 So our main goal is to take care of the patient right and so when when a patient is introduced to our system We want to be able to take care of that patient and their family members in the best possible way that we can So if we're working with a very disparate Organization where we're on multiple EMR specifically It's hard for us to identify that episode of care for that patient So the MDM piece particularly with the patient domain allows us to do that it allows us to View the entire episode of care for that patient to see you know you went to these doctors offices You had these things done you went to this lab you had these tests done you went to the hospital You had this procedure and this is what your follow-up looked like so from and we're also conscious of the patient's expense in all Of this as well as you know, what would have what's the provider's expense? What is the the payers expense so you want to make it cost-effective you want to make it Accessible so that are there services that that a certain zip code or patient population needs that that we're not providing that we We can provide and so this is the whole entire continuity of care to take care of our patients the best way we can Yeah, it's a great question Really what happened is that people started collecting a lot of our data about a decade ago And you know the promise was you can do great things with this There are all these aspirational use cases around machine learning real time. It's going to be amazing Right so people started collecting it they started storing one petabyte two petabytes And you know they kept the sort of going back to the boss and say hey this project is real successful And I'll have five petabytes in it at some point the business said okay, that's great But what can you do with it? What business you know are you business problems are you actually addressing? What are you solving and so in the last couple years? There's been a push towards let's prove the value of these data lakes and actually many of these projects are falling short many are failing and The reason is people have just been dumping this data into the data lakes without thinking about the structure the quality How it's going to be used the use cases have been an afterthought So then number one thing in the top of mind for everyone right now is how do we make these data lakes that we have successful? So we can prove some business value to you know our management So towards this this is the main problem that we're focusing on towards this we built something called Delta Lake It's something you situate on top of your data lake and what it does is increases the quality The reliability the performance and the scale of your data lake what I have seen the works is finding people who have a track record of solving important business problems and using that to select the people that you that you hire because the Having a sound Education in the technology is one thing you got understand the business domain and the problem that you're trying to solve That's where the value comes from the business stakeholders value someone that can understand their Problem that they're trying to solve or the opportunity. They're trying to take advantage of so find in those people that have a track record of solving meaningful problems to me has been a Way to find the right the right folks in that in that area if you Take a look at the whole journey of data within organizations a lot of data is still siloed in different Systems within a different environments could be a hybrid on-frame it could be multi-cloud and on and Customers need this whole end-to-end experience where you can go ahead and take that data move it to cloud Do data cleansing on it do data preparation? You want to be able to go ahead and govern the data Know what data you have with like a catalog also informatic up provides all of those capabilities and if you look at Google Cloud We have some highly differentiated Services like Google BigQuery, which customers love across the globe to go ahead and use for analytics We can do large-scale analytics We have customers from few terabytes to hundred plus petabytes and storing that amount of data in BigQuery Analyzing getting value out of it and from there all the AI capabilities that we have built on top of it This whole journey of taking data from wherever it is moving it cleansing it And then actually getting value out of it with BigQuery as well as our AI capabilities that whole end-to-end Experience is what customers need and with this partnership? I think we are bringing all the key components our customers need together for a perfect Yeah, absolutely, you know with MDM the promise of MDM has always been creating a trusted Authoritative version of any business critical entity and who are the most important business critical entities for any organization customers, right? So almost 80 to 90% of our you know if our customers are talking about Reinventing a new customer experience because some of the things that they've been telling us is that we've all learned You know they're in the past that a bad customer experience means that you know We've all had those experiences. We go to a hotel. We use a particular airline We have bad experience and we say promise ourselves. We'll never go back there again So organizations have always for years now understood that there is a cost to Not delivering a good enough customer experience the big change that I'm hearing at least over the last You know year or so now and especially at this event is that organizations have now been able to quantify What great customer experience can mean in terms of a premium that they can charge for their Products or services now that is a big shift when you start thinking about saying if I deliver a better customer experience I'm actually be able to charge 10 cents more for a cup of coffee I can charge, you know 20% more for an airline ticket that now has a direct impact on the top line Yeah, absolutely. Well show the relationship with informatica for us has become important over the years as That data has exploded right it used to start off You had a spreadsheet of some numbers and you wanted to try and understand what was in there and tabloid I'll help you with that but then as data lake started coming on the scene and not just a single data lake but Multiple feeds of data and streaming data and data is here and data is over in Europe and data is wherever it happens to be That becomes a real challenge for the individuals who have some questions of that data So tabloids only as good as the data that we can get our hands on so to have a great partner like informatic who can Marshall and rationalize where all that data is is a Valuable partnership for us to have the way we think about is that you're exactly right. I would just the way in fact It's so Interesting the analogy I use that data is everywhere study the blood flowing through your body, right? You're not going to get all the data in one place to do any kind of analytics, right? You're going to let it be there So we say metadata is the new OS bring the metadata which is data about the data in one place and from there Let AI run on it and what we think about AI is that think about this LinkedIn is a beautiful place where they leveraged the machine learning algorithm to create a social graph about you and me So if I'm connected to John, I know now that I can be connected with you Same thing that happened to the data layer So when I'm doing analytics and I'm basically searching for some report I don't know I threw that same machine learning algorithm at the catalog level now We can tell you you know what this is another table. There's another report This is another user this one and we can give you help like ratings within that Environment for you to do what I call analytics on your fingertips at enterprise scale So that's an extremely powerful use case of taking analytics Which is the most commonly done activity in an enterprise and make it accurate and enterprise It's funny what you say unlock the power of data is one of our catchphrases I'm meeting with CIOs around the planet who sound like their CMOs because they're using these phrases They're saying things like I need to disrupt myself or someone disrupts me or I need When it was a large oil and energy as a CIO for this massive company said data is the new gold Mine and I need a shovel Using these phrases and so to your point. Well, how do you do that again? We do think it is about getting the right platform that plays both on-premise and ties in everything the customers are doing in cloud So we see partnerships as being critical here But at the same time one of our fastest growing solutions has been our enterprise data catalog Which is operating at the metadata level are my peer in products while you likes to say How come you can ask the internet anything at all, you know, you show you straight with your kids ask you questions You just get online I don't know and get the answer But you can't do that in your own enterprise and suddenly because of what we're doing at the metadata level working with all Of the different companies around the globe through open API's You can now do that inside your enterprise and that is really unlocking the capabilities for companies around their businesses I think what's exciting about the solution they had is it was a great business case, right? I think that really resonated with attendees looking at everyone can identify with fraud analytics everyone's Unfortunately probably on a victim of it, so they get to see how it works I think it also focuses on the aspect of AI, but how do you operationalize AI? So there's the whole model building piece of it and you know informatic as a strong player there as well But now you say well, let's actually have the model we need to execute quickly How do we do that? You know with the giga spaces technology, but also combine it with the right historical context, right? To make the right decisions so that really does hit on how do you actually take AI and make it a real thing? Yeah, I think one I completely agree with you on the skills gap and obviously that's also a great opportunity as well because in reality a lot of the younger folks who are looking at what Careers they want to pursue with the right mindset in the right training This these would be great careers for them and this also the other great thing about this is this is across the country and across the world So you don't have to be in a specific location To have a successful career in as a data scientist or as a data stewarder, etc. So I think from a training Perspective we are actually working with a number of different universities. We actually started working with Indiana University to build a Curriculum that can then be available online available to a lot of different folks We obviously work with a lot of different System integrators and consulting partners who hire hundreds of thousands of people and they are starting to build some very very large Practices around data science. That's another avenue for career growth there and last we're also starting at a much younger age, you know, I think last year we talked about the next 25 program and Tomorrow when Sally's are back on stage, you will see an update on the next 25 program We're trying to get kids at the middle school level interested in this as a career