0:00Hey, it's Tim here. Tableau just announced
0:02Tableau GPT. And in this keynote roundup I
0:06recently just
0:07launched, I explained that Tableau had to
0:09talk about GPT and AI technology, because
0:11every single
0:12company is talking about it. Let's find out
0:14more and get into it. Let's get started. So
0:17Tableau had
0:18to talk about AI, there's just no getting
0:19around it. Every single company that's
0:21invested in the
0:22technology space has to be talking about AI
0:25capabilities. AI has just mothballed
0:28through
0:28every single technology company. And
0:30although they're all still trying to figure
0:31out how it
0:32works or the different ways, the general
0:34consensus, and I'm not sure I'm totally in
0:37line with this,
0:38but the general consensus is every company
0:39thinks there's a way they can use this
0:41technology
0:42to enhance their products. And so what
0:44Tableau had to do is actually highlight the
0:47fact that
0:47they're not just jumping on this bandwagon.
0:49In fact, Tableau has been, and I've done a
0:51few
0:51videos already showing you how Tableau has
0:54already been using AI technology in all of
0:56their tools.
0:57And so this was a really sort of good way
0:59to set this up because they then announced
1:01something
1:01called Tableau GPT. Now GPT stands for
1:05generative, I can never remember this,
1:08generative something
1:09transformer. We're gonna have to literally
1:12come out of this recording, generative pre-
1:14trained
1:14transformer. Okay, cool. Now GPT stands for
1:18generative, generative pre-trained
1:21transformer.
1:22I had to literally go and Google that
1:24because I forget it every single time.
1:26But that technology was actually invented
1:28by Google some time ago, and we've actually
1:30had
1:30multiple versions of GPT. You might have
1:33heard of chat GPT version 4, which is based
1:36on GPT 4
1:37technology. Well, GPT 1, 2, and 3 have all
1:39come before it. You might have been
1:42familiar with GPT
1:433.5, which is what OpenAI introduced
1:46alongside chat GPT. So what it essentially
1:49means is that
1:50Tableau themselves have been building a
1:52large language model that not only
1:53understands the kind
1:55of terminology that we might use with its
1:57product, but also will better understand
1:59analytical context
2:01within organizations. And so this
2:03technology is going to be an underlying
2:05layer throughout their
2:06platform. If I sort of just jump ahead a
2:08little bit, you can see there's this sort
2:10of graphic,
2:10and I think this graphic explains it really
2:13well. Large language models and AI
2:16technology really do
2:17have to work across the platform because
2:19they need to sort of have a hook into lots
2:22of different
2:22metadata sources in order to really
2:24understand what people might ask. And then
2:27throughout the
2:27keynote, we saw different examples of how
2:30this works. The first example was obviously
2:32just going
2:33into the search bar and asking a simple
2:35question about a metric, and we saw a
2:37response. The second
2:38one was in Tableau Pulse, a Tableau Pulse
2:40being this new place for metrics to live, a
2:43new way of
2:43building metrics as well, not necessarily
2:45attached to data visualizations. And what
2:48we had there were
2:49prompts generated by Tableau GPT,
2:51essentially questions you could ask of the
2:54data source,
2:54but also you had a search box which could
2:57understand your queries, which could be
2:59relating
3:00directly to the data source, or you could
3:02even ask open-ended questions. For example,
3:04what else
3:04should I know about this particular product
3:07? They actually did a demo and showed us an
3:09example of
3:09that as well. The other place we saw it
3:11inside of the product was inside of Tableau
3:14Prep, and I think
3:15this is the use case that really we are all
3:17familiar with because this is exactly what
3:19we've
3:19been using ChatGPT for. They showed an
3:21example where they went into Tableau Prep
3:24and they had
3:25a field of data, and the field had emails
3:27contained within it. And I think it was
3:30JSON object,
3:30it was a complicated sort of data format,
3:33but it was all stored in one column. And so
3:36what they
3:36were able to do is ask Tableau GPT, "Hey,
3:39what regex can I use to extract the email?"
3:42or "How can
3:43I extract the email from this field?" And
3:45it came back with a regex response. I don't
3:48think the user
3:48actually asked for regex, it just came back
3:50with it. And so that's another use case. So
3:52if you can
3:53imagine that inside of the calculation
3:54window inside of Tableau Desktop, you're
3:56trying to solve
3:57a problem, just being able to simply go to
3:59Tableau and say, "Hey, I'd like to write a
4:00calculation
4:01that looks back four months." That's a
4:03classic example, right? Even though metrics
4:05now looks
4:06like it has the interface to build that for
4:08us, having this technology inside of Table
4:10au GPT is
4:11going to be great. And you could argue it's
4:13probably been trained on data sets, for
4:15example,
4:15the knowledge base articles, the
4:17documentation that goes back many, many
4:19versions. You've got
4:20Tableau forums, you've got knowledge base
4:23responses to specific known issues. All of
4:26these
4:26are really super powerful sources for this
4:28kind of technology. And if they've been
4:29training their
4:30technology correctly, they can start to
4:32pair that with telemetry because of course
4:34telemetry tells
4:35them how people are using this alongside
4:37what's going on inside of Tableau Public,
4:39which is another
4:40big source of telemetry for Tableau. Pair
4:42ing all of that together gives you something
4:44really rich.
4:45The difficult thing though is making sure
4:47that when it gives you an answer, it's not
4:49only useful,
4:50but it has to be right. And this is sort of
4:52the tricky point here. It's all good and
4:55fine to get
4:55excited about this technology. But, and you
4:58all know this using Chat GPT, when it gets
5:01it wrong,
5:01it has the potential to get it wrong really
5:03, really badly. And in an analytical context
5:06,
5:06in the business context, that can sort of
5:08have issues. And so where this technology
5:10is great is,
5:11you know, what questions should I ask? You
5:13know, what should I look at? That kind of
5:15stuff,
5:15the kind of stuff to prompt you in a
5:17direction, move you in a direction, give
5:19you some starting
5:19points, give you some visual aids, that
5:21kind of stuff is totally fine. But when you
5:24start to go
5:24down the road where you're writing
5:26calculations with it, that's great. But you
5:28also want a little
5:29bit of documentation on that calculation so
5:31that you understand exactly what's going on
5:32in that
5:33regex. And as you get closer and closer to
5:35sort of mission critical parts of your
5:38analytical stack,
5:39you want to know that it's going to
5:41actually help you get the right queries,
5:43the right responses,
5:44and even things like giving you the
5:45performance calculations. We've now got
5:47capabilities in Tableau
5:48that can tell you what's performing and
5:50what's not. We want this technology to give
5:52you the
5:53sort of criteria to say, hey, write a
5:55performance calculation. Or in that example
5:57with regex,
5:58you get a response saying regex, but it
6:00then tells you these are the performance
6:02implications. Have
6:04you thought about pushing this capability
6:06further back into your data stack, so you
6:08can do some a
6:08little bit of data modeling engineering,
6:10whatever you want to call it, and get this
6:12as a field
6:13ready prepared inside of Tableau rather
6:15than doing this here. That's the kind of
6:17sort of intelligence
6:18you need, especially when you're running
6:19big analytical workflows. So I'm super
6:21excited about
6:22this technology. I can't wait to see more
6:24of what it can do. But it's still early
6:25days, we only got
6:27a real small glimpse of how it's working. I
6:29can't wait for the first release of this
6:32inside of what
6:32will probably be a Tableau Cloud, but
6:34nonetheless, I can't wait. Let's find out
6:36more. And until then,
6:37I'll catch you in the next video.
6:38Okay.
6:39Bye.
6:48[ Silence ]
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