How Leading TMCs are leveraging automation and AI to drive real-world action for clients

To kick off.
So yeah, hi everyone.
Thanks for taking your time out of your day to to join us.
The topic of today's webinar is going to be looking at how leading TMCS are leveraging automation and AI to drive real world action for their clients.
Today's session, we'll, we'll really focus on what we've learnt as a team.
So myself, Claudia and Marius, who's joining me on the call today and, and we'll be running the, the demo section of, of the webinar webinar.
We'll be talking about what we've learnt over the last few years of, of working with TMCS and, and in generally across some of our travel customers, the specific challenges that they're facing.
And then we'll be talking through about how we went about supporting those, what would be really nice today as well as we'll talk through some real world examples from some of the customers across their space.
So yeah, lots to go through today in terms of, you know, why why we're here speaking to you today.
You're probably wondering what what Domo is.
So I'll give you a nice little overview of that and then we can jump into to what that means for the travel world specifically.
So if we have a look here at this slide, this is just an overview of all the different components of the Domo platform.
But in in short, it's an end to end reporting solution that allows TMCS and any, any other business within the travel space to centralise data from across their back office systems from their GDS or any other OBT, as well as systems in finance, marketing and sales.
Once you've got that data in Domo, you can start building custom reports within a no code environment and then with that same data begin to build bespoke AI agents and automation workflows that that serve your specific business needs.
So if we look under the hood at the back end of Domo, essentially what you get is a really simplified, simplified integration suite that allows you to connect to your data regardless of where it lives.
You then centralise that within a single location, clean it, transform it so that it can better serve your specific needs.
Then then at the front end of the platform, you've got the ability to build entirely custom data applications.
So that can be for your own internal teams, but also for your external clients.
You can give those users the power then to interrogate that data even further.
So just beyond that, you know, dashboard level and they can use natural language or set up specific alerts so that they can be they can action what really matters to them across all of these different components.
It's important to bear in mind that Domo's been built with with business users in mind.
So the ethos across all of these remains billed once.
So automation exists in each of these different pillars from data integration to task automation.
And that task automation extends to your systems outside of Domo as well.
All of these components have then been further enhanced by this top layer here that we call Domo dot AI.
And that further simplifies the complexities that we'll be, we'll be well accustomed to when it comes to data for, for those of us who you know, and, and I, I'd assume it'd be everyone here who are concerned about governance, you do get full control over how your data is used and set up within the environment as well.
So yeah, big component is that all of this is an encapsulated by that governance and security layer.
Wanted to, to give you a quick look at some of our customers in the travel space that are already benefiting from, from this type of architecture.
So a few names here that you'll probably recognise.
One to call out that that I've worked really closely with over the last 18 months is, is take two and, and really great to see how they've been able to unify all of their systems and actually reduce, you know, reporting turnarounds from what was days to, to to minutes and seconds, which has has saved them, you know, hundreds of hours annually.
So and that's 11 great use case and and we'll definitely look at some of the components that have made them so successful in terms of using the Domo platform.
So just to to pinpoint a few of those challenges that we keep seeing time and time again.
And and you know, hopefully will will maybe not resonate with you, but definitely something that we'll look into today.
Firstly, what we see is that reporting within the space can often be A1 size fits all approach.
So you know, we're relying on third parties to manage this reporting, which has significant cost, but it also means that our clients are being bombarded with hundreds of reports that they might not necessarily be be leveraging in a way that's valuable to them.
So when we look back and say, OK, what really was the return of investment on that solution, it's difficult to to equate that to a number because we can't really understand what people are actually using.
The second piece here is we often see there are quite complex integrations with with source systems and that can be because some of them have kind of a legacy set up using stuff like XML.
So you know, your GDS, your back office system, while they're great at their respective functions, it can be difficult to extract that data and then leverage it in a way that drives really valuable insights for your internal teams and, and those external clients as well.
Finally, when we do get access to those data, those data points, we have a second or another challenge, which is around data quality.
And without the right tools to address data quality, we've seen, you know, TMCS that have been spending hours of, of manual manpower to, to remedy or reconstruct data in a way that that can work for them.
And with all three of these components, what we result, the result is a is a data infrastructure that isn't really prepared or equipped to, to meet the evolving needs of our clients in a world that is so focused on what can AI do for me?
How can we automate this process even more?
The good news is that we can address all of these challenges with with Domo's architecture.
So this is just a quick view of some of the systems that we've already successfully integrated in the past.
And if we just look at the way this architecture works, we have these core systems at the side.
We can then integrate these with into the platform using our connector library, clean that data up, join those data, those sources together and then build reports that are used internally and then of course share externally with clients.
This is an architecture that we've seen work with existing clients and we continue to build on the functionality that we can offer both from, you know, the connectors that we have available, but also the platform functionality itself.
I think one thing that we should point out here as well is what really makes this part so powerful is that as a business, as ATMC, you will remain in full control over every part of this, of this journey.
So once data lands in Domo, you'll see that there is so much more you can do with that data beyond just your standard reporting.
So we look at, we call them data applications, but actually you can build, you know, AI agents, you can, you can build out business workflows that Dr.
automation not just in Domo, but within your other systems as well.
So as I hand over to to Marius, the key areas we'll look at in today's demo will be how we get data into Domo, how we clean it up and enrich that data with other data points from other systems.
We'll look at your options for building reports, including those AI driven options and and data exploration with natural language self-service.
And then of course, the big part of this today will be your options for automating business processes and using a Gentic AI.
As we go through the demo, I'll be charming in with a few examples from our customers who are already using the platform.
Please put any questions that you have in the chat and then we'll get round to to answering those at the end.
So Marius, I'll hand over to you perfectly.
Let's jump over to my screen.
Thank you so much for the yeah for the introduction and really setting the scene here that was ideal.
You're in great hands with Claudia.
She's always our our expert when it comes to travel management and she worked with so many clients already solving their problem.
So she will, yeah, chime in as she mentioned where, where the particular parts of the platform help particular customers and really highlighting that.
So let's bring all of this to life.
So for me, my goal is to show you around the, the platform really from data to business insights to, to data products internally, but as well externally to to clients.
And while we do that, bear in mind maybe the challenges you have and face internally currently and where Domo could potentially help.
And then we're always keen to get in touch directly and have a chat specifically about your, your goals and and objectives.
But yeah, let's let's get started.
We are already in the Domo platform and and a user could potentially mainly see this as like a App Store of his or her most important data apps directly in the browser and or maybe the the builders.
We we can see it as a as a toolbox, right?
We have a very deep platform, very wide platform that allows you to do a lot.
Any, any sort of data, data challenges can be solved with a platform in one way or another.
There's we, we'll touch on a few.
We really go end to end from start from the data source all the way into the application and the dashboard, even into workflows and automation.
A little bit of we're going to show all the way at the end like an agent even helping us to automate our, our workflows and really helping us to yeah, to augment our, our business.
So what we're going to start with is actually the outcome of of what we're going to show today.
We just give us a bit more space.
So this is the the travel analytics app.
I'd like to start with the outcome because then you know where we are leading with this, where we're going with this.
And this could be an app or might might call this a dashboard or a combination of dashboard dashboards that is a available to our users, to our business users, or even our clients and customers in order for them to understand the the yearly spend, the global trips, the really most important KPIs for their business to really stay on top of those.
For instance, here the advance booking type target and time.
So how, how often have our travelers booked in advance a week or maybe 10 days, which is our, which is our target, right?
So we can stay on top of our KPIs wherever we are.
So this works really well on the phone as well.
It will adapt to the phone on screen.
So if you have execs maybe that are struggling with getting the right data at the right time, we can do that on the on the phone on the iPad as well or directly in the browser on anyone's laptop, of course, as well.
So everything here that you see is very, very interactive.
So we're working with the data set around travel expenses.
Think about Concur maybe, and this could be an internal use case, maybe a a large company enterprise wants to understand how their employees are travelling around the world, how much they're spending with which partners, for instance.
That's exactly what we see here in the the charts below.
Or there might be a client of yours that you where you prepare this kind of analysis for them and share either and directly in your portal or you they get, they get direct access.
That's really up to you.
And all of the data is of course secure and and highly governed.
We're going to show this in a moment as well.
And from here on, I can click through my, my, my top flights, for instance, my flight routes, my hotel partners.
So this is what you can design.
This is what you can really build with Domo, a very interactive user interface and you can really control the, the, the look and feel of, of all of this, right?
So I might come in as a, as a user, I want to stay on top of my data.
I want to see the latest data, the data is automatically refreshed.
So I can know what, what I can expect and can trust the data as well.
But so I personally have worked in BI for over 10 years now and I haven't seen like the perfect dashboard or data app.
So there are always some, some additional questions.
And that's already where we can tap into our AI layer where we can ask direct questions on the on the context of this app, which is then really which already knows like what is the data source underlying this app and how can I then answer these questions?
For instance, we might want to understand what are the busiest trouble seasons.
So simply by typing this, Domo will help me now to query that data and to get a direct response, right?
So we see the the most or the the busiest month in in this case would be would be March and the busiest season being Spring, right?
So that's already an additional insight I was able to get simply by using our AI capabilities.
I get a chart as well, which I can adapt and make make changes to.
I might have a follow up questions now like like what or which countries are visited most by the employees and I can kick that off.
And again, the AI chat will help me to understand that answer very quickly.
And have bear bear in mind like we have loads of users in in your client base or within your organisations where they might not be super comfortable with data and analytics and building utilization.
So this is like a great way to really share data with with anyone in the business or within your client base and giving them access to to analytics in the easiest way possible simply by typing, typing it in to into our chat.
We can even go in into much more like sophisticated questions as well maybe want to understand like the the average cost of international versus domestic travel.
So this is AUS based company, so domestic would mean within the US flying from places like San Fran to to New York or Florida, for instance.
And again, we get a direct response very quickly.
And another insight we weren't able to get from the app itself, from the dashboard itself, right?
Understanding, OK, the average flight cost for international flights is actually you over 1600 versus just 580 for domestic flights, right.
Just a few examples where we can tweak this.
Now we can change the, the chart type, for instance, we can iterate and get answers to further questions.
But that's already our end goal, right?
Like this is like real data analytics in the hands of of anyone that has questions and wants, wants to get answers of those questions in with their with the actual data, right.
All right, So how do we get there?
Right.
It's it's a bit, it's of course a journey and the journey starts with data.
So let's have a look at the Dermo data warehouse.
So this really comes down to where your data is stored.
So we give you full flexibility.
If you have invested into technologies like Snowflake data bricks, your your cloud data warehouses, you can leave your data completely where it is and simply give Domo access to it.
And then you can use it throughout the platform for every single capability.
Or you might choose to bring in all of your data into Domo and use Domo as your data warehouse.
That's possible as well.
We see in our internal sort of test and demo environment, we have loads of different systems like here in this in this circle, you can see as well where the data is coming in from the SQL Server data source for instance.
But the the point is that is that I can see all of my data in one unified view, right?
So we obviously have a lot of data already set up.
So how do we get started?
We simply connect to data up here and here you see our deep cloud integrations like Azure, Bigquery, Oracle, if you have invested into these platforms.
But we've noticed a lot with particularly companies in the travel industry that they have loads of different quite diverse data data systems they want to connect to, right?
So there might be a simple Excel file or an XML file, maybe Salesforce, any of the the cloud systems.
And we have connectors for these already already set up things like Concur as an example, right.
So we're using a lot of Concur data here in our example here, but it might be like the underlying database as far like SQL Server.
We have the the connections to all of the the databases of course, and then a lot of the travel management systems run on API so we can bring in API data as well with our Jason no code connect.
And the the point really is that we as a as a simple data BI user, we can connect to this data source as long as I have the credentials, for instance, when we, but we don't need access to the IT team to set all of this up for us, We can we can simply do it and add more data sources as we need them for our business use cases.
Yeah, exactly that.
So a couple of ones to to highlight here.
We know that Travcom uses SQL Server.
So we use that connector for other systems like train line or Magnatech.
That's when we've used that Jason no code option which allows us to to really quickly set up an API connection to pretty much any system.
A really nice use case for this one is now a few years ago, but one of our customers Gantt during COVID, they wanted to basically layer.
You know, health data on top of their itinerary data and they were able to use the connectors within this library to to bring all of that into one place and then provide real time health tracking according to where different travellers were in the world at any of the time.
So yeah, this is a really powerful tool to bring in data from so many different systems, including those third party systems that, you know, perhaps aren't our data, but it's publicly available online.
Perfect.
Yeah.
Thank you for that.
Well, that additional context and and that, that example and yeah, that's essentially the the first.
That's a starting point, right, where we bring all the data in that that we need.
And then if you have a clean data set to work with straight away, that's amazing.
Then you can get started building visualizations, building apps, building dashboards.
But what we've seen typically is that we generally want to bring in data, data sources together, bring them into the the same format, harmonize them, maybe filter, clean that data up a little bit.
And that's what we can do with data flows here in Domo.
Got an example here of a certain data flow and data pipeline.
We can see the input data sets, the output data sets that we can then use throughout the platform.
We can see how often that was run and the lineage of this as well.
But let's have a closer look into the actual data flow.
So what we're, we're actually building 2 data sources here.
On the top we see we actually want to create a analysis with our travel budget.
So we want to compare our travel budget towards the actual travelling expenses that has happened and the transactional travel.
The transaction travel data needs to be aggregated to the group by to bring it onto the same grain as the travel budget.
Right now we can join it, select the columns that we need and I'll put it right directly into a data source.
We can write data into Domo or we can choose as well to write into a database or into another system in a write back sort of way.
And then down below here we have a more, a bit of a like a richer data set, very granular where we have our global travel data.
We clean that up a bit.
We add some some date operations to bring the dates into the right format.
So with data, there's always a little bit what we can clean up a bit, optimize and that's what you get here on the left side with all of our data flow capabilities, right?
So we can filter, we can, we can calculate new fields, we can create new fields, new calculated fields for our business logic.
For instance, if we are a bit more advanced when it comes to scripting, we can bring in our Python scripts or even data science capabilities like forecasting, clustering directly here in the UI without any, any coding necessary, right.
So we have a super simple UI to use if we want to use or do one.
One example here, let's say we want to calculate that advanced travel, the advanced travel days for each booking.
And we can bring in our date operations here.
And all we got to do then is connect this up to our our data flow.
So into our output, bring that back into the data flow.
And then what we got to do is configure it using the the UI, right?
So we might call this advanced booking, booking days and then we want to subtract 2 days and in this case will be the travel date and then the SEC and then the the other date in our data set, right?
So we've got that all set up.
Then we can run a quick preview as well.
And I can show you a quick preview of the data, how it looks like for our for our next step.
Yeah, this this particular part of the platform is really powerful to address that that challenge we mentioned around data quality.
So it really gives you the ability to essentially build out what data you want to clean up, clean up.
And then when that data refreshes, it automatically runs that process for you on a set schedule.
So a nice example that we can say here is, you know, often times if we have a hotel that's constantly changed names and it's difficult to build out reports on because it's constantly changing names, we can essentially map that to the correct or what that should be saying so that in the future we don't have to constantly manually make that different, make that change ourselves.
So I'm a really powerful part of the platform to address those data quality issues that you you have from some of those source systems.
Exactly, exactly.
So we can never, you know, look now at the the preview, so we get an idea of what the data would look like if we were running this whole pipeline now.
So we see like departure airport, arrival airport and the time of travel and so on, even longitude and latitude values that we can then use for our analysis downstream, right.
So this is really your your go to spot when it comes to joining, update, harmonizing data, cleaning it up for analysis.
And you don't have to run this manually, of course.
You can then set up scheduled.
So you can run this let's say every every week, every day, every hour.
It really depends on how often that underlying data is updating and refreshing.
And you can build the, the correct schedule or even triggers to update all of this, all of this data.
So that's the next step.
So we connect the data, we clean it up, brought it into the right shape for analysis.
And now we can have a look in at the at the actual data set here.
This data set has been shared within the platform already.
So it's a great way actually to, to discover data as well to discover visualizations.
But before we do that, I just want to point out like the, the governance elements on top of this data set now, right?
So we can create personalized data permissions or row level and column level security policies that really allow to secure the data and people only see the data they they supposed to see or, or clients, right?
Then it's even more important that we define the the policies on top of our data sources.
We can even add for the purpose of the AI use cases within Domo.
We can add a a data and AI dictionary here with all the different column names and we can say, OK, this is relevant for AI or not.
And we can add further descriptions and synonyms.
For instance, we've got the the PNR here as a column name.
I bet all of you guys know what APNR is, but maybe it would be good to describe this to AI in specifically and say that's a personal name record, which is a digital record of the, the travel plans, right?
And that will help AI like AI chat that we've seen to, to really analyse the data and find the right fields for our questions, but for other, other use cases as well.
So we could now kick off AI chat, for instance, to analyse the data a little bit, or we can look into the, the content, the visualizations that other other users and other colleagues have built directly here in, in Domo, right?
So we might select one of these.
This is then where you can create alerts as well based on a certain KPI for instance, and you get automated alerting into your inbox directly.
But most importantly, can you can dive into each of those visualizations and really customize them.
So we, we really encourage our customers to take control of their, their data, their reporting, their visualization layer.
And this is the, the UI in Domo to do exactly that, right?
So we might want to change our the aggregation of a certain metric from some, let's say we want to understand the average cost for average cost, this is not the ideal way to to visualize the data.
So let's actually change this into a horizontal bar.
So you have loads of different visualization types from simple to more advanced and data science visualizations.
And then on the left side, you have all of your your data.
So we see this is a really rich data source.
We can simply drag and drop our data into the into the view so we can understand and build, build the visualisation that that answers our questions or that we need for our application and dashboards further, further down the line.
Yeah, this, this particular part of the platform as well comes back to kind of the taking ownership of of your own data.
So the challenge that we have previously is where, you know, we're relying on on third parties to deliver these reports for us and, or maybe even just a select group of individuals within a business.
And, and that means that we have that delay between actually getting access to that data.
So one of our customers has actually given access to to this particular part of the platform to account managers so they can go in and start building, you know, particular visualisations, particular reports, depending on client demands, so they can go in and serve themselves in terms of getting access to that data.
So, yeah, really powerful in building out that ownership of data and then reducing that data backlog that we we often see.
Exactly.
This is really the the self-service piece.
I can now filter, I can sort my data.
In this case, we changed this now for to answer a completely different question like the the average cost per per country that we travel to and we actually see Taiwan and Japan being here top of the list.
So again, there could be a valuable business insight for us to look closer into.
We never lose any any detail here.
We can always dive into the actual data roles that are creating that particular part of the the visualization.
And we can add a further calculated fields or metrics or KPIs with a simple calculated field editor here, right?
And we get actually AI to help us to, to generate these these calculations as well.
Because we did the advanced travel day calculation already beforehand and ETL, we can just make use of that straight away.
OK, we can choose now to either save this and share directly in this case, I just kind of, I'm just going to close this for now because I don't want to change this, this beautiful doughnut.
And then what we can now do is we jump back to the, the dashboard, the app that we that we started with in a in a second.
But I want to show you one more, one more thing before because so that's one way to do it, like in terms of analyzing and visualizing your data.
But there's actually a, a much, much quicker way as well to get started, which I absolutely love.
So we just, we have a content builder agent that is using our AI integration as well.
We just have to point it to a certain data source.
Of course, we take our our travel data here again, and then the agent goes away and looks into the like cruise the data checks what kind of key inside categories we've got.
And for me person, that's the fastest way from data to actual business outcomes and and value to the business, because this is now how we can see what are the, the trends, what are the insights we can generate from our data and what kind of visualizations can we build as well.
That's like the quickest way to get started.
If I start working on a new data set, this is where this is where I go and let our, our agent help us or help me in that case to, to, to get started and build that foundation of, of visualizations.
So we see lots of the visualization already popping up below.
Let's have a quick look at the, the key insight categories.
So we see the, the travel spending analysis, geographical patterns, booking efficiency, traveler behavior, and so on.
So really, really important insights that we want to generate visualizations for and then started as well already to yeah, to visualize that data, right.
Some of it are simply tables or bar charts, some simple visualizations, some more advanced ones, KPIs.
And then I can really choose where to where to get started, right.
What do you, what is particularly valuable?
What do you want to use and like the average cost by by year, for instance, or our most utilized airlines.
These are really important piece of analysis that I can now pick and create an application for right with a single click.
So I literally, you know, 2 clicks to go from data to visualization over to two applications.
And now Domo's working in the background to bring the charts that are selected into an app.
And then I can show you the, the app builder functionality as well.
So we've got a, a travel map here where our travellers are are travelling too.
We again see that that hotspot here in the US because it's AUS based company.
And then we can start really tailoring this to what, what we need and what we want to design for our with our business outcome in mind.
So we can change the theming here, might want to have a bit of a lighter theme.
We can change the, the colours, the the fonts and really make make use of the visualizations that I created or bring in things like workflows, even lists, images, anything is a simple drag and drop directly into my into my app if I want to add some text or an image.
So it's a really very, very simple tool to build my application, build my dashboards.
I can add more pages to this.
And then we're very quickly at a state where we can share this out and build an interactive app like we saw at the beginning, the travel analytics app, which I going to run you through in a little bit more detail now where you while while you understand how we actually got to this spot, right?
So most of it was really getting the data into shape and cleaning it up.
And then the rest was fairly easy in terms of building visualizations and then simply bringing them together into the same into the same topics, right?
So here's like a executive overview.
We might want to have a traveler analysis as well, like a traveler deep dive.
So we see our top 25 travelers.
There are a few colleagues that travel a lot like even over 100,000 a year or in total rather.
And then we actually see now the advanced travel histogram, right?
So that's using the calculated field that we created in our ETL step and where we see, OK, most of our travels really book fine advance 21 days or or even further, which is amazing.
But some of them are very booking very short notice trips as well and particularly some trips that are quite expensive for that reason.
So that's another business insight we can actually dive into and used to optimize like the the travel patterns.
I'll remind some of our culprits here in that chart to, to follow our travel guidelines or explain why they have booked like a day in advance or even on the same day.
OK, that's our traveler analysis.
This is inspired by one of our customers as well.
They wanted to understand the the leakage of their the travel bookings, meaning how many users and and employees were actually booking off the preferred platform and how much potential leakage do they do they have?
And that's what this dashboard shows very nicely.
We can we can filter on a particular time frame or a particular department, for instance, particular business unit, and then we go into an area where we step into a whole new area in terms of business, business outcomes and business value.
And this is actually still part of the app where we can actually utilize like an an automated AI agent to help me as a travel agent to do my job quicker and more more efficient and have more time on the time for the actions that are really really important right.
So in a world of like hyper personalization, I can, as an agent come in and create a, a tailored itinerary for every single of my travelers, right?
So the the agent works really, really simply.
I can maybe select all the travelers that travel to say this month or going to travel this month.
Then I get a detailed list of all of these bookings and travelers.
I get a map down here so I can narrow it down even further based on what I'm looking for.
In this case, let's pick my home country in Germany because they're understand the the recommendations and if they are suitable or not.
And then we see all of the bookings that go from the US to Germany.
And we can then, for instance, pick, pick an interesting one.
Let's pick one over here, select his DNR and the the date when he's flying.
And then this is already prefilled now in in our form.
So we have the PNR traveler name, travel date and optionally I can add further requests for that itinerary.
So maybe this is AVIP traveler, I could put in VIPVIP client and maybe I have a personal relationship with a traveler and I know he, he is allergic against seafood, right?
So I don't want any, any seafood recommendations in that itinerary and in that e-mail.
So I can simply type no, no seafood and that's all we got to do and we can kick that off.
So that will kick off a workflow now in the background that will and an agent in the background that will help us gather all of the information that we have about the traveller and combine that with our policies or a travel recommendations all into one.
Single itinerary in the that that we can then approve or or decline.
Let's have a quick, quick look at the the workflow here, how it's set up.
So this is really taps into that area of triggering action directly in my data application in my data products, right.
So it's triggering a business workflow.
This is the trigger here at the start and it goes straight into an AI agent task.
So that's the agent that we set up.
Let's have a closer look into how we've set it up and the instructions that we've given it.
So the prompt here is basically take the data from the form DNR traveller date and the agent requests.
And then before we send it out to a customer, we will be able to approve that.
So we'll still keep the the human in the loop.
And these kind of things are not what we how we imagine the itinerary to look like in a ideal world.
That's all built and defined in the instructions.
And then we can give that agents of certain tools like sending out emails or querying data.
And most importantly, we give it knowledge.
That's really important because the AI model is then grounded in the data of that particular use case or in this case like the the travel analytics data plus train booking data.
So we're going across systems here and we have further unstructured data.
So we uploaded like a business travel policy and a travel guide with recommendations and the agent will take all of these into consideration, right?
That's a lot of data, a lot of information and that's what the agent will use in the in the workflow then too to to work on the task and to complete the task.
We could now test the agent as well.
But let's see if the actual agent that we triggered off a couple of minutes ago has has come to to a result.
Have a quick look and lo and behold, we actually have a a travel itinerary.
So that's for Huan, that's our traveller.
He's travelling to Munich and Cologne and we have all the key, we have all the key information all in one e-mail across systems, making sure all of these are still within policy even though he is AVIP client.
But all of these options going to be more premium now based on our special request, right?
And yeah, Juan is doing a lot.
He's travelling over a few days to Munich, to Cologne.
We have his flights here from Detroit to Munich.
We see what is recommended in terms of getting to the from the airport to the hotel, for instance.
And most importantly, if there's something like a like a gap in his in his schedule on his itinerary, the agent will recommend certain options as well for activities or, or restaurants or even a train and hotel bookings.
All right, so now I'm just conscious of time here.
So let's just kind of wrap up, I think a few key takeaways on on this one.
So let's do it.
Four things that I think we can take away from from today's session is just that, you know, TM CS travel business in the travel world need a complete data in IAI toolkit to to deliver what they need to.
And ultimately you need to be able to do all of this on the fly, build new reports and in just a matter of a few minutes.
So yeah, no more paywalls giving you guys full control over that data.
And then in order to stay at that forefront of of AI innovation, you need a platform like Domo, which can give you a host of tools to to keep on developing, innovating and use your data for.
There's so much more than just those standard of reports.
So, yeah, big, big take away at the end if you leave here with nothing but this that it's data can do a lot more for your business than just those standard reports.
So yeah, we don't have time for Q&A today when we respect your time.
So we put a link in the chat to, to to reach out and yeah, send any other questions that you have.
Marius and I are happy to to jump on a call with you following up on this this session.
So again, thanks everyone for your time today.
Thank you, Raj.
Catch you later.