seen this happen before. You know, ten years
ago, fifteen years ago, people said, oh, if you dump all
your data into a data lake, magic stuff is going to come out of
it. Well what happened there. We got a bunch of really really messy
data environments and people still struggling with this problem. And then said people said,
okay, well the cloud will be the solution to all this problems.
Well, okay, people started to centralize to the cloud, and now they're
going back to having a more sort of on prem and hybrid environment. People
are looking for the magic bullet, the thing that's going to sit on top
of an unstructured environment and pulled insights. That's not what's going to happen,
and AI can never get there because it needs to be trained in a well
architected environment, in a good sort of well balanced place where data flows evenly.
And that I'm not saying that the previous technologies that have come out from
clouds the data lakes have been mistakes. They have and they've been extremely useful
and they've taken us sort of forward. But I think now what we're looking
at is you don't want to have to centralize everything into one place for a
bunch of reasons, but you also can't have everything existing in random regions everywhere.
So really, what we're seeing, and this is just from an architecture
perspective, is a new sort of reliance and focus on data virtualization, so
you can actually connect to data wherever it is and you are centralizing access without
actually centralizing assets themselves. And that's the sort of thing that you can do
to really start to create this central observation platform for the data, and that's
going to be the key to unlock management and distribution within the enterprise. And
then of course, yes, all the downstream benefits of connecting it to your
analytical tools and if you need to your models that you're going to be building
out the back end, but without it, you're just sort of trying to
build on pretty murky, swampy ground. Yeah no, that's a really really
good point where you're democratizing access through a governed zone, if you will,
Because we've talked about this for years now on this show and other shows too,
that something like data governance cannot happen if you have eighteen to two hundred
entry points where you're only managing five to seven of them, well then you
don't have governance. Now. Historically, getting to that point was extremely difficult
because the technology it really wasn't up to snuff. If you try to go
through that one choke point, things just wouldn't work fast enough. But I
think we have seen the capability to expand out the scale out dynamically and be
able to handle those things real quick. I think Lewis, we are at
the beginning of another era because we now can pull that off efficiently and effectively,
Whereas you know, five ten years ago, it wasn't really that possible.
What do you think, Yeah, No, I think you're right.
Governance matured faster than the technology, and so the principles of governance were saying,
Okay, you need to do this, this and this, and so
people were writing extremely robust data privacy programs, but they couldn't actually implement them
with the tech that existed. Now that tech does exist, and I think
that the kind of egalitarian approach to distribution within the enterprise and good governance are
possible at the same time, So you can get increased access and increase governance
at the same time, which before it was I think one or the other.
Yeah, that's a really good point too, because you did have to
make a choice before. Now you don't have to make that choice. There
are other choices you'll need to make, of course, but you can pull
this off. And if governance is important to you, and it's important to
most organizations, not just for security but also for quality, for quality product
out the other side, you know you want to have some governance, so
you know what's going into this, you know how to manage it. And
we're getting there, folks, we're getting there. Well, don't touch that,
do I'll be right back. We're talking through a couple of experts on
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Welcome back to Inside Analysis. Here's your host Eric Tavanaugh show. All right,
welcome back once again to Inside Analysis. Take us from the future.
Indeed, your host Eric Kavanaugh here with Eve Mulkers and Lewis Wynn Jones.
Who are You're talking about the power of AI? Are you ready for it
in your organization? I've been joking that my router may not be ready for
the power of AI because it keeps crapping out on me. That's not very
good. I have to call Armstrong Cable after this show and to read them
the right act. In the meantime, let's get back to our guests,
and we were just talking about how the data management side really is so crucial,
and I'll throw this over to you first. Lewis, you know data
management has always been important data quality, understanding your process as your workflows,
all that stuff, what you have where it came from lineage, etc.
And it's even more important now in the age of AI because these engines are
very powerful and they will take whatever you give them and create something from that.
And if what you're giving them is bad data, you're going to get
very bad results. Right, go ahead, Yeah, absolutely, And I
mean I think that talking about the results. The thing that's kind of happening
right now is that the people who are in charge of data management historically have
been kind of the IT side of the organization, whereas the people who are
in charge of the results, you know, the data products and those things
were more business. And what's happening over the past ten years, fifteen years,
it's those two sides of the business have grown further and further apart.
What we need to do, both like just sort of culturally but also through
infrastructure, is to bring those two sides of the business back together again.
Right, You need to have data management intimately tied to data productization and building
AI and things like that, because alternatively, what you have if you don't
do that is the business side of the organization. Talking about AI and all
of its potential to transform business and the IT side of the organization stuck in
sort of data governance land, and you need to start bringing those two things
together to sort of create a cohesive umbrella of data and analytics together, which
are governed by single tools that are applicable to business users and IT users in
equal measure. And that's I think that that's something that patchypt has actually done
really really well. Is made this something that can be used by anyone.
Yeah, and you know, let me just kind of chime in here and
say that, you know, there are lots of different values that you can
get from data virtualization, one of which is just knowing which data is most
important. I'll throw it over to Lewis first, but if you have a
good data virtualization solution, you're going to be seeing in very clear fashion what
data is being used, what data is important. That can help you that
architect the rest of your data foundation. Right, Oh, absolutely, I
mean a data virtualization is one of those things that I think is close to
that silver bullet that people have been looking for, because what it allows you
to do is instead of getting tied up into this etl land where you're constantly
having to move things from here and there you're just projecting it from where it
lives into a centralized place and creating that sort of possibility for someone who maybe
doesn't have access to your sequel server to go see the data deps in it
without it moving, without it breaking in of your governance rules. And that's
why virtualization is a tool within a sort of larger data management framework that's really
really powerful to just provide that democratic access piece. Now, of course you
want some layers of control on top of that about how you actually distribute the
data, but just having the ability to project or create a lens through to
the data from where it resides into another location that is more user accessible is
incredibly powerful technology. And then the next piece of that, of course,
is once you've got access to that that piece of data, that virtualized data,
being able to then connect it to your damn stream analytics from that same
centralized place. Right, Because if you can't finish off that storyline, then
you've just sort of checked the book out from the library, but you can't
open it, right, it's not useful for you. So you need to
have that end to end integrated set up. And Eve earlier was talking about
the architecture needs to be right there. And the problem is if you try
to do this with seven different discrete pieces of functionality, they all need to
play nice with each other, right, And what our approach has always been
is that you need to have to be able to do all of these things
from a single system that can virtualize the data, make it discoverable, and
then push it down where it needs to go. If you try to break
that apart too far, it's going to get You're gonna get in the weeds
again. Yeah. Well, and there is this question of scale. I
think eve that's what's really shaking a lot of businesses right now, is that
the scale of data that's available that needs to be processed to get some signal
for AI is so much larger than it was five years ago, ten years
ago, and all systems are just going to break. They're just not going
to be able to suffice. And so we're in this sort of transformative period
of time now where organizations have to be very strategic and figure out what is
my stepping stone to the next generation of technologies. And I think that's where
data virtualization is extremely compelling, because again, you use it as this pathway
of bridge to get to the next way of doing things that still is connected
to your existing environment, because you know, rip replace is always very very
difficult, so difficult that it almost never happens. We've joked before about sun
setting applications and other fantasies, right or other fairy tales. So it's difficult
to do. So that data virtualization is a really powerful mechanism to solve these
problems and open the door to the next generation of how to do things.
What do you think even yeah, we've been discussing about that, and what
I see and in a virtualization layer, and which is not so understandable for
the most of the people, is it's it's simplified or it's abstraction of the
integration behind the scenes as such. If you're back in the days they've been
building building point point systems, so you had to integrate each system with each
and every other system within the organization. That takes a lot of time.
If something changes on one end, you need to change all the interacting systems.
With virtualization, this is kind of glue what you put on top of
that. If you think about object oriented programming, you had your clauses and
and what's kind of abstraction layer and what you had through which you were talking
to the other systems compared to an API. In such a way, this
is where integration solves well, where virtualization solves a part of the integration problem
as such, but at the other end as well, allows you to s
your systems because it's composable. You can change your database behind the scenes or
depending on which type of query you want to run, is it more analytical
or more operational. Wise, you can talk to do different systems, but
it looks like one and the same system if you're a user using the virtualization
layer, and that helps into going much faster in building applications and systems on
top of a virtualization layer. So for the future as well, you're kind
of safe because you develop everything on the simplification layer, and if you need
a more performance system, you simply let's call it still simply plug it in
the other system that is more perform more scalable as such. If we now
see at the cloud, if you need the cloud, you can switch it
off to the cloud and make it more scalable and move back to one prem
So that will simplify your IT landscape in such a way. But my feeling
is that we're still pretty far off on the virtualization level. A lot of
organization are still very reluctant towards virtualization because they have back experiences in the day
where it was not performing it was pretty expensive. But if you see now
the virtualization landscape together with some semantic layers, this is the time to really
look into that and consider that if you're building modern data applications. You know,
and a lot of terms get thrown around like data lake house and data
lake and data warehouse, and of course a very popular term has been data
fabric over the last number of years. And when I think about the functionality
that the data fabric should have, well, what does it boilt down to
bost down to access governance? It boils down to automation or automation makes sense,
and I think that's the ultimate data fabric that has the automation built into
it to know when things are happening and to go and grab data sort before
it's needed or before it's going to be needed, that kind of thing.
It seems to me that virtualization, if it gets robuffed enough, can fulfill
that role of a data fabric. But I don't know, what do you
think lewis am my splitting hairs or conflating things. No, I mean I
think that that's I think that that's a fair point. I think we're still
going to see where we land with the whole data fabric, data lakehouse.
The new nomenclature that I put my money right now on data fabric being being
the one that sort of emerges triumphant because I think it's very realistic in terms
of what is possible. Access, discovery, governance, observability are all made
possible by having data virtualization and a data catalog blended together. Be throwing some
data ops and some health monitoring there, and you've got a pretty slick d
you know platform. And then the final key I think is that actually,
at the end of the day, being able to access that underlying data becomes
really really critical. And this is you've mentioned, you know, some people
have been bitten before with sort of virtualization tools and things like that. I
think that that was based on a sort of system where just cataloging the data
was enough, but at the end of the day, you still need to
be able to provide access to that data, need to push it down to
where it needs to go in order to provide benefit to the organization. And
these are the types of things that we've learned over the past decade that now
we're getting right. And yeah, I think that fabric is really the solution
to that. I think the next step is sort of also getting a solution
that enables you to distribute your data and more holistically throughout the enterprise and in
a secure way to really enable that democratization among business and technical units. Yeah,
that's a really good point. Even maybe I'll throw it over to you
for commentary. Again, these are fairly loose terms, but data fabric,
I think does boil down to the access layer that you want your apps to
connect to. Because the end of the day, that's what we're talking about.
We're talking about applications. They're going to connect to the data that they
need to get something done. So instead of the old fashioned process of just
persisting data in mongo eb or mic sequel or whatever, that's a very linear,
sort of limited approach, and instead you want this richer environment that any
number of apps can tap into and quickly get what they want and get more
than they would have gotten from just a single connection to a single database,
righting, Yeah, the most important part is here that the centralized or the
central available governance what you can tie to your data products in such a way
and that you either consume it to an applicational operational application an API, or
you just do an export. You always have that same type of approach on
who can access which data. Before it was only within the organization, and
we see more and more organizations exchanging it with external organizations. So there is
the important part that you have an easy way of onboarding your external bodies and
decide who can see what type of data in an easy way that you can
govern them that data, where is it going to, who is using it
for what? So you stay comply and know where your data is going there,
especially in marketing, you want to know who are your consumers and who
is using your data. Especially in Europe, we're pretty strict on on understanding
where that data is being used for privacy reasons and I think in that direction,
data fabric is definitely a very nice architecture to support governance in a pretty
easy way. Easy in such a way, it's not out of the blue,
it's not snapping with your fingers, but the tools are there and learned
a lot from the past where you said in the beginning it was purely cataloging
your data, understanding where it is and who maybe has access to that.
But access was mostly a very ideal related thing in place, very technical,
pretty complex, so getting your user account to have access to systems that took
months in some some organizations. So this is definitely not something if you want
to put a product into the walked in the next coming month. Now,
these are all really good points, and you know, before the break comes
up here, I do want to dive a bit deeper into the discovery side
of the equation because we know we're talking again about how to get ready for
leveraging a large language model, and that means you got to do some discovery
and understand what's out there whereas there personally identifiable information, what is the nature
of this business information that we have, and create some sort of a topology
or a map to better understand, and then you create your short list of
things to work on. I scaring this data or do we really need that
data? But it really does start with that discovery process, right lewis what
do you think? Yeah, I think you're right. Discovery this sort of
the pillar here, and again it's not the thing that's going to turn heads
in the boardroom being able to discover things. But at the end of the
day, any sort of catalog or management solution is going to need to index
everything to do with your data, and it's going to need to automate that
that indexing process and then allow you to create sort of combinations of terms.
And those terms might be based on what's in the data, but it might
also be based on where does the data live, who's the owner of the
data and my team, you know, for some of our users, it's
like what's the data cost and when's the contract term coming up? These are
the things that I actually need to know. And that's metadata. That's not
just about the you know, the structural build of the data or its descriptive
elements. That's also sort of the administrative side of metadata, all of which
needs to be captured by the solution, indexed by the search. And then
it can't just be a blinking cursor you know Google type search that where you
you plug in, you know, personally identify a whole information and automatically everything
comes back. You want to be able to create sort of rich combinations of
things to get to what you're looking for, so that you can create these
very highly curated lists, and then once you've done that, you need to
sort of have recourse to move data around if you need to. We talked
a little bit about the expense that's that's driving a lot of this stuff,
and you're able to do hot and cold storage from this platform, move it
from a really performed warehouse to sort more open source one based on how much
it's being used, and things like that. That all starts with discovery.
All those benefits start from being able to just open the door and see what's
going on in your environment. Yeah. No, that's exactly right. And
you know, you talked about the importance of metadata, and I love the
comment you made about contracts and dating. What its is costing me and what
value are we getting? You know, when you bring together the power of
observability with the power of discovery and analysis and of course the human brain.
Let's not forget us humans. I don't care what it says. We're gonna
be around for a while now and we'll be very important in this space.
When you bring all that together, it starts to get very interesting. In
things like ROI and TCO, which historically have been almost impossible to really quantify.
I mean, you can tell the story and you can get people excited
about stuff. That's one way to keep investment in your projects. But we're
getting so much better now at being able to determine the value of this particular
solution against key metrics. For example, customer service improved by thirty percent because
the tickets came down. Stuff like that, we're getting into space where it's
very measurable and that it's very good news for this business. Will be right
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Welcome back to Inside Analysis. Here's your host Eric kabinat right. Folks back
here on Inside Analysis talking to Lewis Winnomes from Think Data Works and Eve Mulkers
from seven W Data and the Brink. There we're chatting about the power of
AI and the importance of data discovery, and I keep thinking to myself that
these foundational models, which is not just the large language models, but these
foundational models are going to fundamentally change a lot of different things. And if
you think about how systems talk to each other on the average day, just
transacting the work that needs to get done over the network, for example,
there are so many efficiencies that we can gain from collaboration, from being able
to see what other people are doing. I'm talking to a company right now,
in fact, writing a paper in my head for a company called dev
rev which has built this really interesting object model and they're really fusing development with
revenue management and isn't that what everyone wants? And through their system, you
can see across roles and see exactly who's working on what at any given time.
And because they thought through that metadata side of the equation ahead of time.
I'll fill this over to lewis what you're able to see is not just
what all your individual developers are working on, but what kinds of projects are
they are they maintenance projects, are they net new functionality projects? And then
where is the value you'll be able to track? Oh, well, this
project that we launched two months ago is already generating some value. So you
can actually start seeing ROI And to me, that's going to be the key
is seeing across organizations who's working on not just up, down, but sideways.
And once you can see up, down, left and right and really
understand how your teams are coming together, that's a pretty compelling storyline. And
that comes from discovery, but it comes from data management. It comes from
setting up your models efficiently such that people can see around and understand what's going
on. But Lewis, I'll throw it over to you. I think that's
going to be a real push forward in terms of productivity gains. Just the
observability across workloads, across teams. What do you think? Yeah, I
mean you mentioned the critical word there, which is observability, and I think
that a pound of observability is going to be a dash of observe ability is
going to be worth a pound of cure or whatever it is. And the
point here being that teams have always been building interesting things, right, You've
got data teams. You've got data scientists who are doing great stuff, but
they might be doing it in a vacuum. And I think that when I
speak to our clients and the people who use our platform, there's often some
person who might be in governance, might be in charge of ROI, who's
having to knock on a lot of doors and email a lot of people saying
hey, can you give me an update on what you're working on? What
data sets? Are you saying? What's going on here? That they can
still do their thing. I think the model shouldn't be too that you need
to completely upend the good work that your teams are doing and force them all
into a central place. The model should be that if you can have something
that can sit on top of all the work that all of your different teams
are doing, lurp up that information and then make it more generally accessible to
people without upending the work that's already being done. Then you're letting someone from
your team who's in charge of establishing things like ROI or making good connections right
saying, oh, this team's working on this data set. This team's working
on this data set. If I sort of play a bit of matchmaker between
them, maybe they can both run a bit faster. That's the real net
benefit here. It's not in improving individual teams, although they will get that
benefit as well. It's about sort of across the enterprise having that sort of
democratized view and observation platform and what you get the benefit that you get from
from that. Yeah, knowing how teams can work together. I mean,
I'll throw this over to Eve. AI can help with these things. I
think AI is going to be very compelling for just suggesting things to people,
pointing out stuff that maybe you hadn't thought of, because you'll never think of
everything. There was a great quote in a movie with William Hurt, like
thirty years ago. I'll ever forget this. One of the characters says something
like who they were criminals? And then one guy said, hey, man,
you told me there's a hundred ways to screw up any crime. If
you could think of fifty of them, you're a genius, right, So
point being, you're always going to miss something, and that's where AI comes
into help. But to me, again, the power of the human mind
just thinking through things and processing and figuring out what Lewis just referred to,
Hey, these teams are working on something pretty darn similar. If I can
just get them to connect and start sharing information sharing that at data sharing plans,
roadmaps. I mean, just knowing what people are working on is really
compelling and just and team size, right, if you have a hundred developers,
that doesn't mean you're accomplishing one hundred times as much as one developer,
or even fifty times as much as two. It really depends on how well
those teams work together, what the plan is, how that plan fits in
with what other people are doing. I mean, this is the strategy side
of development, and I think all of that is enabled by these large language
models, by these foundational models and the AI, and of course the data
management and the virtualization. But Lewis'll turn that first over to you and then
over to Eat to comment on. Go ahead. Yeah, I mean,
I think that's that's the key is if we want to make good on anything
to do with AI, you can't have a bunch of developers in closed rooms
trying to figure this stuff out on their own. It needs to be an
open door policy and there needs to be governance attached to it. Right,
We're not in the wild West anymore, and you can't just say we're going
to throw a large language model over our entire database, because there's PII in
there. There's stuff in there that you don't want it to be trained on.
So you need to have governance, which for a lot of people has
been a big hairy problem because they don't know how to do it, and
so they've just said, well, our version of governance is no one gets
access to anything. We've solved that problem. Now, that's not the way
we're going to move forward, right, More people need access to more data
and in a secure way. So that observation platform and letting it be mission
control for data teams. Right, So the person who's tasked with revenue gets
to do revenue based stuff, and the person who's tasked with doing data stuff
still gets to do their data stuff, and they get to do it in
a quicker, faster, more integrated way. Yeah, that makes a lot
of sense. Eging to comment on there. Yeah, a quicker and foster
way I think to insights, and that's an important thing that you were discussing
just before. It's an easy way of looking at the one hundred tasks or
even one thousand tasks. We see it in medical care as well, where
the scale really helps you build those insights and give the insights at a hyper
speed. And I think that's that's the power of what we're seeing and sometimes
missing these interactions and communication lines within organizations. If you don't talk to the
person, you don't get these insights. It's by accident if you're around the
water cooler that you have these type of discussions and say, hey, this
is interesting what you're working on. Let's have a look and see if we
can do something together. And with the large language models, you get these
these advices in such a way on the other hand as well, where it
gives you a pretty complete list and you say throw in another of five points
and you get the out of five points and after a while you understand,
okay, we really exhausted the model. This is all we can get from
it. But that's much harder from a human mind to get all of these
aspects listed in such a way. So I think that the scale and the
speed that's the important thing. Where we see that it can augment on how
we work. Like every was saying, thinking through the critical thinking, which
is not yet in the models. I think we will see that in the
future that the models will evolve and get more critical thinking as well, but
this is still a very strong capability of what we have as a human being.
Yeah, and you know, I'll get back to the theme of the
show here, which is using these technologies to discover what data you have,
to classify it, to organize it, to understand it, and then maybe
you're ready to leverage some of these large language models. But I am pretty
excited, Lewis, to think about what you could learn about your business by
connecting understand that you can possibly remediate so you can publish something in a vector
data base and go, oh, we shouldn't have done that. Let's pull
that out now to not have that exposed. But I have to think that
these language models are a very profound mirror that you can use to reflect back
what your business has been doing, because you will see stuff in there and
it's almost like you want, if you're a big enough organization, you want
to have a skunkworks team or you know someone who's just in there poking around
seeing what's going on, to be able to develop your plan to govern and
access and virtualize better. What do you think, Lewis, I think that's
such a great point I would if I was in charge of a large enterprise.
The way I'd be looking at LMS right now is let's use this internally
to understand a few things internally before we even think about the data products with
it. I think the problem that I have with generative AI right now is
the generative portal part. I think I'd like to use it for classification,
train it on very good models and things like that. But immediately dropping it
in and thinking that some production ready tool is going to come at the other
end of it, I think it is dangerous thinking. And I don't think
that this limits the use for the enterprise of starting to integrate it, to
bring in and do some automatic sort of classifications of things. And chat pots
are a great use for it. But let's not get ourselves like this is
still pretty nascent technology, and things like large scale statistical analysis have been around
for a long time and people haven't necessarily made good on those either. Why
Because the management problem is still sort of very very at the core of this
issue. So I think chat ept the biggest thing that it's done is it's
shown that business focus people need to be able to integrate with data in meaningful
ways. And they did a really good job of making that possible for us.
But we still got a lot of work to do in order to make
sure that everyone can benefit from an equally Yeah. Well, there's a lot
of education and guiding basically guidance provided to organizations to understand how to use it.
It's like any tool. You want to be playing around it to understand
the contours basically, to understand what it does. We do have a show
coming up on Thursday. As a matter of fact, eviill be delivering a
keynote on master of the prompt. I'll give you some advice on how to
leverage that prompt because it is very important how you phrase those prompts and which
specific words you use and how you deliver the information to the large language model.
And it's just taking time. It's going to take time to see what
comes back at you and learn how to fine tune these things. And remember,
you can regenerate. They all have this capacity to regenerate. So it's
like I didn't like that one, Let's do it again at a couple more
keywords. To do it again, it comes out, it'll get you eighty
percent of the way there. Then you have to get yourself the last twenty
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