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Matthew Halliday: Alright, welcome everyone, we will get started in just a minute or so just waiting on some participants to join the call, so you are here for the fourth and the final session of EBS.



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Matthew Halliday: set of webinars that we've had diving into all things related to Oracle E business suite analytics and going through a number of topics we will again get started in just a few moments, so please stick with us and look forward to hearing from you during this webinar.



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Matthew Halliday: While we're waiting just another reminder a big push today we really would like to have questions, maybe we'll even get some swag for someone who asks the the best or most questions during today's session, so please do use that Q amp a option.



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Matthew Halliday: Ask your questions, we will answer those live and, as always, if your question comes to you after the webinar.



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Matthew Halliday: Do not hesitate to reach out to us here at encoder would love to hear from you, you can just go to encoding calm.



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Matthew Halliday: engaged for the chat Bot schedule a meeting with one of the experts here at encoder and we will definitely get answers to any of your questions, but would love to hear from you.



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Matthew Halliday: These these webinars are being recorded and will be made available, so you can watch any one of the sessions, so if you're coming today, for the first time.



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Matthew Halliday: You can go back and watch, one of the three sessions that came before and you're welcome to have that and, of course, share it with anyone who you think might have might have be interested in the content.



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Matthew Halliday: So.



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Matthew Halliday: Hopefully everyone's ready to go we're today talking about EBS content for analytics but specifically with the focus today for it and the analytics teams to support it.



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Matthew Halliday: And so the agenda today we're going to go through is just going to do a quick introduction to one of the offerings that we have here as part of this webinar series we're excited about.



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Matthew Halliday: Then we can have a discussion with two experts that I brought in ashland Mario my head soggy.



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Matthew Halliday: we're going to be talking a lot about our experiences with it organisations, how they should think about analytics what's changing and.



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Matthew Halliday: Some of the key things that we're seeing that we think of very important in the space, right now, so there'd be a lot of examples, a lot of conversation about real customer.



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Matthew Halliday: experiences and again any questions, please ask them put them in the chat put them in the Q amp a and we will get to those.



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Matthew Halliday: For those of you here last week, you will remember, Patrick as well, Patrick gave a wonderful webinar.



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Matthew Halliday: DEMO rather around supply chain he's going to be digging in a little bit more deeper to that and not just focusing from the business perspective.



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Matthew Halliday: But really unpacking what's going on behind the scenes, for all of the technical people are saying that looks great, but how did you do it what's the what's the stuff that's going on behind the scenes.



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Matthew Halliday: And again any questions put them in the Q amp a we will be monitoring and managing those and we will try and answer those questions as they come up.



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Matthew Halliday: So with that let's go ahead and kick off i'm going to introduce our speakers.



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Matthew Halliday: So for those who've been here here, you know who I am i'm Matthew how them one of the co founders, but also a VP of product and quarter.



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Matthew Halliday: i'm joined today with three really great experts in this field, who have a lot of experience so ashwin is one of the senior directors of product management here at in quarter.



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Matthew Halliday: wealth of experience, but number of years working at Oracle building out analytical applications working with lots of customers.



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Matthew Halliday: So he's seen it from that side and he's done a lot of that as well in quarter and he's a very well known and loved inquiry employee with our customers and a lot of people love to get time with ashwin and pick his brains on.



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Matthew Halliday: What they what what's going on in this space and also what are we, what are we doing here in quarter, we also have VP of product head saggy who's joining us as well and.



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Matthew Halliday: My head will be here for the first half an hour section he doesn't have to jump on a plane.



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Matthew Halliday: So he will disappear, not because he likes hard questions, we will make sure he sends all the difficult follow up questions you might have but.



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Matthew Halliday: we're glad to have him here at least here for half an hour, so thank you for joining us and and then, as I mentioned, we have Patrick with his 20 plus years of experience.



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Matthew Halliday: Part of our pre sales organizations sales engineering building out applications showing and proving out the value of in quarter and so that's been really a great lineup to hear from from for today, then we're gonna have some great interesting conversation.



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Matthew Halliday: bed let's kind of kind of kick in here and i'm just want to talk a little bit about our discovery October value sprint's that we have So what we are offering.



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Matthew Halliday: In this month, starting from right now, and you can avail yourself of this, and this is a really great offering and really there's nothing for you to lose but there's a whole lot for you to gain.



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Matthew Halliday: And the cost of not going forward to this could be cast just catastrophic to your organization.



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Matthew Halliday: You could find that you're really missing out on something quite unique, so what we're offering is 30 days free access to our cloud platforms, you can get started for free.



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Matthew Halliday: You can work side by side with a technical team here at in corner that will help you get up and running so you're going to be on your own.



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Matthew Halliday: it's not just hey I get access to software and then I got to figure it out and I don't have time for that we will come alongside you, and we will ensure that you get something up and running in a really short amount of time.



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Matthew Halliday: will also ask you bring us some use cases bring us something that you are struggling with.



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Matthew Halliday: Hopefully, something that would build your confidence in the platform don't give us something that's really easy that you go okay yeah cool I see how your product works, but you know it works in every other platform I have.



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Matthew Halliday: it's even you know, the best thing is bringing us a problem that you're struggling with.



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Matthew Halliday: Because bringing some problem today that you find you don't have a really great way to answer.



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Matthew Halliday: that's where we've seen great success with our customers we've closed a lot of deals and that enabled a lot of customers to do great things by taking on problems that they hadn't been able to solve.



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Matthew Halliday: And you know build something that's a value today that has high impact your business.



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Matthew Halliday: don't just make this theoretical make it highly practical and relevant and so that's something that we're offering you can make use of that again reach out say hey I want to make use of the discovery October value sprint for EBS.



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Matthew Halliday: or just remember the word sprint if you can't remember anything else to say hey there's a sprint thing that I want to do within quarter like can we can you get me started, and we will gladly get you up and running and so that's.



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Matthew Halliday: a really great program definitely definitely make yourself and make yourself available to take advantage of that and so with that i'm going to jump into our conversation, and so, first of all i'm just going to ask ashwin and my head and either one of you can respond to this question.



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Matthew Halliday: there's a lot of change that's been taking place after mighty organizations there's a lot of pressure that's put on them to deliver the demand for analytics has gone up.



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Matthew Halliday: organizations are asking for more and more people want to be data driven the business wants to be data driven.



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Matthew Halliday: The organizations that are responsible for providing that, though, have largely stayed the same size right they're not growing they're not.



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Matthew Halliday: You know, expanding at the same rate as the demand, so this is huge backlog, what do you think some of the things that organizations who are feeling the heaviness of the backlog of analytics requirements should be thinking of today what, how can they try and get ahead of that.



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Ashwin Warrier: know if you want to go first.



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Ashwin Warrier: yeah.



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Mohit Saggi: Sure, so I think I think talking to talking to our customers, you know one thing one thing that they continue to struggle with.



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Mohit Saggi: Is the is the the backlog of all the requests that that they get from business right and, and this is.



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Mohit Saggi: This is a will continue to happen with some of the more advanced you know data analytic solutions, you know that are based on data data lakes and and some of the other other offerings that are out there and and part of the reason there is that.



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Mohit Saggi: You know these are these are very complex solutions right there are there are multiple technologies multiple tools involved.



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Mohit Saggi: In addressing some of some of the some of the requirements that that business or it gets from the business, so I think I think, to me, one thing that that it can can start looking into as to how to how to simplify.



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Mohit Saggi: The the analytics workflow or the data pipeline that they currently have right like, how do we, how can we, how can we move move more towards.



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Mohit Saggi: more of an automated you know pipelines or or the other tools or the vendors that can that can help improve time to inside.



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Mohit Saggi: That it continues to struggle with so so to me, I think, looking at legacy tools, looking at some of the existing pipelines that it has and and think think about how to simplify how to modernize.



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Mohit Saggi: Some of that do to help reduce time to insight is probably one of the key things that that can move the Needle for it, as well as business.



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Matthew Halliday: I think we're at that ashwin.



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Ashwin Warrier: yeah great points now more I think we've we've seen this over the years, where.



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Ashwin Warrier: Where multiple large enterprise customers small customers time to insight is a key right, and if you if you talk to them about bringing in new cloud data sources or even legacy data sources with custom enhancements to tables and whatnot.



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Ashwin Warrier: Historically, the time to insight has been with the data warehouse model is the amount of work that it takes it does take time to make significant changes right.



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Ashwin Warrier: it's not an overnight course correction in a data model that is dimension model based, so the time to insight is one of the key measures.



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Ashwin Warrier: And one of the key value propositions within quarter, because you don't have to wait, the 10 weeks, you don't have to wait, the the three months to get a new deployment in place right.



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Ashwin Warrier: And the benefit of, that is, if you can take it down from 1012 weeks down to a week.



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Ashwin Warrier: Then the it team can do more value added services right they could focus more and maybe building machine learning pipelines or building some departmental Apps that they can are the framework for departmental Apps that customers can start to leverage.



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Matthew Halliday: Now.



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Matthew Halliday: Actually, just for those of you are live on the webinar maybe in the chat you can just put in like today how long does it take for you to respond to a request for.



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Matthew Halliday: let's say a new piece of information on existing report where it's like Okay, we need to change it for this new column, maybe comes from a different table.



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Matthew Halliday: But we need to be able to bring that in if you can put that in the chat would love just kind of see what that timeframe is.



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Matthew Halliday: There are no wrong answers here we've seen this up for some customers as much as you know, 16 weeks.



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Matthew Halliday: You know, like you know, three months right to be able to get this stuff done, and some people do it a little less but generally it's it's a significant amount of time, so.



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Matthew Halliday: Definitely put that in the chat would love to hear and will will read out some of those as they come in, we won't share your name and who you are we'll just kind of give the answers, of what we're hearing and seeing.



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Matthew Halliday: But I think that's that's you hit on right point here ashwin is that time to insight I like to think is for an analytics organization time to insight is akin to what a number of mq l's are for a marketing department.



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Matthew Halliday: It should be the one thing that you're judging is your organization doing well what's your time to first insight.



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Matthew Halliday: How long does it take you to take that that's a really good guiding principles guiding staff you to figure that out.



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Matthew Halliday: and actually there's a slide here that images show which I think helps showcase exactly how encoded can help in this space So here we can look at.



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Matthew Halliday: Speed to success or time to inside.



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Matthew Halliday: there's the old way of doing it, and all of the steps that you'd have to go through you know you start with the same data point right.



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Matthew Halliday: Whether you're going within quarter or you go with an old system you identify your data sources that's no different right, we all have to figure out does this data reside in this EBS where an EBS doesn't reside.



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Matthew Halliday: Or is this in some other application that very commonly I have.



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Matthew Halliday: Once you've done that you have to extract that data, then you have to get it, you have to manipulate it transform it put it into an environment get it into a structure.



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Matthew Halliday: That you're able to show we'll dive into a little more of these structures in a moment, and what they are, but you have to give to change the shape of the data, this is the key point right.



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Matthew Halliday: And one of the things that I was used to tell people when I was working at Oracle we did a lot of performance tuning, and a lot of times the performance tuning was not changing sequel statements.



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Matthew Halliday: people seem to think that performance tuning means I wrote my sequel incorrectly well, a lot of the ways now sql statements.



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Matthew Halliday: Cost based optimizes execution plans are getting smarter and smarter, to create the optimal based upon the intent and it's not always changing it like it used to be, it could actually be changing the data structure and it's oftentimes moving data and what we refer to as.



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Matthew Halliday: d normalizing the data right taking it from this highly fragmented setup into a very flattened couple of tables reduce the number of joints.



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Matthew Halliday: And then get it to perform this takes weeks and events, a long time, the big problem in here there's this that last box on the bottom left.



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Matthew Halliday: insights lead to more business questions and where do you go back once you identify it you go back to the top and start going through the flow again.



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Matthew Halliday: Within quarter insights lead to new questions you don't have to go all the way back to the beginning a lot of the steps the steps removed you don't spend all this time, and what takes you know literally months, if not years can be done and put into production in days, weeks.



00:14:12.930 --> 00:14:20.790

Matthew Halliday: And really kind of showcase that value so there's a real benefit for organizations to be able to kind of push and drive this through.



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Matthew Halliday: Maybe you can share a little bit about what you've seen my head, maybe in the space for some of the customers you've worked with spirit and quarter.



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Matthew Halliday: What does this meant for them, what would what was what was their environment, looking like how long is it taking what was the transformation, what did that mean to the business, but also what did it mean for the it organization that was supporting it and how did that make them look.



00:14:44.010 --> 00:14:53.610

Mohit Saggi: yeah sure, so I think we, we also shared this in the in the in the finance webinar that we did a couple of weeks, but but yeah so so recently.



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Mohit Saggi: We, we had a big fortune 50 media company, who recently migrated from from rbi to to encoder.



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Mohit Saggi: And they actually saw a huge difference of both, both in terms of you know how quickly they were able to respond to the business requests and also the quality of the of the outcome right so so so within quarter, they were they were able to go from.



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Mohit Saggi: You know, making like a inside taking anywhere from from two weeks to to less than a day and and what that what that under ended up.



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Mohit Saggi: resulting from from business point of view is this, you know they're they're not able to pivot and meet.



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Mohit Saggi: You know some of the more important requirements from from business so so So this was this was a this was a key driving factor, or the other key outcome that that that that was just a result of.



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Mohit Saggi: You know, putting putting in quarter and getting rid of some of the some of the legacy of warehouse type of type of solution that that they had earlier.



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Matthew Halliday: yeah that's um that's a it's it's interesting because organizations, I think, can look at this one of two different ways, you could feel one.



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Matthew Halliday: If we can do things a lot faster, what does that mean for myself for an organization doesn't mean in my organization is going to shrink and we expected to get rid of people.



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Matthew Halliday: What, in reality, we see like I have yet to see personally an organization just lay off people because they took in quarter and even though they were able to contract these timelines.



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Matthew Halliday: What ends up happening, what are these people start doing what do you then begin to see them doing in those organizations.



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Matthew Halliday: Because we don't see them getting laid off, even though we say become super productive what used to take you 12 weeks, you can now do in a week.



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Matthew Halliday: A lot of people might just think Oh, then I need one 12th of the amount of people on my team in reality we see a different thing panning out what have you seen with the customers you've worked with.



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Mohit Saggi: yeah so, so I think this is this is something that, as we mentioned earlier right so so once once once we simplify the the foundation or the other basic tasks that are required to enable analytics and then it can can focus on.



00:17:09.540 --> 00:17:16.560

Mohit Saggi: Some of the more advanced use cases right so so, whether that be machine learning or something else right so so that way.



00:17:16.860 --> 00:17:25.170

Mohit Saggi: It it can can can stay a little bit ahead of what what what the business users are asking for, they can be more proactive.



00:17:25.890 --> 00:17:34.200

Mohit Saggi: They can actually better partner with business in resolving some other some of the critical things that that business needs need need answered on instead of.



00:17:35.040 --> 00:17:45.660

Mohit Saggi: You know, always trying to do to resolve bugs or or working on incremental changes within within the analytics workflow so so so for this type of approach it can actually bring.



00:17:46.260 --> 00:17:55.290

Mohit Saggi: Some of the transformative changes in both how you know business report on data and and also to some extent, how business operates at the end of the day.



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Mohit Saggi: So that's that that that's that's really you know that could really change in how it is being looked at and and and eventually how how I can partner, the business.



00:18:08.310 --> 00:18:12.420

Matthew Halliday: cool thanks my head ashwin let's just talk a little bit about.



00:18:13.680 --> 00:18:17.910

Matthew Halliday: a topic that you brought up when we had the supply chain conversation and.



00:18:19.080 --> 00:18:26.760

Matthew Halliday: It kind of goes down to like what is the fundamental approach, regardless of the technology that we're using that we're seeing a very common today so.



00:18:27.240 --> 00:18:35.490

Matthew Halliday: Obviously, we know Ob is been around a long time people might be thinking, we need to modernize right there's a big push people saying let's go to the cloud what we're going to do in the cloud right.



00:18:35.850 --> 00:18:45.990

Matthew Halliday: And there's number of vendors cloud vendors that are out there, but all those cloud vendors, I think, have something in common right in terms of the way that you need to structure the data for them to build perform and work for you.



00:18:46.500 --> 00:18:58.620

Matthew Halliday: Maybe you can unpack just a little bit about what that looks like and then compare contrast with what does the quarter Way Approach look like and why does that bring so much more benefit to an organization versus.



00:18:59.070 --> 00:19:02.790

Matthew Halliday: just saying okay let's take our existing infrastructure and just put it on the cloud.



00:19:04.950 --> 00:19:06.480

Ashwin Warrier: yeah it's a good question, but I think.



00:19:08.460 --> 00:19:19.440

Ashwin Warrier: What you'll see even with modern technology, like the Oracle autonomous warehouse even with snowflake they create systems of record right there great if you if that's your soul.



00:19:19.890 --> 00:19:26.880

Ashwin Warrier: System of record, but if you're starting to move things there just for doing a dimension modeling and then your analytics.



00:19:27.450 --> 00:19:42.090

Ashwin Warrier: Then you still not solve the most fundamental problem that you've always seen and i'm sure every iot practitioner on this call will agree that one of the big challenges with dimension model is is like trying to put a square peg in a round hole right I think you're always trying to.



00:19:43.890 --> 00:19:50.070

Ashwin Warrier: reshape the data take all these three and a half tables these public API is that even the cloud vendors provide.



00:19:50.550 --> 00:19:58.290

Ashwin Warrier: and trying to confirm them into a very rigid structure that's your dimension model right and the challenges with that is it's great once you do it.



00:19:58.890 --> 00:20:08.340

Ashwin Warrier: Or if you putting it from from let's say your on premise Oracle data data warehouse to your autonomous warehouse or any other cloud data warehouse.



00:20:08.670 --> 00:20:13.080

Ashwin Warrier: you're still following the same approach right you still have the same inherent problem which is.



00:20:13.650 --> 00:20:21.240

Ashwin Warrier: You will still have to reshape any new data coming in, I mean if if somebody could promise that hey there's never going to be a change in the structure.



00:20:21.690 --> 00:20:29.670

Ashwin Warrier: Of not going to ask for a new aggregate fact or i'm not going to ask for a new star schema great you can stay with the old course, but every time.



00:20:31.050 --> 00:20:37.410

Ashwin Warrier: We know that there are new cloud systems coming on board, you want the business to be flexible and pick up the best of the breed.



00:20:37.800 --> 00:20:44.940

Ashwin Warrier: So you don't want to stick with just one vendor who gives you everything you want to go find the best solution out there, which means you, you might have to go change.



00:20:45.390 --> 00:20:50.160

Ashwin Warrier: The source, you might have to change the tables that you pull from and then the next thing you know is.



00:20:50.760 --> 00:21:00.450

Ashwin Warrier: you've got to build atl maps for every spoke in your star schema again right which is a significant amount of work to match your existing dimension model.



00:21:00.900 --> 00:21:10.410

Ashwin Warrier: But within code I it's so much easier because you now have your three enough form whatever format of data that you have you pull in the tables you build the relationships.



00:21:10.800 --> 00:21:23.520

Ashwin Warrier: If you cannot info the relationships, if you if you can infer the relationships great you're up and running in a literally a matter of hours, if not days so it's a big make fundamental change in how you can get to analytics and the time to insight.



00:21:24.690 --> 00:21:28.500

Matthew Halliday: yeah I think i'm Barbara savin son of firestone calls it.



00:21:29.670 --> 00:21:37.830

Matthew Halliday: Instead of bringing data to be I bring big data, and I think that captures it pretty well because what we're saying is your data is already residing in a format.



00:21:38.310 --> 00:21:43.320

Matthew Halliday: If we can bring the analytics as close to that as possible and do analytics on its native format.



00:21:43.710 --> 00:21:53.310

Matthew Halliday: than the need for data pipelines which are take a long time to build but also take a long time to process right it takes a long time to take data and move it and transform it completely.



00:21:53.790 --> 00:21:58.380

Matthew Halliday: If we're just taking data and incremental data and it's not even going through those transformational processes.



00:21:58.920 --> 00:22:10.080

Matthew Halliday: It means really it opens up a whole discussion now about analytics becoming operational analytics in a way that we've not seen the way that you cannot get to write with will be a and some of those products.



00:22:10.440 --> 00:22:16.530

Matthew Halliday: And there's no talk about getting you know incremental refreshes that are happening, you know less than an hour like.



00:22:16.860 --> 00:22:26.370

Matthew Halliday: The most i've ever seen, out of all the customers I ever worked with Ob I even as at Oracle was four hours, like that was the best incremental refresh time they could get down to most customers is once a day.



00:22:27.780 --> 00:22:38.610

Matthew Halliday: That opening up that space opening up the operational reporting and blurring those lines where it's it's you you bring in intelligence into the decision making.



00:22:39.030 --> 00:22:42.690

Matthew Halliday: At the time of the transaction where people who are on the ground, making the decision.



00:22:43.020 --> 00:22:50.640

Matthew Halliday: It begins to feel like analytics goes from being something that's going to be used against me as a store card scorecard to evaluate my performance.



00:22:51.090 --> 00:22:55.140

Matthew Halliday: and have everyone kind of pick apart, what I did right or wrong as an organization.



00:22:56.040 --> 00:23:07.110

Matthew Halliday: versus giving me analytics to help me make better decisions, so I looked at, at the end of the month that my measures and metrics are moving in the right direction because I actually have the data when I need it to make those decisions.



00:23:09.000 --> 00:23:20.520

Matthew Halliday: How do you see organizations leveraging things like spark and Python in in their infrastructure, what are we seeing with people.



00:23:21.510 --> 00:23:29.820

Matthew Halliday: utilizing how they're utilizing that oftentimes people might think spark and Python is reserved for the data, scientists and that's the only use case for it.



00:23:30.480 --> 00:23:39.510

Matthew Halliday: What have you also seen, though, in terms of maybe like business logic, processing and customers how they've been using pi spark within the quarter platform in an interesting way.



00:23:41.910 --> 00:23:48.600

Ashwin Warrier: yeah I can take a stab and more it's feel free to add as well, but by by spark is has opened up the door for.



00:23:49.260 --> 00:23:57.960

Ashwin Warrier: that a lot of the work that you do in your email pipelines, even without dimension modeling is data enrichment right, I think, even if you remove the.



00:23:58.620 --> 00:24:05.520

Ashwin Warrier: The transformation component for dimension modeling there is a significant value add in enrichment that happened between your source data.



00:24:05.970 --> 00:24:15.720

Ashwin Warrier: And what your analytics consumers need in terms of enriched data and that enriched data historically was done through etfs maps, you have informatica you'd had.



00:24:16.500 --> 00:24:23.850

Ashwin Warrier: ODI there are tools like DVD that do it today, but the whole realm of spark spark and what spark and provide.



00:24:24.510 --> 00:24:34.020

Ashwin Warrier: Is is not just limited to just the enrichment piece, you could, if you if you have your analytics on the cloud, and you can open up the, the number of.



00:24:34.770 --> 00:24:39.690

Ashwin Warrier: executors and you can use you're now starting to run extraction that could be spark based.



00:24:40.380 --> 00:24:46.200

Ashwin Warrier: Most accurately your transformation and your procedure logic can run through this where you can leverage the benefits of auto scaling.



00:24:47.040 --> 00:24:52.050

Ashwin Warrier: spin up in and shut down as needed the different instances that you have.



00:24:52.560 --> 00:24:58.650

Ashwin Warrier: And you can really start to perform at scale right, so your your problems of scale can be better managed with with spark.



00:24:59.070 --> 00:25:10.890

Ashwin Warrier: And the whole option of using spark means that you can do some great procedural enrichment with with to like incorporate encoded does all the modeling in terms of the.



00:25:12.060 --> 00:25:27.450

Ashwin Warrier: The extraction the enrichment and the Lord, but the enrichment are really value add that's being done with very simple code that you can quickly write up, you can take pieces of code from from get from others who've already done similar work and quickly build your own procedural logic.



00:25:29.130 --> 00:25:30.990

Ashwin Warrier: yeah, of course, like you said ml as well.



00:25:31.980 --> 00:25:40.440

Matthew Halliday: yeah it's interesting right because i'm sure, a lot of people on this webinar today are familiar with PL sequel right being an eds sharp right and seeing.



00:25:41.250 --> 00:25:46.800

Matthew Halliday: Businesses actually take what would have been PL sequel code and transform it and write it now into Python.



00:25:47.340 --> 00:25:54.090

Matthew Halliday: And so they're not doing data science that literally DEMO they've done in PL sequel but leveraging a spark platform to be able to do that.



00:25:54.720 --> 00:26:00.690

Matthew Halliday: has been pretty transformative and one one customer I remember telling us about doing fixed asset depreciation.



00:26:00.990 --> 00:26:06.990

Matthew Halliday: which traditionally would have been done using PL sequel in the Oracle database, but running bad inside of in quarter and then pushing the output back.



00:26:07.320 --> 00:26:16.500

Matthew Halliday: To Oracle through the EBS interface tables, so we see a lot of kind of very creative and interesting ways that people are leveraging that side of the technology and bringing it in.



00:26:18.000 --> 00:26:30.210

Matthew Halliday: let's there's some kind of circle on just one of the topic that we see is always very interesting so in quarter obviously you know we have a rich heritage we understand EBS amount of free see other applications like netsuite and SAP etc.



00:26:30.960 --> 00:26:34.770

Matthew Halliday: Service now and other products that we've worked very closely with with.



00:26:36.060 --> 00:26:43.950

Matthew Halliday: What, but one of the things that we've always seen as the value of in quarter becomes so much greater when other data sets are brought together.



00:26:44.940 --> 00:26:51.360

Matthew Halliday: When businesses are not looking at only their EBS data, but they say you know now I can combine my EBS data with.



00:26:51.900 --> 00:27:06.390

Matthew Halliday: And then pull in that maybe talk a little bit about what you've seen with not just EBS analytics but the magic when you bring EBS in conjunction with other products, what are the ones you've seen and and how's that been exciting for our customers.



00:27:09.690 --> 00:27:27.840

Mohit Saggi: yeah so I can I can take a stab on this and ashwin you can answer, so I think it does actually also relates back to you know what are the, what are the use cases where spark or Python could be could be useful outside of machine learning right so so Python and spark actually does provide.



00:27:29.370 --> 00:27:39.990

Mohit Saggi: Like a source agnostic you know capabilities, where you know whether your data is coming from Oracle or SAP you can actually write one common.



00:27:40.920 --> 00:27:58.650

Mohit Saggi: You know logic within spark and Python and that you can apply apply to both both data sources right instead of writing PL sequel for Oracle and then something else for SAP so so so given that it is, it is actually embedded or very tightly integrated with.



00:27:59.670 --> 00:28:09.840

Mohit Saggi: With an encoder data pipeline, it does provide that additional capability, where you can you can build once and then you know reuse it, you know for for different data sources.



00:28:11.010 --> 00:28:29.640

Mohit Saggi: But yeah, so I think magic going back to your original question, yes I think that's the that's the that's the reality that we live in right, I mean you know majority of our customers, if not all have multiple data sources and, and I think you know to be able to bring.



00:28:31.170 --> 00:28:43.050

Mohit Saggi: analytics across these data sources, you know if you if you look at the traditionally traditional way of doing that it's usually building a star schema and dimensional models and then you know.



00:28:44.310 --> 00:28:51.330

Mohit Saggi: Bringing bringing those data sets data sets together but, but I think one of the one of the key value adds that we see within code is where.



00:28:51.870 --> 00:29:03.540

Mohit Saggi: You know, you could you could do that without without having to you know one builder start schemas and then try to merge the stars stars schemas together right, because the fact of the matter is.



00:29:04.350 --> 00:29:07.500

Mohit Saggi: Some of these big complex earpiece systems like Oracle and SAP.



00:29:08.280 --> 00:29:18.840

Mohit Saggi: The data is quite different right there they travel structures are different, you know the the granularity of the data is different, even even if you're even if you're still you know talking about the same functions.



00:29:19.380 --> 00:29:30.870

Mohit Saggi: So so so within Cora it's it's very easy to sort of bring those data sets together, and I think that's that's the key value add that we have seen that the cloud of our customers right, even though they may start with one data source.



00:29:31.320 --> 00:29:42.210

Mohit Saggi: But but, but like very soon across all these deployments you know, we have seen customer you know go or move towards you know, having multiple data sources within the within the quarter.



00:29:44.040 --> 00:29:45.120

Matthew Halliday: awesome thanks for that.



00:29:45.150 --> 00:29:46.590

Ashwin Warrier: One thing one thing.



00:29:46.830 --> 00:29:48.090

Ashwin Warrier: We had Matthew is.



00:29:48.540 --> 00:29:51.300

Ashwin Warrier: More put up a great point I mean if you think about.



00:29:52.650 --> 00:29:58.110

Ashwin Warrier: I mean, even today, if you have Oracle on premise with EBS and moving to Oracle cloud.



00:29:58.890 --> 00:30:06.660

Ashwin Warrier: Oracle themselves don't provide a single solution that has bridges across both because it's such a hard problem to solve right if you follow the dimension model.



00:30:07.380 --> 00:30:14.700

Ashwin Warrier: And if you think about Oracle plus ASAP I mean that's a much bigger problem as boy mentioned the complexity just balloon so much more.



00:30:15.240 --> 00:30:24.300

Ashwin Warrier: And we have customers are starting to do this in really benefiting from using our approach which doesn't require these conformed dimension models that he had to fit them into.



00:30:24.780 --> 00:30:31.470

Ashwin Warrier: You can bring in different extracts from SAP from from Oracle E business suite and start to create these unions.



00:30:32.400 --> 00:30:44.520

Ashwin Warrier: with very little effort with an encoder right and start to quickly leverage that time to inside across two earpiece systems that large scale is something that, frankly, I would not have thought possible when even a few years ago.



00:30:45.240 --> 00:30:45.570




00:30:46.830 --> 00:30:58.020

Matthew Halliday: i've one more question before we jump into Patrick but I know it's the bottom of the hour and want to make sure that my head does not get stuck without a plane home so thanks for joining us my head.



00:30:58.110 --> 00:30:59.640

Mohit Saggi: appreciate your insight on.



00:31:00.150 --> 00:31:07.170

Matthew Halliday: Safe travels this one's for Patrick and also for ashwin and this one kind of comes from from the.



00:31:07.860 --> 00:31:19.710

Matthew Halliday: From our listeners here can you talk about what kind of training or efforts will be needed for an existing team, if you want to learn in quarter I think there's a be good for you to because both of you have come in and learning quarter.



00:31:20.850 --> 00:31:35.910

Matthew Halliday: muscle from ahead we're very early on in its journey so maybe talk about your own personal experience of how did it feel coming in, having been in this space being it professionals and understanding technology what was that what was that training and effort like for you or the experience.



00:31:41.220 --> 00:31:42.390

Ashwin Warrier: Patrick going to go first.



00:31:42.720 --> 00:31:44.250

Patrick Rafferty: yeah sharing go first so.



00:31:46.260 --> 00:32:03.030

Patrick Rafferty: You know I think a lot of the skills that that we build in technology organizations, when it comes to understanding data how databases work how they're structured and all completely map over into inquiry these concepts are.



00:32:04.470 --> 00:32:12.630

Patrick Rafferty: At the heart of our engine and the value that we provide where I think that we really accelerate things is.



00:32:12.990 --> 00:32:20.430

Patrick Rafferty: By up leveling that work and making things a lot easier there's a lot of things that you would do that you would look at it in a traditional database and say oh I.



00:32:20.850 --> 00:32:28.920

Patrick Rafferty: can't do that because, if I do that, then the core is going to run for an hour and I want to get a phone call from a guy who's monitoring the database saying what are you doing what are you trying to do.



00:32:29.370 --> 00:32:36.660

Patrick Rafferty: what's happened in quarter it's almost like that scene, towards the end of the indiana Jones and the last crusade movie where the guy jumps over the bridge says.



00:32:37.530 --> 00:32:42.120

Patrick Rafferty: What can I do this, can you guys go ahead and bring these two tables together and and.



00:32:42.600 --> 00:32:50.670

Patrick Rafferty: Stephen get that answer, and the answer comes back in two seconds you sort of take that leap, and all of a sudden you're standing on that bridge and you're able to do that.



00:32:51.330 --> 00:32:55.470

Patrick Rafferty: fire alarm didn't go off you maybe you want to go go around and get a cup of coffee before you even.



00:32:55.890 --> 00:33:08.850

Patrick Rafferty: got out of your chair the answers back and those types of things that you're trying to do today, they still happen in quarter, we just make it so much easier and allow you to skip so many steps in that process that.



00:33:09.540 --> 00:33:17.550

Patrick Rafferty: At the end of the day, the business doesn't doesn't necessarily benefit from finding to do nine steps before I can get to that final step, where I give the business, the answer.



00:33:18.120 --> 00:33:21.360

Patrick Rafferty: The business is going to look and go hey that took a month that took three weeks.



00:33:21.900 --> 00:33:30.330

Patrick Rafferty: But you being able to skip all those steps and become way more efficient in providing those answers and later down the line, enabling the business to actually get those answers themselves.



00:33:31.140 --> 00:33:43.410

Patrick Rafferty: it's a huge win, so the skills are absolutely the same and in court is designed to be no code low code easy to use, we want you to actually be able to do these things that you couldn't do before.



00:33:44.370 --> 00:33:58.140

Patrick Rafferty: without having to pick up the phone and call it without having to think and say oh God what's going to happen if I try to do this really make it easy and straightforward and and for advanced things like we mentioned machine learning and Ai spark and some of the other concepts.



00:33:59.910 --> 00:34:08.220

Patrick Rafferty: Earlier in the call we've got a tremendous training program for folks who work in technology and really want to go and say all right Well now, I met a.



00:34:08.700 --> 00:34:22.410

Patrick Rafferty: i'm at a level of of incorporate expertise in my organization is really seeing the value, how do I take it to an a plus how do I take it, even further, how do I use this platform to to stand even even higher in terms of the value that i'm able to bring the business.



00:34:24.420 --> 00:34:31.860

Matthew Halliday: that's a great point you raised actually there Patrick I am not an expert in dimensional modeling I never did that.



00:34:33.090 --> 00:34:41.790

Matthew Halliday: But what I found is, I was able to do things that traditionally I would have need to lean heavily on someone like ashwin who is an expert in that field.



00:34:42.450 --> 00:34:54.120

Matthew Halliday: But I understand databases right, I understand the structure, so I was able to take my knowledge of the applications and now be able to make my skill set be completely relevant for analytics which I think is super empowering and.



00:34:54.420 --> 00:34:57.570

Matthew Halliday: really can really changes and transforms what you're able to do.



00:34:59.190 --> 00:35:06.390

Matthew Halliday: Given that let's let's maybe shift over Patrick and let's show the platform and again if anyone has any questions.



00:35:06.780 --> 00:35:22.830

Matthew Halliday: Definitely free free to ask them put them in the chat ask them in the Q amp a something you'd like to see, we can, maybe see if we can show it to you right now, as part of the DEMO but I will keep monitoring that while Patrick jumps in and give us an overview of the actual encoder platform.



00:35:24.750 --> 00:35:25.830

Patrick Rafferty: All right, thanks Matthew.



00:35:31.140 --> 00:35:33.060

Patrick Rafferty: Let me know when you can see my screen.



00:35:43.080 --> 00:35:52.110

Matthew Halliday: always keep thinking that zoom should have a little feature that I can hit like the the elevator music and just have some music play for little interlude, we can see your screen.



00:35:52.770 --> 00:35:56.070

Patrick Rafferty: Alright sounds good thanks yeah or the jeopardy theme song either way I.



00:35:56.130 --> 00:35:56.670

guess it depends.



00:35:59.130 --> 00:36:10.860

Patrick Rafferty: So so right here, I know that some folks are back here again from some of the previous sessions and some of this might be brand new you know people i've never actually seen in quarter before, so what you're looking at right now.



00:36:11.340 --> 00:36:16.200

Patrick Rafferty: Is the quarter platform just my web browser open to one of our servers running up in the cloud.



00:36:16.680 --> 00:36:27.060

Patrick Rafferty: And this is a server I typically use when we want to talk to a technical audience because it's got a ton of data volume in it now all that data actually happens to come from EBS that's a really nice fit.



00:36:28.140 --> 00:36:33.390

Patrick Rafferty: For a session like this, and when we talk about the quarter platform we've talked a lot about.



00:36:34.980 --> 00:36:47.340

Patrick Rafferty: And we've touched on the old way of doing things Matthew mentioned dimensional modeling and and the thought processes and all the things that you kind of have to think about and consider and plan for when you're in a traditional.



00:36:49.170 --> 00:36:58.140

Patrick Rafferty: A traditional mode of dealing with analytics and data warehousing we're in quarter kind of comes in, is, we want to just kind of set you free and kind of unleash you from that.



00:36:58.890 --> 00:37:10.230

Patrick Rafferty: And unchained you from that process, and so in in quarter, we want you to be able to get your data onto the platform very quickly and very easily we want you to immediately realize that value time to insight.



00:37:10.890 --> 00:37:21.420

Patrick Rafferty: Time to that first win with the quarter platform, we want you to be able to continue to iterate continue to change not dread the change request or the additional.



00:37:23.070 --> 00:37:31.890

Patrick Rafferty: additional requests from the business to welcome that, and then eventually as your organization matures and and quarter starts to gain adoption.



00:37:32.940 --> 00:37:36.870

Patrick Rafferty: turn the business loose and set them free to actually do a lot of this themselves.



00:37:38.070 --> 00:37:42.720

Patrick Rafferty: they're there by kind of freeing up your technology resources for kind of their nirvana right the.



00:37:42.960 --> 00:37:49.920

Patrick Rafferty: The more high value use cases things that impact the top line of the business not you know, doing the things that are going to grow, the business.



00:37:50.100 --> 00:37:56.940

Patrick Rafferty: Not just doing the mundane kind of tasks that you need to kind of keep the business running keep the lights on day to day, so we talked about in quarter.



00:37:58.410 --> 00:38:06.870

Patrick Rafferty: Usually starts with bringing your data onto the platform and encoder comes with dozens and dozens hundreds of connectors, of which this is only a subset.



00:38:07.590 --> 00:38:17.640

Patrick Rafferty: To bring your data into the quarter possible doesn't matter if you're going to talk about EBS today, but it could be salesforce it could be SAP it could be netsuite it could be.



00:38:18.690 --> 00:38:29.550

Patrick Rafferty: machine data coming or log data coming into spunk any kind of data that is important to your business or that you think might actually have the ability to impact the business.



00:38:30.420 --> 00:38:35.820

Patrick Rafferty: If it weren't properly harnessed in quarter wants you to have that capability and to bring that data onto the platform.



00:38:36.840 --> 00:38:41.970

Patrick Rafferty: These are sort of just a number of data sources that we support once we've connected in quarter.



00:38:43.560 --> 00:38:57.150

Patrick Rafferty: To a data source that next step is to load it into what we call the schema so now a schema can basically be whatever you make it so if you have a large flat file a series of data fees that you pay in that are syndicated.



00:38:57.720 --> 00:39:04.890

Patrick Rafferty: You could load all those into a single schema or in the case of EBS a system that has thousands and thousands of tables in it.



00:39:05.430 --> 00:39:18.420

Patrick Rafferty: We choose to kind of break up those schemas into different functional areas common common tables that sort of span across all the different functions and modules of EBS and then specific.



00:39:19.350 --> 00:39:31.440

Patrick Rafferty: Specific schemas tied to things like accounts payable accounts receivable, and this is really where kind of the power of the Platform starts to come in in this environment right now i've got 3 billion rows.



00:39:32.220 --> 00:39:40.740

Patrick Rafferty: of accounts receivable 3.1 billion words and if you kind of if you have familiarity with EBS and an understanding of what that actually looks like.



00:39:41.040 --> 00:39:57.210

Patrick Rafferty: you'll recognize these tables, these are the real table names, you know hundred million payment schedules for example 442 million transaction lines in my accounts receivable the lines on the invoices that i'm responsible for collected quarters, bringing all that data into the platform.



00:39:58.590 --> 00:40:09.420

Patrick Rafferty: And not only that we're have the capability to present it to your users in a way that makes sense, a lot of technical people on the phone probably know these tables inside now or.



00:40:10.020 --> 00:40:13.320

Patrick Rafferty: Can pretty much fake it until they make it with some of them.



00:40:13.980 --> 00:40:20.430

Patrick Rafferty: But for the business they don't necessarily want to know the complexity kind of what we were talking about earlier they don't want to know the nine steps.



00:40:20.730 --> 00:40:27.330

Patrick Rafferty: That it took to get to the answer, they want to get to that answer as quickly as possible and that's really what in court is all about, and then enabling that.



00:40:28.980 --> 00:40:36.390

Patrick Rafferty: nibbling that experience for the end users so 3 billion rows of accounts receivable data we've got inventory data down here.



00:40:36.900 --> 00:40:44.430

Patrick Rafferty: From our supply chain DEMO we're going to dive a little bit into this as well you know tons and tons of rows here all of the key tables.



00:40:45.210 --> 00:41:03.570

Patrick Rafferty: Inside of Oracle EBS we have that data and we bring it into the platform and make it and structured in a way that's consumable for your end users, when I say consumable for your end users what i'm really talking about essentially is what we call this business scheme, the business schema.



00:41:04.860 --> 00:41:16.050

Patrick Rafferty: is where you can sort of abstract away the complexity of your underlying models where that data is actually coming from and present your users with curated sets of data.



00:41:17.880 --> 00:41:28.140

Patrick Rafferty: so that they can build reports dashboards insights and do the work that they need to do without having to call it, and the key benefit here of this business schema thing that I always make sure that.



00:41:28.770 --> 00:41:38.460

Patrick Rafferty: Technical audiences kind of walk away with is hey, this is not, this is not a materialized view, and this is not something that I, that I stand up and needs to be refreshed all the time.



00:41:39.060 --> 00:41:51.780

Patrick Rafferty: it's purely semantic so it abstracts away the complexity and i'm able to build in complex calculations, so that no matter who my end user is they're able to get the same answer.



00:41:53.520 --> 00:41:59.910

Patrick Rafferty: doesn't matter if they're sitting in San Mateo if they're sitting in New York City if they're sitting in Chicago they're operating over the same sets of data.



00:42:01.200 --> 00:42:06.600

Patrick Rafferty: And they're getting the same answer when they go and ask those questions of the data, so you kind of eliminate the Wild West.



00:42:07.560 --> 00:42:18.960

Patrick Rafferty: The Wild West Microsoft excel problem that so many organizations going to end up when, especially when it comes down to things like quarter and close month and close and inventory reordering windows.



00:42:21.510 --> 00:42:32.130

Patrick Rafferty: So that's kind of the backend right, and this is how we kind of load data into the platform, we make it available, what does this look like in real life, so to speak, what does this look like when.



00:42:33.600 --> 00:42:44.040

Patrick Rafferty: I let my users explore my data and i'm going to start here are on my supply chain data very similar to what we showed last week, where we kind of did a dig in into the supply chain so.



00:42:44.430 --> 00:42:49.260

Patrick Rafferty: What am I what am I users what am I, my folks are monitoring the supply chain really want to know right now.



00:42:49.740 --> 00:42:56.520

Patrick Rafferty: Well, given the the state of the world and the general chaos that our global supply chains are in by the number one thing they want to know is.



00:42:57.330 --> 00:43:01.800

Patrick Rafferty: What are the orders that I have booked and how am I going to fulfill you know, and this could be.



00:43:02.670 --> 00:43:10.800

Patrick Rafferty: A manufacturer who needs to assemble something in a factory and needs those raw materials and needs to understand what what raw materials they have on hand.



00:43:11.490 --> 00:43:22.350

Patrick Rafferty: It could be a distribution function within a large CP G company like say like PepsiCo or something like that, where i've got palettes and palettes and gatorade sitting in warehouses, I need to make sure they're getting out to the.



00:43:23.790 --> 00:43:30.060

Patrick Rafferty: You know, to the stores to 711 to the Convenience Stores to the bodegas here in New York, how do I efficiently manage that inventory.



00:43:30.330 --> 00:43:39.270

Patrick Rafferty: or it could be capacity we've got a really great customer down in South Carolina precision manufacturer where sure they need to make sure they have enough material on hand.



00:43:39.960 --> 00:43:51.120

Patrick Rafferty: To construct the materials that their customers are paying for but it's very precision process and so their biggest challenge is actually not just raw materials and finished goods its capacity.



00:43:51.750 --> 00:44:01.740

Patrick Rafferty: To actually have capacity machine hours and she manpower to produce this and so really that supply chain problem is something where depending on the kind of organization, you are.



00:44:02.700 --> 00:44:10.590

Patrick Rafferty: You might have to slice and dice this problem, a different way, so what we're looking at here right now is the open sales orders from now until the end of the year.



00:44:11.160 --> 00:44:23.010

Patrick Rafferty: For our for our supply chain organization and, as I scroll down here, I can see where i'm planning on sending those orders right so walmart target kroger safeway.



00:44:23.760 --> 00:44:30.630

Patrick Rafferty: How much those customers have ordered what those items are what am I actually trying to distribute and send out.



00:44:31.260 --> 00:44:38.490

Patrick Rafferty: To these particular customers and within quarter now because everything happens at the transaction level everything happens.



00:44:39.090 --> 00:44:49.470

Patrick Rafferty: at the lowest level of granularity and your data, not only to have a very powerful capability here, where I can drill in on anything I see on screen, I can click on.



00:44:49.950 --> 00:44:58.920

Patrick Rafferty: and start to navigate and see and diagnose problems and changes but it's also something that's very flexible so in in quarter we typically.



00:44:59.640 --> 00:45:15.660

Patrick Rafferty: What we say we do is we basically mirror the data from the system of record we bring over an entire table all the roles that you care about all the columns are available to you, and so, when a business user has a new request when they need to go find something else.



00:45:16.890 --> 00:45:20.220

Patrick Rafferty: The loop to close that's really small and quarters already got that data.



00:45:20.640 --> 00:45:29.580

Patrick Rafferty: A lot of times it's literally just to kind of continue that loop analogy it's pulling that data through and making it available and that goes across the board it's not just simple dimensional modeling.



00:45:30.210 --> 00:45:35.490

Patrick Rafferty: It could be cross metric and cross organization model again this is really the strength of in quarter.



00:45:35.850 --> 00:45:48.810

Patrick Rafferty: So here right now again just to remind you i'm looking at the open orders here, and I can see my top orders out of the national organization for the seven inch tablet Amazon fire, I can click on this and from here.



00:45:50.640 --> 00:45:54.300

Patrick Rafferty: be transported into my inventory, how much inventory do I have.



00:45:55.200 --> 00:46:03.450

Patrick Rafferty: In this nashville warehouse for the Amazon fire tablet right now, I can see i've got it on hand quantity of about 3000 to slightly different.



00:46:03.810 --> 00:46:13.170

Patrick Rafferty: Items here to slightly different models like a gen y and gen two, I can see that I can back this out again everything editable changeable everything is.



00:46:14.190 --> 00:46:19.710

Patrick Rafferty: is very easily customizable and and quarter, but the real power here the real thing that says hey.



00:46:20.400 --> 00:46:32.070

Patrick Rafferty: What happened to those nine steps, I had to do in the old way of doing things i'm going to click on the edit panel here and just kind of give you a window behind how we actually build out that inventory detail table that we were just looking at.



00:46:34.110 --> 00:46:41.160

Patrick Rafferty: And i'm gonna come over here and actually show you the query plan so I mentioned earlier they're sort of like that that leap that you take, where you go God I can't.



00:46:42.300 --> 00:46:50.730

Patrick Rafferty: In court as a new type of database I can't ask it to do 810 different table joins in a single query well here's what actually in court is doing right.



00:46:52.410 --> 00:47:01.050

Patrick Rafferty: So it's Taking all these tables and again if you're familiar with EBS you know these are, these are the real tables right, these are their names and and some of the key.



00:47:01.650 --> 00:47:11.220

Patrick Rafferty: The key objects and entities here and in court as running this query every time that page refreshes to show you that tape every single time.



00:47:12.240 --> 00:47:22.440

Patrick Rafferty: And you're not penalized for it as i'm clicking through you guys are seeing that data refresh across hundreds of millions billions of rows of data so as an IT professional.



00:47:23.070 --> 00:47:29.250

Patrick Rafferty: You kind of make that leap where you go okay wait a SEC i'm actually pretty comfortable with what's happening here i'm comfortable with the way in which you're able to.



00:47:31.050 --> 00:47:33.690

Patrick Rafferty: To traverse your data and to see all these different.



00:47:34.830 --> 00:47:37.770

Patrick Rafferty: These all these different objects and make changes and bring things in.



00:47:39.030 --> 00:47:45.450

Matthew Halliday: One of the interesting thing sure Patrick is you know if we look on the Left panel side right and you knew kind of scroll through.



00:47:45.810 --> 00:47:57.990

Matthew Halliday: All of those columns are available for you to be able to segment slice and segment those dashboards any way that you want, so any one of these columns if they happen to be in that source application.



00:47:58.620 --> 00:48:06.390

Matthew Halliday: You have access to them, so you can start to say Okay, I have this dashboard Oh, let me just filter it by this value traditionally that would need to be in your dimensional model.



00:48:06.960 --> 00:48:18.690

Matthew Halliday: But here, it in quarter you don't need to because it's just happens to be in one of those tables that are using the joins unknown and it's like you instantly get that capability to create as many segments, as you want.



00:48:19.170 --> 00:48:23.610

Matthew Halliday: to view the data from any vantage point that you want, which I think is pretty amazing.



00:48:24.660 --> 00:48:26.040

Patrick Rafferty: yeah exactly you don't have to.



00:48:26.850 --> 00:48:30.660

Patrick Rafferty: That conversation of okay tell me how you want to drill down the data doesn't need to happen.



00:48:30.870 --> 00:48:40.350

Patrick Rafferty: You can just go ahead and Sarah will give you everything give you those capabilities and quarter doesn't penalize you in court encourages that that dialogue or that conversation with the data.



00:48:41.640 --> 00:48:42.000

Patrick Rafferty: One of the.



00:48:42.240 --> 00:48:51.180

Patrick Rafferty: One of the things I want to kind of quickly call out here is, you know I click this query plan button down here at the bottom that kind of show you what's happening behind the scenes, but this is truly behind the scenes, this is.



00:48:52.740 --> 00:49:00.600

Patrick Rafferty: The it persons domain, if you will, over here on the Left i've actually up leveled the conversation with my data.



00:49:01.020 --> 00:49:07.440

Patrick Rafferty: i've got something I got a business chemical inventory item, so I, as an end user here don't have to understand.



00:49:08.220 --> 00:49:17.280

Patrick Rafferty: That certain columns come from HR all organization units and some come from mtl on hand quantity I don't have to deal with that as a business user.



00:49:17.910 --> 00:49:28.170

Patrick Rafferty: As an IT person I can ask I can I can I can parcel out these different data sets to the end user, so they can just come in here and see a view called item.



00:49:28.920 --> 00:49:36.810

Patrick Rafferty: Now if they really care where this field is coming from, they can click on the item type and see that it comes from mtl system items be.



00:49:37.140 --> 00:49:42.600

Patrick Rafferty: And a long description coming from mtl system items to they have that capability.



00:49:43.080 --> 00:49:51.930

Patrick Rafferty: But really what you want to do is you don't want them to be in that business you want them to just go and say you know what I really need the primary unit of measure on this table, because I don't see it here drag it in.



00:49:53.370 --> 00:50:05.580

Patrick Rafferty: click save and i'm done and so eventually where you're going is you start with this incredibly powerful platform with all this great reporting and analytics to help you run the business and then.



00:50:06.210 --> 00:50:14.130

Patrick Rafferty: you've also got this capability of saying for certain users for all my users or maybe we're going to wait a couple months, but whatever kind of works best for your organization.



00:50:14.580 --> 00:50:21.360

Patrick Rafferty: I want to get out of the business of hey Patrick I need primary unit of measure on this table how long is it going to take you.



00:50:21.900 --> 00:50:30.510

Patrick Rafferty: You can go ahead and give them these capabilities curated data sets that you didn't have to build all you do is just drag them onto the page and you're good to go.



00:50:31.290 --> 00:50:42.510

Patrick Rafferty: And that's dimensional model but also keep in mind, it goes across the board, this capability transcends fact and dimensional modeling and even extends out to things like.



00:50:42.960 --> 00:50:50.070

Patrick Rafferty: I want to actually build metrics I want to go ahead and say all my open sales orders, I want to see all the stuff from inventory on the same page.



00:50:50.970 --> 00:51:03.120

Patrick Rafferty: I don't want to just be limited to my open orders and have to kind of go back and forth and tab and go I can come in here to see my top 50 warehouse item combinations click the edit button.



00:51:04.770 --> 00:51:09.150

Patrick Rafferty: And now I want to see my on hand quantity, and I can look through here.



00:51:10.200 --> 00:51:16.920

Patrick Rafferty: I can just type the word hand, for example, and see that I have an on hand quantity field drag that in.



00:51:18.150 --> 00:51:24.810

Patrick Rafferty: Again, very easy low code no code is really kind of where where where we're going with this right, you know, we want to.



00:51:26.250 --> 00:51:33.570

Patrick Rafferty: We want to understand we'll have a conversation, where the data, not necessarily having to understand all the underlying complexity.



00:51:33.990 --> 00:51:44.100

Patrick Rafferty: But one thing I do want to kind of highlight here as a tech people like looking under the hood keep saying the word under the hood and get behind the scenes and getting in there, I can click this button up here that's mark sequel.



00:51:46.230 --> 00:51:57.840

Patrick Rafferty: And if I want to know exactly what in quarters doing here how exactly did you get here well here's 155 lines of sequel that made this happen that's painting This insight here.



00:51:58.500 --> 00:52:03.960

Patrick Rafferty: With all of the richness and all the the calculations and everything that needs to happen here.



00:52:04.410 --> 00:52:11.340

Patrick Rafferty: This is all the stuff that's happening off of those raw tips if I so choose I probably don't if i'm the business user i'm probably thrilled.



00:52:11.640 --> 00:52:18.870

Patrick Rafferty: That I never have to think about how all this stuff is kind of coming together, I want to be able to do these things, and then again as your capabilities grow.



00:52:19.890 --> 00:52:33.540

Patrick Rafferty: Your use of the Platform your adoption increases you able to get deeper and deeper into the platform where, then you can start letting users, create their own calculations don't want to go ahead and say, well, show me my shortages show me this show me what I have on hand.



00:52:34.890 --> 00:52:35.820

Patrick Rafferty: let's add that up.



00:52:37.020 --> 00:52:42.180

Patrick Rafferty: Just like you would an excel let's say where you write an excel formula, I can sum up my on hand inventory quantity.



00:52:43.950 --> 00:52:45.630

Patrick Rafferty: And then maybe I want to subtract from that.



00:52:46.890 --> 00:52:47.640

Patrick Rafferty: My order corner.



00:52:49.080 --> 00:52:56.490

Patrick Rafferty: Type the word some i'm just going to Double Click on this, then adds it right there and the formula I click validate and save.



00:52:58.560 --> 00:53:03.240

Patrick Rafferty: And then I click save again I can go right back into that view that we were just looking at and now.



00:53:04.500 --> 00:53:10.380

Patrick Rafferty: i've got my opening orders and how much I have i've got my on hand quantity, how much do I have in each of these.



00:53:10.980 --> 00:53:17.820

Patrick Rafferty: In each of these warehouses, and then have this for that basically says hey how much Am I short, you know, and I can come in here now and Sarah will.



00:53:18.330 --> 00:53:25.980

Patrick Rafferty: Show me these items, you know these top items that have down here in the bottom, the orders by items navigate through and immediately see my.



00:53:26.850 --> 00:53:34.950

Patrick Rafferty: Changes my all my insights paint and change over time and I actually see there's a shortage here that can do all kinds of visual treatments that in quarter like.



00:53:35.490 --> 00:53:41.820

Patrick Rafferty: format that and read you know when you see that there's a shortage and things of that nature but incredibly powerful to just say hey.



00:53:42.930 --> 00:53:51.450

Patrick Rafferty: Just do it you've got all the data, you need all the raw materials, you need to construct this incredibly powerful business business Center dashboard.



00:53:53.130 --> 00:54:03.450

Patrick Rafferty: Go start building and see how efficient, you are and and to Matthews point earlier compare that with the old way and all the steps you might have to take to build some kind of fact effect comparison.



00:54:04.080 --> 00:54:12.030

Patrick Rafferty: In another tool, or you know you know, in a traditional data warehouse it's it's mind blowing how much how much more quickly, and you can you can execute.



00:54:12.900 --> 00:54:22.140

Matthew Halliday: yeah and I love that you showed the sequel equivalence there of that query and if you were to copy and paste that and you let's say you replicated all of these tables into.



00:54:22.920 --> 00:54:29.340

Matthew Halliday: A cloud data warehouse provider and he said okay great and then you ran that sequel query that sequel query would would run.



00:54:30.060 --> 00:54:35.550

Matthew Halliday: It might not come back or you'll probably come back, but it will come back after you spend probably a lot of money, paying for that query to run.



00:54:35.850 --> 00:54:44.310

Matthew Halliday: And it probably take minimum of a few hours to come back and we're seeing those live coming back this isn't you know recorded this is, you know, a live DEMO.



00:54:44.730 --> 00:54:47.550

Matthew Halliday: And you've seen them come back in you know if, in a few seconds.



00:54:47.910 --> 00:55:00.090

Matthew Halliday: Which is truly remarkable of that kind of volume, because anyone who's worked with data volume at this size knows that you don't get this stuff for free seeing this level of performance, without having to go through those hoops and jumps.



00:55:00.960 --> 00:55:08.040

Matthew Halliday: is generally just never seen so that's awesome Thank you so much, Patrick for great DEMO appreciate that.



00:55:08.880 --> 00:55:18.120

Matthew Halliday: Again, if you have any questions about what you just seen in the DEMO feel free to put that in the chat and or in the Q amp a and i'll kick it off with a question here.



00:55:19.050 --> 00:55:27.690

Matthew Halliday: Deployment options within quarter, can they deploy this in the cloud or can I install on premises either one of you like, to take that one.



00:55:30.810 --> 00:55:38.430

Patrick Rafferty: yeah sure it's it's up to you, so you know we work equally well on premise or in the cloud and we understand that organizations are different.



00:55:39.240 --> 00:55:50.160

Patrick Rafferty: different stages in their cloud journey and we want to be able to enable them, I would see our customers in 95% of the cases, you know in quarter files, the data if you're still on Prem.



00:55:51.270 --> 00:56:02.250

Patrick Rafferty: You know a lot of times we're running on Prem but you also can use in quarter as part of your cloud migration strategy it's like hey i'm running this stuff on Prem and I know I need to go to the cloud at some point running court in the cloud using quarter to actually have that.



00:56:03.420 --> 00:56:18.660

Patrick Rafferty: You know, have that data movement up to your cloud up to to Microsoft azure, for example, or Google cloud platform and use that as part of your strategy, but yeah we're completely agnostic from From that standpoint, in court as a service or in quarter on Prem that isn't.



00:56:19.950 --> 00:56:27.090

Matthew Halliday: Another question here is you showed in quarter visualizations do you support other tools, if so, which ones you support.



00:56:28.170 --> 00:56:28.410

Matthew Halliday: yep.



00:56:28.440 --> 00:56:37.500

Patrick Rafferty: So the most common ones, I would say our our power bi and tablo but essentially anything that you have a visualization perspective.



00:56:38.310 --> 00:56:48.240

Patrick Rafferty: or from a query execution, even just a sequel client if it can speak to a postgres database, it can speak to in court, and we hold out those business views that we talked about earlier.



00:56:48.780 --> 00:57:00.000

Patrick Rafferty: As that interface layer into your data to make things super easy and super seamless for those users who prefer to be working in those other tools Microsoft excel also we have a Microsoft excel add and.



00:57:01.080 --> 00:57:09.060

Patrick Rafferty: that's part of the quarter product that finance users love they spend all their time and Microsoft excel they're huge fans of of interacting with the data and that way.



00:57:10.770 --> 00:57:18.000

Matthew Halliday: It seems that you mentioned that in quarter was ingesting the data, how is that data stored and is that a.



00:57:18.150 --> 00:57:20.310

Matthew Halliday: proprietary format for encoder.



00:57:21.630 --> 00:57:30.150

Patrick Rafferty: Sure, so the data is not in a proprietary format in any way we persist data in our platform in a format called Apache parquet.



00:57:30.600 --> 00:57:37.230

Patrick Rafferty: So if you're familiar okay great if not essentially all you need to know is it's Open Source standards based and lots of tools.



00:57:37.740 --> 00:57:52.740

Patrick Rafferty: Data bricks beta robot Apache spark Apache hive all read and write and understand parquet so when you bring that data into in quarter there's no lock in and actually can use other tools to interrogate and and build insights on top of the data that in quarter has acquired.



00:57:54.840 --> 00:58:05.430

Matthew Halliday: final question I have here is you mentioned doing intraday refreshes of data, how does encoder handle incremental updates.



00:58:06.990 --> 00:58:10.110

Patrick Rafferty: Sure, so really well you know everything getting quarter is.



00:58:10.860 --> 00:58:20.640

Patrick Rafferty: Typically, done in absurd fashion so brand new records modified records and we update those incremental and what's really cool about in quarter from a speed perspective about intraday.



00:58:21.480 --> 00:58:27.180

Patrick Rafferty: there's so many organizations still there, gain their data once a day and for some organizations that's fine for.



00:58:27.570 --> 00:58:37.050

Patrick Rafferty: publicly traded companies for companies want to move very, very quickly at quarter end it's kind of unacceptable and within quarter because.



00:58:37.410 --> 00:58:45.270

Patrick Rafferty: Our technology allows you to go across that raw data there's no aggregates or cubes or reflections to update from an IT perspective.



00:58:45.540 --> 00:58:54.090

Patrick Rafferty: You get the new data, the new half a million records the new 5 million records and there are available in the system, your users are seeing them right away, and we have customers who have.



00:58:54.630 --> 00:58:59.610

Patrick Rafferty: done refreshes every one minute every four minutes every 15 minutes and it completely changes the game.



00:59:00.090 --> 00:59:13.200

Patrick Rafferty: When you have those business cases when you have those needs were fresh data at month and close frustrated at quarter and closes is a critical component to not bring out your staff and keeping your finance people there all night trying to close the books.



00:59:15.300 --> 00:59:16.350

Ashwin Warrier: Writing find me at.



00:59:16.800 --> 00:59:24.900

Ashwin Warrier: That really quick is I mean to better explain the duplication, is always a huge challenge right and we do that really well.



00:59:25.440 --> 00:59:31.890

Ashwin Warrier: The ability to duplicate on top of upsets and be able to create that unique set of records that you can then query on.



00:59:32.550 --> 00:59:43.200

Ashwin Warrier: Almost a swap as soon as a load is done is is one of the core value propositions of encoder so you cannot diminish the fact that these incremental refreshes are on massive volumes of data.



00:59:43.710 --> 00:59:54.660

Ashwin Warrier: And it's not like we're doing just a small silo of operational reporting and doing incremental like micro to it's your entire data set being D duped and presented to users X came.



00:59:56.100 --> 01:00:04.140

Matthew Halliday: Through Thank you, thank you, Patrick Thank you Ashley and also thanks again to my head, thank you for joining us today.



01:00:04.740 --> 01:00:12.870

Matthew Halliday: For a lot of you are jumping into your next meeting, so thank you for giving us an hour of your time we hope it was useful the recordings will be sent out those will be available.



01:00:13.110 --> 01:00:26.280

Matthew Halliday: Just a final push just a reminder, there is that in October sprint where we will provide access to in quarter and we can showcase this to you on your data, so you can get to experience what Patrick was showing on your life production data.



01:00:26.790 --> 01:00:31.530

Matthew Halliday: You might be well be skeptical that you say yeah i've never seen a slow analytics DEMO.



01:00:32.610 --> 01:00:45.240

Matthew Halliday: And that is probably true, but in reality we try to be very transparent and what we've built and, in reality, what you will see in your platform is exactly the same pieces of code like it's the same objects that Patrick was showing we will show that.



01:00:45.600 --> 01:00:55.650

Matthew Halliday: On your data so we're glad to do that and to kind of keep give that capability, so please reach out to us the recordings are available on in quarter.com slash resources.



01:00:56.190 --> 01:01:02.670

Matthew Halliday: So make yourself available of those, thank you for those of you who joined every one of these four webinars I think you deserve an award.



01:01:03.240 --> 01:01:15.510

Matthew Halliday: reach out to us, you should get a prize for putting up with myself for four weeks, but again I just want to thank you for joining and for everyone being here today and hope you all have a wonderful rest of your day, thank you very much.



01:01:15.990 --> 01:01:18.030

Patrick Rafferty: And Matthew there's one more question just popped up.



01:01:18.240 --> 01:01:18.690

Just as.



01:01:19.740 --> 01:01:21.540

Patrick Rafferty: Just as you were wrapping up there.



01:01:23.100 --> 01:01:36.270

Matthew Halliday: Speaking of the excel add in is there a project in place to make specific dashboards or schemas for excel I didn't have a separate category to have ease of search and excel sounds like this is actually an encoder user so welcome glad that you've actually probably seen the product.



01:01:37.470 --> 01:01:44.190

Matthew Halliday: yeah we are actually making improvements to our excel at and we're making a lot of investments over the next few months.



01:01:44.580 --> 01:01:47.520

Matthew Halliday: That you'll see I mean it actually is going through a complete overhaul.



01:01:47.970 --> 01:01:58.890

Matthew Halliday: That is one area that we are going to be looking at how the integration between the excel add in and our business schemas we have that as a project plan as well that we're going to be revamping the way we do some of the business schemas.



01:01:59.520 --> 01:02:02.880

Matthew Halliday: To make it a way that business users will actually start in that layer



01:02:03.210 --> 01:02:11.370

Matthew Halliday: And then they would be able to say, let me explore this data directly in excel click on the button there and it would invoke the excel Adam within excel and they'd be able to engage with them.



01:02:11.730 --> 01:02:17.610

Matthew Halliday: So we have some exciting things in place there to kind of make that user experience a little easier for people because.



01:02:18.000 --> 01:02:24.300

Matthew Halliday: As Patrick will show, and you can have lots of business schemas available Patrick we're showing you know, a huge amount there but.



01:02:24.720 --> 01:02:35.610

Matthew Halliday: If you are just in finance, you might have you know 10 or 15 of those i'm still you know you might want to make it a little easier to navigate through those in your excel add in, but we will be making.



01:02:36.120 --> 01:02:45.900

Matthew Halliday: Changes there so thanks for the question thanks for taking a look, and if you have any further questions again do not hesitate to reach out to us and, again, thank you for joining today.



01:02:47.400 --> 01:02:48.810

Matthew Halliday: Have a good one, everyone thanks.