foreign McGuire and on today's espeaks we're taking a deep dive into the relationship between data analytics and cloud computing along the way we'll talk about Ai and machine learning to explore those topics I'm joined by a very special guest with me is Steve McMillan Chief Executive Officer of teradata Steve absolutely thrilled to have you with us today James it's absolutely great to be here thank you for having me excellent so at the data analytics sectors there's a lot going on and I'd like to see especially how it's influenced by multi-cloud use I mean that's a big theme this year what do you see driving this sector here in in 2023 yeah I think there's a lot of drivers especially around the data and analytics Marketplace for uh 2023.
I mean clearly what's got all of the attention recently is chat GPT and large language models and uh certainly in teradata we see that as driving use cases but we'll talk a little bit that in uh in a moment or two look I I joined teradata three years ago and um one of the reasons I've been in the I.T industry for over 30 years um but one of the reasons I joined teradata was because uh of its placement in the data and analytics space because as a market and a sub segment of the I.T Market that uh that is going through substantial growth um there's never a day goes by where there's less data in the world right than the day before yes so how you consume and how you analyze and how you get value out of that data becomes incredibly important and uh teradata was uniquely placed to be able to do that so that data growth is selling only something that we continue to see growing through 2023 and that will power the data and analytics uh industry but I think also what's been interesting in terms of the shift from 2022 into 2023 a lot of companies were looking at data and analytics for really growth use cases so you know how can we analyze the market better how can we get Market insights into their customer base and utilize their data so that they could put forward a growth agenda I think in uncertain microeconomic times uh companies kind of pivot their use cases towards much more cost control or expense control and utilizing the data that they've got in their ecosystem to really optimize their operations or the efficiency and effectiveness of operations and um you know I think RPA is a good example of uh of some of those use cases in terms of um you know robotic process automation right um but it's interesting how chat GPT but more specifically large language models are going to really disrupt that that Marketplace and continue to drive more growth in terms of the use cases that are deployed against the data and analytics Marketplace well interesting okay so if I'm hearing it correctly you're saying that the the large language model that's going to disrupt the RPA RPA sector how so how is it going to change that sector well that's just one example I think of the disruption I think um large language models especially deployed against um especially when that is deployed against um you know data that's trusted inside an organization uh can really start to um impact all different parts of the company operations and when I talk to organizations around the world I don't think there's a board in the world that's not asking about uh usage a chat GPT but really they mean the usage of large language models to actually look at improving their operations internally or can they utilize that technology to improve customer service um you know we've got a great example of a retailer in Israel um who's kind of implementing a future of retail um and an in-store experience so they have shopping carts and they want to make the shopping cart intelligent so that it can give you an Amazon like experience as you're actually physically walking around the store and so um we actually work with that retailer to utilize teradata technology so we utilize uh Terry data to do all of the feature engineering around that large language model and then we link it to AWS sagemaker and sagemaker does all the model training and then uh teradata Vantage Cloud then does all of the model scoring against Real data inside the customer's environment to actually get the shopping cart to enable shopping cart to make a recommendation to the consumer that's actually in the store and so that is a really great example of you know providing a brand new differentiated customer experience utilizing these Technologies but I like that example because it not only talks about how to use data and analytics but also in a cloud context I think it's incredibly important for data and analytics platforms to really integrate well with um the hyperscaler services and when we declare then say Terra data that we were going to move from uh a focus on-prem to being Cloud first yeah what we really meant by that is we would look at our company in terms of not trying to answer all questions to all people we would utilize best-in-class services and these those might be from a cloud service provider so actually um integrate into the data flows and data pipelines and the analytics use cases that our customers have so that they can use the best service for the right job and say that data pipeline to get the best possible outcome for their customers and so as we've looked at the evolution of the teradata technology I mean we are a 40 year old company you could say we invented Enterprise data warehousing but the next gen of teradata is all going to be about the the real linkage between cloud and multi-cloud um and data and analytics and then using the best of the hyperscalers services to bring unique differentiated value propositions and as I said in 2023 a lot of those are looking at optimization uh and cost in this expense optimization in 2023 yeah I definitely see that connection between the world of cloud and the world of data I think that's and using the hyper Steelers to get your best in class performance I mean I think that companies are often really confused about optimizing their data analytics practice I mean what what advice would you give them especially as it relates to the world of cloud because companies are trying to deal with their Cloud deployment and their data analytics deployment I think both are somewhat confusing yeah it's really interesting the whole topic of cloud optimization is a fairly Hot Topic in the marketplace just now as people want to ensure that they're getting the best value from their Cloud spend and they don't have accelerating and escalating costs from a consumption perspective and we have a very uh simple view of that and say teradata now what what we do is actually because we were born on-prem we actually take that technology to the cloud and we have patented capabilities that do workload management and query optimization um in the teradata system when it's running in the cloud which gives a very cost effective um you know we would say probably one of the best Enterprise class price performance capabilities around data and analytics in the marketplace um and that is delivered because we know how to squeeze as much as possible out of every ounce of compute and every ounce of storage that's in the high hyperscaler environment is made available to the teradata ecosystem and so customers can actually have a real Advanced view of data Financial governance utilizing the Terry data system and I think um you know other data Technologies which kind of they solve complex Problems by spinning up extra compute nodes inside the hyperscaler environment we solve difficult problems with great software inside teradata and so we give a very financially very well financially governed solution to our customers and that's really resonating inside uh inside our customer basis now especially in 2023 when there's a focus on that course and expense envelope but it's super interesting that actually um what one of the analysts actually asked me so what's your view on cloud optimization do you think teradata's revenues overall are going to be negatively impacted by Cloud optimization and my perspective on that was well our platform optimizes you know every millisecond or sub millisecond in terms of the delivery of value to our customers so is that we don't see that as a Tailwind at all for the companies we move forward and it's a real Mutual benefit for teradata and our customers hmm all right let's let's drill down actually a bit more into the shared data product portfolio you've talked about it but how exactly is teradata addressing the data analytics needs of its clients what is the teradata advantage in in a nutshell yeah teradata Vantage is our uh essentially our data and analytics platform um and it's multi-cloud so uh Vantage cloud is actually our teradata platform and the cloud and we deploy natively across all three of the biggest cloud service providers Google AWS and Azure um so advantages or product set name Vantage cloud is our implementation in in the cloud one of the great things about the teradata ecosystem and the teradata platform is it's consistently deployed across all three clouds and back into on-prem and so when you want to make that multi-cloud capability come to life you can do it in a very consistent way so it's very easy for our customers to move from their on-prem implementation of teradata Vantage to teradata Vantage cloud and run inside the cloud inside the clouds and the hyperscaler environments but we've just added to that product portfolio recently we announced it last August where we announced Terry did a vantage Cloud Lake which is a new uh completely Cloud native architecture uh which enables um you know self-service provisioning of um compute capability in the cloud it's deployed on AWS we'll have Azure coming out before the end of uh first half and really what that enables our customers to do is utilize and run data and analytics workloads you know outside of their core teradata system and Link data together um across multiple clouds to get a level of insight that they might not otherwise get so with that new technology and new capability really we serve the needs of Enterprise data warehousing we serve the needs of data Lake inside our customers we enable the creation of a data mesh or a and a query mesh into and enable the deployment of data and analytics in a use case and a pattern that our customers choose we think we've got one of the most flexible deployment Platforms in the industry just now you know you understand point out you talked about the the Vantage solution it's deployed to the three major Cloud players uh is is there a way where that data can talk to each other between the the three players you know as you were to AWS through Google like or is it is it siled once it once it resides on those three Cloud players yeah so we have a great point of view in terms of uh data for our customers which resonates really well with our customer base and that is um to get the best out of data and utilizing the teradata platform you don't have to move it you can just use it and set you right and the way that we do that is by putting teradata query engines close to the data so I we have a use case of a bank and Europe where you know the front office system I think is running on AWS um their back office system is that if you're running on Azure and then the utilize teradata teradata is implemented in both AWS and in Azure and they utilize teradata to integrate data together from their front office system together with their back office system and the um the the way that they do that is uh patented capability that teradata has is called push down query and so um if the Azure system can can send a query to the AWS system and the AWS will execute that on the data and the data gravity that's in the AWS system and just return the query results back to the teradata system that's running in Azure and so that stops the expense of Ingress egress costs of moving data from one Cloud to another and so and that we call that a query bread or a query mesh so it's it's not a data mesh as such because you're actually moving you're actually processing the queries next to the data you're not moving that data around and that capability also enables our customers to not only go multi-cloud between the three csps but if they have regulatory concerns that mean that you know they have to keep data on-prem in their Data Center then we can also link their data center data into that overall query grid so they can have data in Azure data in Google data in AWS and data on-prem and Link it all together in a way that other providers just can't uh can't touch makes a lot of sense uh all right well let's let's look at the big question the questions I think companies are worrying about really heavily and that is the future of data analytics especially as it relates to the cloud landscape there's so many different directions that could go uh what what is your prediction for the the near midterm what do you see happening in that in that sector yeah I think it's a really it's a really robust area for all organizations around the world you know if you read any CIO survey I think um you know the top three or four um categories of uh CIO spend where cios are either maintaining or increasing their Cloud spend you know that the top three are cyber security cloud and data and analytics yeah right um and so that I think that trend is going to continue um as organizations realize that in order to respond either for a better customer experience or a better operational outcome or better Supply Chain management you know they're good they're going to have to bring all of that data together I was talking to one of the largest bank one of the largest banks in the world actually um and they had a really interesting perspective in terms of uh competition they have a large internal I.T organization but their perspective was for them to be a bank of the future they are going to have to take advantage of the Innovation that the hyperscalers are bringing to the marketplace right and take advantage of all of that r d spend that those hyperscalers are executing against um so that they can utilize Services as they come up from a hyperscaler good example of that is large language models with Azure and Azure ml now having that built in and so that encouraged that particular Bank to really accelerate their Cloud strategy um because they saw that um in order for in order for them to take their I.T capability to the next level they know that they're going to have to take advantage of those uh services that are made available from the csps and that the the selected teradata is their data and analytics platform in the cloud is one of the data and analytic Platforms in the cloud because of that level of integration because our strategy really says you know we're going to be that uh connected multi-cloud data and analytics platform uh that really resonated with that bank and how they wanted to drive their technology strategy into the future interesting Steve I think you said it's a lot of good stuff I learned something and uh the whole world of of cloud and data analytics is obviously going to be growing dramatically in the years ahead uh thank you so much for sharing your Insight and please come back and talk with us again sometime Thanks James thanks for the questions great to chat with you
This transcript was generated automatically from the
video's captions and may contain errors.
