Unprecedented and soaring demand for immediate access to financial and operational data – as well as the ability to analyze and act upon it quickly and with confidence – is forcing IT teams to reimagine their tech stack for financial analytics.
Finance tools such as BlackLine and Adaptive Workday Planning are critical to finance functions for accounting, planning and forecasting. With Incorta’s Analytics Data Hub for Finance, IT teams can now easily and quickly implement a fully integrated solution and provide unrivaled access to detailed transactional data at record-breaking speed.
In this webinar and virtual hands-on lab, you learn how Incorta can help you:
- Significantly shrink down your financial close cycle with immediate and unmatched access to the latest data for BlackLine
- Raise the level of trust and confidence in your data and deliver budgets and forecasts with precision and accuracy
- Quickly and easily connect Oracle, SAP and other complex data sources to financial and operational data destinations with complete control over data access and governance
- Rapidly deploy new finance dashboards and reports with pre-built data applications for a wide range of finance use cases
- Get a first hand experience with a virtual lab on connecting Oracle EBS data to BlackLine for accounting and reconciliation
Ardeshir Ghanbarzadeh: hello, everyone! Thank you. Uh, thank you for joining us today. Um, welcome to our Webinar on a supercharging work day and black client for financial analytics. We've all business data. My name is I'm. Director of product marketing at in quarter. I'll be your moderator today and speak to you a little bit about um our uh finance, That data. Hub
Ardeshir Ghanbarzadeh: joining me today is uh Mike Nader.
Ardeshir Ghanbarzadeh: Mike is the vp of business analytics here at in Incorta. He is a highly experienced solution. Architect, uh a consultant and educator. He's been working on data analytics for enterprise for over twenty five years in areas of uh performance management. Um, he is a former global domain lead for analytics. But uh oracle.
Ardeshir Ghanbarzadeh: He's also worked with strategic global clients on enterprise uh analytics, strategies and deployments. Um, He has worked on Uh, no! A numerous data, visualizations, tool analytics, engines, and relational problems. And we are very fortunate to have him here today to um go into the technical um, and and the business value of uh in quota uh with you.
Ardeshir Ghanbarzadeh: Uh, once again, If you have any questions, please do type them into the chat uh, and our team is going to uh do their best to answer those questions for you.
Ardeshir Ghanbarzadeh: What we're going to cover today, Um!
Ardeshir Ghanbarzadeh: Is some of the challenges in the office of finance will be the first uh thing we're going to tackle, followed by the analytics data help for finance and and what that concept uh brings to the uh use cases around the office of finance. We'll have a uh a product demonstration a little bit later on. And then for those of you who are participating in the virtual hands on lab. If you have not already signed up for in quarter's, free trial instance. Uh, you'll need to do that. Um! And uh, you'll need to do that before the uh actual hands on live begin, so you can follow along
Ardeshir Ghanbarzadeh: um with the with the uh lab itself. You can do that by going to inordinatecom and registering on the homepage um to uh to get your access set up uh uh on the free trial system for the for the virtual hands on lab
Ardeshir Ghanbarzadeh: uh, at the very end of the uh uh, the session, If you have some time left, and some additional questions. We will open that up to Q. A. So again, please do feel free to put your questions into the chat box, and uh, let's go ahead and get started. Mike. I'm gonna hand it over to you to talk a little bit about the different problems that are existing right now in the um in the office of finance, and some of the needs uh that are there. And and you can. You can step uh our folks through that.
Mike Nader: Absolutely. Thank you so much. Can you hear me? Okay, Just make sure I'm coming through uh, not on mute or anything. Here, you just one wonderful. So you know, as artist just said, we're gonna walk through today. Um, Not only essentially why we're having this conversation, which is really what I wanted to start with.
Mike Nader: But then allow you to play with technology a little bit, those you we're gonna stick around for the the hands on portion. So again. Thank you for your your time today. What really drove our background, or really our focus in this area was
Mike Nader: these kind of questions. And this kind of. You know these kind of needs that we'd run into over the last few decades, and that you know my experience, experience, my colleagues in
Mike Nader: the the data space. But specifically the data space around finance and accounting, and what it takes to enable those teams to run those processes at the end of the month for close consolidation, but also on an ongoing basis. But a close consolidation, as well as the the Fp. And analysis piece
Mike Nader: is that we we get a lot of different needs. Whether that is most often excuse me formed in
Mike Nader: questions, you know somebody will ask you about. I need to to see an updated forecast, and I need that
Mike Nader: now. I need it in forty-eight hours, so that gotta help out with a qpr presentation. I I need to track down and and out of balance on the trial, the trial balance side. I need to understand where we're sitting from an estimates and perceivables, and I also need that in the next two days.
Mike Nader: Then you've got the person sitting over on the right,
Mike Nader: often played by myself and and other folks in in the space who were are starting to think through. Okay? Well, you just ask me for data from three different systems. Um. We may or may not have that pulled together. It may or may not align to the dimensionality or need that you have, you know, he would have had previously.
Mike Nader: So we're gonna have to do some gymnastics to pull this together and let's figure out how to get this done. And this kind of scenario plays out just constantly
Mike Nader: every organization I've worked with over the last twenty years
Mike Nader: in quota, and the way the engine operates is is really the first platform. I work
Mike Nader: same time make it scalable and effective from a data perspective. To put that data together quickly, and to provide the right access to answer the
Mike Nader: the agile on demand. Really, the unknown question, as it comes in
Mike Nader: so. Or if you go to the next slide,
Ardeshir Ghanbarzadeh: Yeah, and i'll let you just speak to this just a little bit. But it it. It really goes back to what I was just talking through. It's only those yeah, in fact, what Mike was talking about the gymnastics that you really have to go through uh in a lot of these scenarios really comes down to um a a uh a gap between what the finance teams really require of uh of the data and the business to be able to um go through their day to day operations, whether it's financial, close, or Fp. And a uh for planning and budgeting and forecast
Ardeshir Ghanbarzadeh: thing between between those needs and between what the today's business technologies are offering. There's a serious gap, for example,
Ardeshir Ghanbarzadeh: and the finance teams really need uh this holistic view of financial and operational data in order to be able to do what they do on a daily basis. Their their regular financial operations on a daily basis. Uh, they need to be able to do real time analysis, So they need the latest data, and they have to have confidence that that data is free of errors and current.
Ardeshir Ghanbarzadeh: Um, They need to have the ability to drill into that data in practically any direction they want um, and be able to investigate anomalies and identify the root cause um uh root causes of discrepancies or variances that they see in their uh top line um kpis and metrics. Um! They also need to be able to answer some new questions like Mike said, a a new business question pops up that doesn't mean that you already have a data set or a data model to be able to answer that. So you need to be able to uh investigate
Ardeshir Ghanbarzadeh: mit
Ardeshir Ghanbarzadeh: um also there the you know, most of these uh finance teams have already made investments in, and tools that they use for their um, you know, uh finance workflows, so they It would be ideal for them to get a more value out of the capabilities that these tools have. Uh, by uh by elevating and up leveling the um the access to data that that they could provide. Um uh using using a solution that can deliver all of that data instead of just a subset or or top line aggregates.
Ardeshir Ghanbarzadeh: Uh. But unfortunately, what they're stuck with from the existing erp solutions, and even some uh, you know, some ad hoc finance tools is the data sits in these multiple erps or business applications, or databases, or dele, or houses or spreadsheets even, and you know, a lot of times they have to be stitched together manually. Um. The So the Etl process that is involved in extracting data from a source can be quite time consuming, depending on how much data is being pulled, and how often it's being pulled. So data, latency and uh
Ardeshir Ghanbarzadeh: mit ctl and and data integrity. Um also become an an issue uh for a lot of the finance things. We need to be able to provide quick answers to to critical questions. One hundred and fifty
Ardeshir Ghanbarzadeh: um often. That's also limited by just getting some aggregation or top one. Kpi: that's not going to uh be enough for them to do a deep dive analysis and provide that proper commentary to the business in order to be able to. Um, you know, guide a decision making and and do that in the trusted manner
Ardeshir Ghanbarzadeh: it could sometimes take uh weeks to months. Um, you sometimes in over a year to stand up a new bi project, if it involves a full on full scale. Implementation. Um! That could be very time consuming and very expensive. Um! And and those are really at some point become business, prohibitive, or really hamper um. How much efficiency and productivity financing can get um out of the tools that they have, and how well they can answer critical business questions for decision making
Ardeshir Ghanbarzadeh: with the data that the organization actually has in its hands.
Ardeshir Ghanbarzadeh: So going to the uh next slide uh this is where the analytics data hu for finance can really help. Um. And if you kind of take a look at the uh, the data Hub concept here you have your popular business forces on the left which include um, you know, such as oracle and sap, or business applications like such a salesforce. Um! And they are. They have tons of data. Obviously. Uh, you know, stored in those data sources. Um, What in in quarter can do, And the analytics data
Ardeshir Ghanbarzadeh: for finance can do is actually uh, bring one hundred of that data. Now, we're not talking about a subset or uh, or just aggregations. Um, you know, from from the source to one hundred percent of the data that's in the source. Bring that quickly into in quota um, and use our direct data mapping technology to
Ardeshir Ghanbarzadeh: uh to quickly uh connect uh all those tables and uh together uh, from the different sources, even in a multi-source environment bring data together and relate that data to each other. Um! And we'll talk a little bit about data apps which you see there at the very top and the center of the diagram. Um, we'll talk a little bit more about that. But using data apps, then they can um. The finance teams and finance it. Teams can really quickly deliver um the data that the finance teams need
Ardeshir Ghanbarzadeh: um to the either downstream applications uh or in quarter's own dashboards and reportings. Um, You can also deliver those to data discovery tools such as tableau and power, Bi um, or even uh existing finance solutions uh such as black line that is used for account reconciliation and close. Um. So the idea here is to be able to pull the data from multiple sources. Bring one hundred percent of that data. Um, in a very uh speedy fashion,
Ardeshir Ghanbarzadeh: and and deliver it to the very uh uh applications that finance teams use on a daily basis. Um, without cutting out um any kind of uh data, fidelity or visibility to transactional level details uh which becomes pretty critical when it comes to doing a deep dive analysis. You want to be able to drill all the way down to a transactional record and understand exactly why you're seeing a discrepancies or variances and have an explanation for that uh, that has already been investigated and true.
Ardeshir Ghanbarzadeh: So let's uh, let's dig into this a little bit more around the the finance data, Hub, and you know. So what what? Typically, we see the problem in the way things are architected. Uh: when it comes to delivering data to the office of finance uh, from the existing business solutions is that you have your data sources that we talked about, and then that data is often moved into some kind of a data Lake Um in a in a raw data business zone, for example, as noted in this illustration,
Ardeshir Ghanbarzadeh: here, you have one hundred percent of the data that was in the source already. Um. But as you move through refining that data um, and making it available to the end application and the end, user as you hop through this transformations from Data Lake to data warehouse. The data, mark you. At the end of the day you end up with only about ten of the original data that is business ready, and can be used for analysis. So that's a lot of data loss. And there's a lot of information lost in that data that it really pro
Ardeshir Ghanbarzadeh: um that level of analysis that sometimes uh folks need to do in order to be able to um have the amount, the the the confidence in the and the data to be able to drive a decision. They don't it's not confident in the answer that they're getting from the data. It's not going to make it very easy to to rely on it for decision making. So you end up going with, you know, good instinct or or some other mechanism to uh to justify the decision itself. And that's where really in quarter comes into change. I'll change all of that. And
Ardeshir Ghanbarzadeh: the way we got the way we do this is by bringing all of that data into into into the Uh uh
Ardeshir Ghanbarzadeh: into quota from the original sources. Um, you know, maintaining that data there, and I'm using data apps to um to to deliver that bring the business views. Bring the lot physical schemas, Um. And even the visualizations and dashboards uh all the way to the uh to the end users for analysis, for real time, analysis of all of that data um and and
Ardeshir Ghanbarzadeh: doing uh to do this in quarter employees, or what we call data apps and data access what something? We're gonna talk a little bit more. And you're gonna see some of that today in the in the demonstration and the virtual hands on lab that we have. Uh, but the data apps are
Ardeshir Ghanbarzadeh: essentially um pre built um packaged up uh um physical schemas, business views and and visualizations and dashboard that are specific to used cases and specific to data sources. So,
Ardeshir Ghanbarzadeh: for example, if you see on the screen right now, you can see that we have a a number of different use cases identified uh in the center as functional areas uh where, uh, you know, finance teams and our personal teams. Um are spending day to day managing uh the uh, the the inventory, managing the accounts, payables, and receivables. Um and uh going through each individual use case with the data that's available in the business, and some of the finance tools that they have right now operationally.
Ardeshir Ghanbarzadeh: Um. So with the combination of these pre-built schemas and business views and dashboards, they are able to really accelerate how quickly they can get that data. Make sure that data is being delivered in a current fashion, and it's not just so much about the breath of the application, but also the depth of the functionality. Um that spans across these business processes. Um, you know, including like payables and receivables and order management, and it's supporting the delivery of those um analytics data
Ardeshir Ghanbarzadeh: into these systems that are downstream, like the black lines and synapse analytics. And even some of the again the data discovery applications that are out there.
Ardeshir Ghanbarzadeh: Um. So what's the benefits here to? Uh to using the data Apps: Well, uh for beginners. Uh, you know the for enterprise, data or House initiatives, for example, with oracle it's about ten times faster uh to reach production. Um, using in quarter data applications. This really cuts short, I mean by months or even potentially years. Um. The amount of data prep work that is needed to um to around normalizing or on staging or modeling
Ardeshir Ghanbarzadeh: and optimizing uh a data engineer pipeline that is used
Ardeshir Ghanbarzadeh: for solutions because you have already um vetted This with this vetted the the process with pre-built data apps that right? Now, we have hundreds of customers using.
Ardeshir Ghanbarzadeh: Uh Second, you're simplifying and really streamlining the data pipeline from um the erp source um. We're making this migration considerably cheaper, because we're reducing the need to rebuild thetl scripts and star schemas. Uh, and all of those uh legacy aspects so that translates to huge cost savings. And finally, um uh putting high performance self service analytics in the hands of the business user means a lower cost, faster times to answers um less tickets into it
Ardeshir Ghanbarzadeh: and support for new data and and delivery of of new data sets. So you can shift some of your resources to higher value work initiatives.
Ardeshir Ghanbarzadeh: So before before we got a little bit further, I just wanted to um bring back uh the analytics data. Hub: We recently Um made an announcement of some of the new changes and new additions that we've made in terms of capabilities to uh, to the analytics data help for finance. So um, recently we have uh added the the new data destinations uh, so that there is a direct integration with um with new desktop data, destinations and finance tools that uh, fortunately off the final
Ardeshir Ghanbarzadeh: may already be using, and now they can get actually more out of it uh an integration uh with black line for finance. Um, uh financial, close and reconciliation, for example, is one. We've also um as part of our uh community uh have uh introduced a marketplace where uh
Ardeshir Ghanbarzadeh: our partners, and uh, to certain extent, even our our our customers, can build their own data apps and share that with the inquiry community. Um. And so that other customers and partners can actually take advantage of the the benefits that I just discussed in a previous slide that benefits that data absolutely bring um to uh to overall supporting uh the used cases and and sources and destinations. Um, that finance teams uh are uh working on today, um, and then finally, with the excel uh being a very,
Ardeshir Ghanbarzadeh: a very popular tool for uh for analyzing data and also building visualizations and tables. Uh, we've included an integration of excel queue. Um, This is going to provide really function out, not only uh excel functionality, but really up levels, excel into being able to provide um highly detailed, Very powerful visualizations right within excel that are not native to excel itself. Uh, but with excel cube you are able to have that additional capability right within the the encoder works
Ardeshir Ghanbarzadeh: Uh, Mike, i'm not sure if You' anything you wanted to add at this point. Um! There was a couple of customer stories I wanted to go through.
Ardeshir Ghanbarzadeh: Now you're muted. But
Mike Nader: nope, all good. Yeah, and i'll and i'll pick back up and talk a little more about
Mike Nader: not only the current data destinations provide an example of that, but also talk about way in corda operates, and what you can do today with a data destination. But what you can do today with just deployments of in quota, and how we're. We're leveraging a number of customers. But go ahead, please are to share.
Ardeshir Ghanbarzadeh: Yeah, sure. So I just wanted to kind of list uh, you know. Step through a little bit. Um, a couple of customer stories, a couple of customers of ours that have seen uh incredible success uh with the employee uh deploying in quota to really support finance use cases uh one of them is a comcast, obviously very well known uh media company. Um, Their particular implementation um spanned across a couple of thousand tables of data
Ardeshir Ghanbarzadeh: uh some some nearly two thousand uh unique users, uh, and and close to one thousand five hundred different uh reports. Um! That they were uh they were using a utilizing on a daily basis, using their Ov Ii system. Now, what they were able to do here, and the kind of the benefits that they saw from operational efficiency and data low uh data load wins. Um! When times uh was that they were actually able to really redo significantly. Reduce that um uh time it takes to develop new.
Ardeshir Ghanbarzadeh: down to a single day. The availability of data.
Ardeshir Ghanbarzadeh: Uh, this is essentially refreshing. The data and getting the latest data available made available to the end users when from once a day to every fifteen minutes, which is four times an hour, obviously. And so that's all you know, up to ninety-six times a day. Um With respect to data load wins. Um! They were able to significantly reduce, for each individual used case like fixed assets. Apparel go from hours down to sometimes just a couple of minutes. Um! In terms of, in terms of how quickly now they were able to get the data they needed
Ardeshir Ghanbarzadeh: uh for the different use cases within the office of finance. Um, and this uh, you know where we know it. It translated to you know
Ardeshir Ghanbarzadeh: hundreds of thousands of dollars in savings over time. Um! And and it's been a quite a bit of a game changer for the folks over at Comcast. In fact, Um, you know they they've uh they've been telling us that. Uh,
Ardeshir Ghanbarzadeh: you know they they They're seeing significant improvements when you, because some of the metrics and the numbers that they are um using for analysis. They're seeing some three hundred million rows of data. Um, they're seeing significant reduction like I talked about in that time it tasted develop new reports. Um! Ten thousand hours saved by users every month. That is a a huge amount, and and and translates to a lot of a lot of dollars and quite a bit of business value that they're uh they're realizing there.
Ardeshir Ghanbarzadeh: Um! Another customer of uh inquiry has had pretty good success is uh broadcom, as you are likely familiar with the company, they are. Um, it broadband infrastructure, slash, data, center, slash, electronic motors, slash Cyber security company. Uh, And they also um uh this year uh put put a bit uh a big bet down by acquiring Vmware uh? So they're now into the virtualization and cloud computing business as well. Um, they've had. They have a large variety of erp system
Ardeshir Ghanbarzadeh: and different business applications that they um. They store data in and and use for for reporting and specially supporting the office of finance. One of the things they were struggling with was to get a new report. It could take something like eight to twelve weeks uh from request to production, and that that essentially was unacceptable to them, and and really prohibiting out um the the office of finance to be able to do their job. And uh, but with the in quarter implementation they are now all um essentially getting their answers instantaneously
Ardeshir Ghanbarzadeh: uh, because they have visibility to all that data, because the uh, they are very able to very quickly access the data even run ad hoc queries. They're running something close to seventy thousand different queries. Um! And asking those types of questions uh on a regular basis. So it's been a a big game changer for them in terms of the speed and the delivery of the data, and the impact that has had uh on the on their business in terms of productivity in terms of efficiency and
Ardeshir Ghanbarzadeh: um in terms of protecting uh the value of the business. And it's a market capitalization with um, with the uh availability of the data for analysis and decision making.
Ardeshir Ghanbarzadeh: So uh, from this point on uh, I uh, I I wanted to um give you an opportunity to see a little bit of the product. And then, uh, the next phase of this uh session will be the virtual hands on lab. So uh, i'm going to uh stop share here, Mike, and uh and hand it over to you if you're ready.
Ardeshir Ghanbarzadeh: Uh, and you can let me know you can take over
Mike Nader: all right, all good. So our sure if you would, just before we uh
Mike Nader: jumping, Can you go back to the
Mike Nader: the slide where we had the in Court of Cloud? So on a slide Number two. I just want to make sure everybody is ready to go, and then we'll pick up right there. No, go down, won't back up one more
Mike Nader: right there. Okay, no
Mike Nader: down one. There you go right there. So everybody who is in session going to walk through the hands on Lab with us. Uh, please make sure, Anna, we put that into the chat channel
Mike Nader: for the Webinar today.
Mike Nader: Make sure you have signed up, and then i'm gonna share my screen in just a moment, and we're gonna go walk through a I'm gonna provide a demonstration quickly of what we're gonna do today as a group, and then show you a little bit, actually, even on the black line front,
Mike Nader: and talk a little around the data destinations like the upcoming ones like work day, but it's it ones that we've got deployed currently black line and other ways in which our customer base is leveraging in quartet
Mike Nader: to feed those financial systems and support those processes.
Mike Nader: No,
Mike Nader: before we jump in i'll make sure there are no any initial questions, and if not, we'll uh so we'll get right to it, or should we have any questions currently in the uh in the queue there?
Ardeshir Ghanbarzadeh: Yep, no, we are. We are good right now.
Mike Nader: So let's go ahead and
Mike Nader: jump into it. I will share my screen. Please let me know when you can see it. The other thing I will say is, we go through today? If you have any questions,
Mike Nader: Don't hesitate to
Mike Nader: just throw them out. Want to make sure that whatever we do
Mike Nader: uh we're answering your questions, and you're getting what you know
Mike Nader: that you all like this to see out of the session today. And I said, we will
Mike Nader: endeavor to get as many of those answered
Mike Nader: as possible.
Mike Nader: You see my screen.
Mike Nader: Okay. So what I want to make sure, And I'm: i'm gonna walk through this. Um, i'm gonna sign out and sign back in here everybody to participate in session today. You should have signed up at Cloud and Cordacom.
Mike Nader: So when you come into the environment. There
Mike Nader: you're going to get the option you won't. See all of the things that i'm seeing you're going to get the option to create a new cluster.
Mike Nader: Everybody should have created that new cluster if you did not.
Mike Nader: I'm going to do that with you right now.
Mike Nader: Then, I'm going to demonstrate a little bit for you. Why, everything is spinning up. I'll see what the email looks like
Mike Nader: that you would have received, and
Mike Nader: and we can jump into the hands on portion, and again as we go through um our to share out of folks on the call. I think guys are, Grant, from the imported team as well. If there are questions to pop into the chat,
Mike Nader: slow me down and
Mike Nader: make sure we get those addressed.
Mike Nader: So inside of the cloud environment. Your very first step you're going to do. If you have not already done it. Sit tight for a couple of minutes. If you have not very first thing you're going to do is create a brand new cluster,
Mike Nader: and
Mike Nader: you can name that cluster more or less anything you would like. There will be some nomenclature rules. You can't use as an example. And in the name. So by try to put something in that is
Mike Nader: sample.
Mike Nader: I'm: sure you're gonna be. Say, okay, you can't use that now for our lab today. I'm just gonna say hands on.
Mike Nader: I'll put a dash in.
Mike Nader: It's good,
Mike Nader: and you can just select either in our case. Today it won't make any difference. Select either an extra small or a small, that you may only see those options in your environment.
Mike Nader: Then you're going to create up a cluster, and you can call again. You can call this
Mike Nader: lying the naming rules, whatever you would like to call it. I'm gonna let that one be in my example for a moment.
Mike Nader: And so what will happen in the background for you? It's in court. It will spin up
Mike Nader: environment fully functional
Mike Nader: that is going in to allow us to deploy out the
Mike Nader: data app, and it will allow us to work through the activities today, and I'm. I'm. Going to have us deploy out one of the existing applications
Mike Nader: and play a little bit with the information we got.
Mike Nader: So while that's happening. If you haven't created something
Mike Nader: all right, or you're waiting.
Mike Nader: Um,
Mike Nader: i'm going to.
Mike Nader: Why would they show you an example of what the kind of thing we're going to deploy, and how it operates.
Mike Nader: So in quota as a technology, our first point earlier, we're an operational analytics platform and data feed platform designed specifically to work and anchor into your Erp environment and then supporting operational sources like your Hcm.
Mike Nader: And and other systems. But we really pride ourselves and focus very heavily on the operational data in the erp. And then just as importantly and aligning to the topic Bar Session today,
Mike Nader: feeding that data into the hands of finance and accounting teams, and frankly feeding that data into those core systems above the Erp for the consolidation, the reconciliation, the plan that those teams rely on
Mike Nader: and in court. It does this in a very unique way. As I mentioned earlier, it was. It is the first engine that I've worked with in my career that allows me to not only
Mike Nader: get the data available,
Mike Nader: or, let's say, my reconciliation, but then also have full ability to do on demand analysis down to the
Mike Nader: ledger and sub ledger transactions on what the Fp. And a side, if I want to do, let's say a comparative week over Week bridge, a financial bridge and and trending analysis in court. It has all the core transactions, so you can see that information and make that readily available for your reports.
Mike Nader: But then, if you want to aggregate those up to, let's say a month and feed that in for your re forecast process
Mike Nader: and quarter. Does that as well.
Mike Nader: And so I i'm gonna walk through a quick demonstration. And then i'm going to have everybody go through and follow me on a set of activities. So right now just sit tight. Let me walk through the demonstration portion of this. I'll show you what we're going to do today, and then we'll work through this as a group.
Mike Nader: So
Mike Nader: you know ours earlier also mentioned in Cortis data applications. These become the building blocks for the solutions you want to deploy out in your organization. Those can be. In some cases they are system specific, and in some cases they are not; and in fact, many, if not,
Mike Nader: go on a limb and say all of our customers, add in quota leverage in quota for tying together multiple data sources, but multiple operational data sources. You'll see
Mike Nader: one of the applications in here blame being that black line application we were talking about earlier. Um a little more on that momentarily. But just to you as an idea of how we operate and what we're going to do today.
Mike Nader: I'm going to deploy out a one of our oracle Evs applications, and i'll just forsake a demonstration that i'm going to use accounts payable,
Mike Nader: and we're going to use a slightly different one when we do our lab
Mike Nader: that way. I don't get too far ahead of what we're out with a lot of activities.
Mike Nader: So what this is going to do for us is going to bring in the transactional data sets that aligned to accounts available in this particular case
Mike Nader: we have we're gonna get the core data in. We're going to become a digital twin of that data set more on that momentarily as well. And then we're going to provide a simplified
Mike Nader: way for the business community to engage with that and a set of outputs that can serve as a basis for creating
Mike Nader: rightly more analysis, or even sending that data out to other systems. Now, and even in our lab, our hands on portion Later, we're going to use sample data here. And so i'm going to leverage a sample data set. Now this is a regular full set of erp,
Mike Nader: but it is sample, so not uh violating anybody's nda
Mike Nader: but what we will do when in quarter. And again. We'll do this all together is we are going to to pull that data, Replicate that into the platform and become a digital twin of the source, and you'll hear us say that digital twin concept frequently,
Mike Nader: because if there are a billion transactions in quota wants to hold those billion transactions,
Mike Nader: we're relevant to you and make them necessary, or make them available for necessary analysis and for other environments.
Mike Nader: So we we're not going to do. Is we're not going to Pre. Aggregate a bunch of information for you. We're going to pull that together here all your invoice lines. As an example.
Mike Nader: We are going to
Mike Nader: provide a set of relationships to the data that aligned a business process. And then we are going to create the
Mike Nader: an easy way for the business teams, almost dimensional in nature to engage with that information. But at no time will you ever lose access to the transactions,
Mike Nader: and regardless of whether or not we're doing that in, Let's say, a wizard like this, or we're doing this
Mike Nader: more custom or manual
Mike Nader: and court is going to follow the same process every time.
Mike Nader: The very first thing we do is extract the data
Mike Nader: ingested into the platform, row for row
Mike Nader: table, calling for column, table for table.
Mike Nader: And
Mike Nader: as we do that we start building out an intelligence around it. Now, you, you'll let us technically talk about this thing called direct data mapping. But really what that means
Mike Nader: from a practical perspective is,
Mike Nader: I don't need you to take
Mike Nader: your core system data and convert it into Let's say two or three different kinds of models, and then just make it available for analysis within quota. We're going to ingest that and then be able to use it.
Mike Nader: And, more importantly, be able to use it in context to a business process like you know, the pure pay on the on the accounts payable side, or even from a a reconciliation, or, you know, forecast perspective on the uh,
Mike Nader: the more chart of account-based systems,
Mike Nader: the second step in the process, and again, we'll do a little bit of this today in our hands on portion. We'll do it outside of the Wizard even, is the enrichment face with enrichment, you know. In this instance we're talking Ap: so this will be your
Mike Nader: ap aging buckets. This will be your, you know, ratio of po amount remaining to original Po Mount.
Mike Nader: This could also be
Mike Nader: a an an an Ml. Process machine learning process that you deploy inside of encoder and light as a data set and lay it right next to the actual side by side. So you you can compare what the the predictive outcome was supposed to be
Mike Nader: to how the actuals are actually trending. So very simple, very direct, varying thing.
Mike Nader: Now the data is all in the last step we did we did was make that data available in a memory-based, analytic engine for use and for feed out to other environments.
Mike Nader: I can consume that right now in, you know, in cortis visualization interface, and we'll use that for the session today.
Mike Nader: But the most important thing about inquiry is our engine, and how we operate at that core. So
Mike Nader: I don't want you to get lost into the visuals as much, and I love our visualization
Mike Nader: interface. It's easy.
Mike Nader: But the most important thing is how we're pulling the information together and how we make it available
Mike Nader: to systems and to people. So i'm going to back up a step, and i'm going to go over back over to
Mike Nader: our schema tab. Now most people never see that tab most in us, or see home content and scheduler.
Mike Nader: But going back to the Schema tab, we just deployed out in this example.
Mike Nader: That accounts table structure.
Mike Nader: It is four million rows of data,
Mike Nader: one hundred and eighty, you know, compresses well down to just about two hundred Megs of space. But all of It's here the same data structure that we were looking at earlier. This is your Erp.
Mike Nader: We make that available, and we can even incrementally feed that data to you as frequently as every five minutes, so you can have a constant view into what's happening in the world.
Mike Nader: Now,
Mike Nader: just to give you an idea and not something you would normally say, let's just jump into that. But to give you an idea here, if I click, explore and this will take it in Quarter's visualization interface.
Mike Nader: Yeah, I could see all twenty-three tables and not And again not normally, something I would say, Yeah, but it's definitely have somebody come in here and start playing with the vendors and in building reports
Mike Nader: off of a whole bunch of scattered data structures.
Mike Nader: So to simplify that instead of doing it. That way in quota provides not only packaged, but
Mike Nader: business scheme or semantic layer, but also the ability to very quickly you
Mike Nader: set these up on a persona basis or on a data set basis. It truly depends on how you want to deploy it.
Mike Nader: But the important thing here is that this is not
Mike Nader: a reload of your information. This is simply a dragon Drop
Mike Nader: way to pull columns together
Mike Nader: Think of it like a window on the side of a house. I can make it wider. I can make it narrower, and I do that by simply dragging it in.
Mike Nader: So if I want to look at my Ap. Hold codes, and maybe the descriptions on those.
Mike Nader: These are my descriptions.
Mike Nader: and leave it there.
Mike Nader: And now, when I want to build something when I work with it,
Mike Nader: instead of seeing all of the underlying Erp information.
Mike Nader: I see just that window. That's a small portion of it.
Mike Nader: And if I want to build something quickly, just to give you an idea
Mike Nader: now to that vendor name again,
Mike Nader: and i'll do that by See, I just throw the amounts on here
Mike Nader: and notice I've got an empty vendor name up there. So let me go and get rid of the empties
Mike Nader: You can very quickly build out the
Mike Nader: analysis to work with. But again, and I will go back to this
Mike Nader: also provide the ability to feed that out.
Mike Nader: Now, we package Finally, from a a data app perspective, we will deploy one of these together. We also package a set of insights and dashboards along with that. Now our insights. Those are the visuals, the singular visuals here
Mike Nader: and there are hundreds and hundreds of reports and insights that we provide on top of sap on top of ebs on top of the combined, you know, merged crosswalk drps.
Mike Nader: But what you, you, shouldn't lose perspective or sight of in the entire process, is our essentially the the truth of the business. The transactions that are occurring
Mike Nader: because the numbers up here are great, but these are aggregations for your total. So everything in here is
Mike Nader: the bottom table is a good example. There are two hundred and nine thousand invoice lines in there, every one of them,
Mike Nader: instead of scrolling through those thousand at a time. Here and there I went to see
Mike Nader: what something looks like, and in court I search for it, or I click on it,
Mike Nader: and that
Mike Nader: my number here three, three hundred and fifty, five point nine nine million dollars for monitors.
Mike Nader: He is
Mike Nader: sixteen thousand two hundred and twenty-six transactions,
Mike Nader: and they're all right here
Mike Nader: and it never misses. I mean in court. It doesn't, miss, because I never removed the fidelity in the access
Mike Nader: to that core data set for you.
Mike Nader: Now, last part, just from quick demonstration perspective. We'll talk a little more about. This is what we have done from a marketplace, but also integrating into the larger ecosystem of technologies.
Mike Nader: So we we mentioned earlier black line we mentioned earlier work day. We've You may hear us talk from time to time about one stream, or the old or or the Oracle Apm Cloud legacy hyperion stack, because we have customers that feed in into those environments, and frankly take the data from those environments and feed that back into in quarter. So you can have that side by side analysis,
Mike Nader: and one of our more recent partnerships in that spaces with black line where we have a number of customers, but also a package data application where we take the reconciliation data set and feed that directly out to black line. And that can be deployed as one of our applications
Mike Nader: we have
Mike Nader: inside of in Corda the ability to push data directly out to
Mike Nader: black line
Mike Nader: from a uh
Mike Nader: destination. Perspective, and over time you'll see more and more
Mike Nader: options popping in here, where we expand reach into that ecosystem. So we we've mentioned work day. We are in process right now. We're building a first class push out to the work day environment. But frankly, you could do it today
Mike Nader: by just automating the push out of in court and pull that into to really any planning, forecasting environment. But to give you an idea of how this operates from a a a a black line, perspective, or reconciliation, because reconciliation. If you're going to a different platform,
Mike Nader: you still have the same data set you need to work with,
Mike Nader: and that in the data app. We've packaged up
Mike Nader: not only
Mike Nader: a automated push which can be scheduled here, but we take the transactional set and put it into the input format. That
Mike Nader: black lines looking for It's an example.
Mike Nader: Feed that directly over to the environment on an ongoing basis,
Mike Nader: and we do that across oracle ebs. But multiple erps as well. Customer of mine right now
Mike Nader: live with pushing of data from oracle ebs as four hundred, and the last system escapes me. I think it's. I don't even speculate. But three different systems crosswalk together, and in Corda pushed out to black line on an ongoing basis. Their goal is to do it every two to three hours
Mike Nader: throughout the month, with an eye on reducing their cycle time in the closed process by about two days overall,
Mike Nader: because they don't want to wait toward the end. They want to constantly be able to look at it and perform those reconciliation activities and coming from the other direction. If i'm in, Let's say, a black line environment.
Mike Nader: Make sure I'm still log in here.
Mike Nader: I want to look at the reconciliations.
Mike Nader: Start getting into the numbers. Well, now, I can from in here
Mike Nader: those gl details, and I can draw right back out to
Mike Nader: and in quota environment.
Mike Nader: And then from there well, that one's pulling back up, but from there, once I land in it,
Mike Nader: it'll bring up
Mike Nader: the details
Mike Nader: around the activity.
Mike Nader: Then allow you to go all the way down
Mike Nader: from you know the to letter all the way, even down into the the arap inventory sub ledgers as well. So everything becomes available.
Mike Nader: So with that i'm going to pause from a demonstration perspective, and I really want to get us rolling on more that hands on website.
Mike Nader: So you should have received if you set up an environment,
Mike Nader: and you're gonna walk the hands on that. When i'll show what the email looks like, you should have received
Mike Nader: and email and the email will look very much like this right here.
Mike Nader: It's going to say. Here's your cluster creation, and it will give you your username and password. And what you're gonna want Is that original password up top here?
Mike Nader: So I want I'm going to pause for a moment. Give everybody an opportunity to open their environment up
Mike Nader: um grab their password
Mike Nader: log out of my base environment here, and then we will
Mike Nader: start playing on uh
Mike Nader: building out some things inside of inquiry.
Mike Nader: So give that about sixty seconds, and then we will get rolling. Um are to sure any questions that it popped into the uh chat up to this point.
Ardeshir Ghanbarzadeh: Uh, Yes, yeah, thanks. Mike Ryan and Grant have been um answering the questions that have been coming into the chat so far. Um, while uh, while folks are getting set up. I did want to take an opportunity to
Ardeshir Ghanbarzadeh: um to mention uh data app week, and if um if you can uh just let me share my screen real quick, absolutely. There you go!
Mike Nader: Let me! Uh, there we go. Stop my share.
Ardeshir Ghanbarzadeh: So folks um
Ardeshir Ghanbarzadeh: just wanted to let you know that uh in this on December sixth or the ninth Um. This year we are going to have a virtual event called in quarters uh Data App Week. We will have a
Ardeshir Ghanbarzadeh: um several days, four actually four days of of content, and uh, and a a a list of uh prominent guests that are going to be talking um to uh to uh to the registered uh attendees of um of data app. So um come and join us uh registered today. You can register at the go. That in quota dot com slash data,
Ardeshir Ghanbarzadeh: dash app dash week, as you can see in the link in the bottom left on your screen. Um, we are going to have a like, I said. A a a great cast of presenters uh from comcast like in wander quanta um meta and power play uh, as well as presentations by subject matter experts, adding quota um all around data applications and uh and financial uh analytics. So be sure to register for that event again. It'll be
Ardeshir Ghanbarzadeh: December the sixth through the ninth, and you can find the registration link at Ncorda. Com
Mike Nader: all right.
Mike Nader: I'll take the uh share back from you again, and we will get rolling on the hands on portion.
Mike Nader: alright, So you should have at this point. I I've got my password. I'm gonna just start with some very beginning. Start on the login side, and we will then work through deploying out one of the data applications and the play with a little bit. Change up a a business schema, build out a couple of pieces of analysis, and
Mike Nader: along the way, hopefully answer any of the questions you may have about. You know the platform, how it operates, and really where we're focused, and why? Um, So with that, the very first place you're going to come to is your login and screen.
Mike Nader: I know I changed my password out. I can show you where to do that uh, maybe a little easier one that you remember as opposed to the very complex one you get by default.
Mike Nader: But
Mike Nader: you should be able to go and sign into your environment.
Mike Nader: There we go, and if it's your first time signing in you're going to get prompted with,
Mike Nader: make sure we have your first and last name correctly, and it's going to ask your role in the organization.
Mike Nader: Bye,
Mike Nader: that's two boxes. Pick Any role is fine right now. It doesn't matter today,
Mike Nader: and it'll bring you to the in quota welcome screen. Now
Mike Nader: everybody logging in for the session today is going to log in as an administrator. So you're going to see all the tabs across the top of the screen from home all the way through security and marketplace.
Mike Nader: Today we are going to focus on starting from the marketplace, and we're going to move through the business scheme aside and the Content side
Mike Nader: There's a lot to the in court platform. We have a full set of learning options which are offered to the community, to the public free of charge. We have documentation that is offered
Mike Nader: publicly. We have a a community where our our customers interact. And frankly, our our partners and internal employees interact with our customers and each other
Mike Nader: around questions and around thoughts and ideas. For in quota. Given some time towards the end. I'll pull those screens up
Mike Nader: So all that said, My intention is to encourage you to, you know. Play a little bit on your own. You know my goal today is to let you walk through the process with me to get you started, but you know, from a a learning perspective from a documentation, learning perspective from just to playing with data.
Mike Nader: I would very much encourage you to continue working because it's the best way to learn a platform, and frankly, it's a lot of fun to do within corda.
Mike Nader: So very first thing we're going to do as a group. I want us to go over the marketplace tab,
Mike Nader: and
Mike Nader: as we were looking at earlier, there are going to be a whole host of applications that in quota and Franklin Cortis partner, community provide,
Mike Nader: and if you have an application that you think would be wonderful for us to build, or for one of our partners to build. You can submit requests for that.
Mike Nader: Um! If you have something you have built as a partner, you can
Mike Nader: put a request in for that, or if you would like to come in and quarter, partner, there's an option at the bottom. The screen will drive you to the the sign up page the request Page to have that conversation
Mike Nader: keep keep things simple. Today we're gonna use these quick start applications once you find across top of the screen here
Mike Nader: and to make it even simpler still. What I want you to do on the left hand side.
Mike Nader: I want you to click on Oraclei business suite.
Mike Nader: You know I I've got a pretty good data set in it that you orically business suite set of applications or about uh, I think we've got about half a billion records in it, and it is
Mike Nader: fairly broad.
Mike Nader: Um! The quick starts, and we put these out here specifically for folks to learn with and really start working through initial solutions in the platform. This is deploy quickly,
Mike Nader: hence the name,
Mike Nader: but we will leverage these today because it will automatically set our connections up for us and walk us through.
Mike Nader: You know the initial deployment piece, and then we can start modifying. You have a little phone with the data set. So what I want us to actually use, and I know we're talking a lot about financials. Bear with me. We're in the supply chain, one, and I want to use the supply chain application, because not only is it going to give you visibility to your orders, it's also going to give you a trial balance,
Mike Nader: and we can. You know it. It extend the number of things from there.
Mike Nader: So again, in the application, Go to the marketplace, Tab. I'll do the clicks again, just to make sure. But it's caught up with me.
Mike Nader: Select on the left hand side orically business suite
Mike Nader: under the quick start area, select
Mike Nader: orically business suite order management,
Mike Nader: and then in the
Mike Nader: upper right You're going to see an install button. I'm going to go ahead and click that,
Mike Nader: and I would like you to do the same. Click, install. Now, all that is going to do is just carve out a space in the platform for this application, and we're gonna work with,
Mike Nader: and what we're going to do is pull in detailed, you know, booking billing backlog information as well as the financial trial information around that, and it's going to break out in an inquiry into
Mike Nader: Yeah, two schema for. So two subject areas. Really, you're going to see one from the Gl side. You're going to get one from the order management side. Now we don't see the tables in here yet. I'll show you the tables that you are d diagram in a moment.
Mike Nader: But
Mike Nader: as a collective group, I want us to pull this data in. We will get a series of those semantic areas,
Mike Nader: and we will. Also
Mike Nader: you get a series of dashboards that we can work with.
Mike Nader: So in the lower right corner for me here,
Mike Nader: click, load data.
Mike Nader: Now, same process. I was doing people. I'm just using a different application. We're all using a different application.
Mike Nader: This is going to go through and pull in
Mike Nader: the rose. It is going to do the enrichment that we talked about earlier, and we'll take a look at some of those examples, and maybe even make one of our own, and then it will make that data available to us,
Mike Nader: and we'll play with that inside of the in quarter. Ui. But I I will pause just to see if there are, and I I saw a couple of bubbles pop up Grant, or to share anybody where any questions you think we need to address
Mike Nader: or just topics it would be while this is loading up we should uh
Mike Nader: go to
Ardeshir Ghanbarzadeh: No, I'm: Sorry. Yeah. Looking at the chat right now. It looks like Brian. And uh,
Ardeshir Ghanbarzadeh: yeah, I've already gotten to most of just about every question. Yep, perfect, perfect.
Mike Nader: Alright, So this is, if you on the same click Strand schedule as I am. It should be done here in just a few moments. You'll see that, or or our order management piece. We've loaded up about just under four million records of data uh the the Gl side we're bringing in,
Mike Nader: you know, nine ten million records, I believe overall
Mike Nader: uh first schema is ready to go, and we're just waiting on that second one to finish here.
Mike Nader: The other thing to note, and we've meant this, you know, mentioned a couple of times in the the conversation,
Mike Nader: but you know it really important from you know. Thinking about the way in corda operates is that, you know, today we're playing with
Mike Nader: a given
Mike Nader: our customers
Mike Nader: in most of our you most have done all of our you case use cases or multiple erps
Mike Nader: so, but because in court it keeps the data
Mike Nader: at the transactional grain it makes that available.
Mike Nader: If you have a you know set of master data that you feed into your environment and want to leverage the day that can be fed into in quota. And since simply related
Mike Nader: to the data sets, and i'll explain what I mean when I say related
Mike Nader: just as soon as this finishes it's easier to show than it is to just talk through
Mike Nader: point, being that cross, walking multiple, completely different companies, like Sa. Pcc. And in Oracle's ebs. Or
Mike Nader: as for Hannah and Vbs, or fusion,
Mike Nader: very simple,
Mike Nader: but also tying together.
Mike Nader: Let's say your Hcm. Environment and wanting to see how that impacts things. We're looking at a unified,
Mike Nader: you know P. And L. Tree, and then drilling down and disaggregating into separate operating companies
Mike Nader: on a click
Mike Nader: out of the box and in quota. And so we'll take a look at that now. So my my environment is loaded up the data,
Mike Nader: i'll make the assumption that,
Mike Nader: and most of you are following along with me and you're in the same place.
Mike Nader: You'll get the two schema in, and we've got about twelve million rounding up rows on the uh
Mike Nader: Gl. Side in about
Mike Nader: four million are also rounding up a little more generously on the order management side, and in the lower right corner click, explore data.
Mike Nader: Yeah. Immediately you would be brought to
Mike Nader: the sense of the consumption. You are
Mike Nader: same thing. You're on content,
Mike Nader: and you'd have the ability to look at that content. And we will do that in a moment. And actually i'll tell you what? Just so you've got an idea. Now go ahead and click the content tab for me.
Mike Nader: Click on Ebs order management,
Mike Nader: and then
Mike Nader: click on the one dashboard order and billing overview.
Mike Nader: So this is the output of the action we just went through.
Mike Nader: We just created
Mike Nader: The data set are pulled in a data set
Mike Nader: from any Rp.
Mike Nader: We have
Mike Nader: set of analyses that are packaged, populated automatically.
Mike Nader: But for me the most important thing is going to be.
Mike Nader: How did the process happen, and what else can I do with it? Not just the you know the small set of dashboards that you've given me,
Mike Nader: so what i'd like you to do is come over to
Mike Nader: the data tap,
Mike Nader: and you may not have all of these uh connections in here, or you may depends because I did a couple of extra things on the on the demonstration early on,
Mike Nader: but as part of the deployment
Mike Nader: set up here, we got a set of data connections out to
Mike Nader: the various sources.
Mike Nader: So you'll see a my sequel database. Most likely this is an online store. The old adventure works data set. Then
Mike Nader: something something similar, that this is a a sample that in court it tends to package up and deploy on on most of the the cloud environments, just to give you something to start with. We deployed out
Mike Nader: an Ebs data application, and we're using the sample data.
Mike Nader: So again, on the in cortiside we created that
Mike Nader: there we go. So we created that connection under the covers. Um, if you click test connection over here, you should get it successful, and if you want to look at the connection and click the pencil.
Mike Nader: And what you'll see is a connection out to a data source.
Mike Nader: The password will not show there.
Mike Nader: But this is how we set up all of our connections, and in
Mike Nader: if you have a data set or a destination,
Mike Nader: so let's go with the data set right Now that I want to go after
Mike Nader: adding a data source into the platform is simply, or is as simple as selecting one of the options from a connection perspective,
Mike Nader: you know, from a database, from the application side,
Mike Nader: even out into the Data Lake world other query services,
Mike Nader: file, based or cloud file based,
Mike Nader: and even custom, Api or
Mike Nader: stream connectors.
Mike Nader: We can also
Mike Nader: upload data files and folders as well.
Mike Nader: Now, the connectors in here and oracle, or excuse me in court has,
Mike Nader: I think, access to
Mike Nader: two to three hundred different connectors. We package a set in the platform here to make it simple to start with, and a lot of vendors from data sources will supply connectors at no charge, and some charge for the connector. So everything you see here that i'm showing on the screen. I'll just double check myself. This is package to this in quarter.
Mike Nader: Uh, if I wanted to add new custom, connector some C data, Whether or not. Something is charged there that
Mike Nader: that would go to. You know our partner, c. Date in that particular case
Mike Nader: the other thing to point out um when something that is fairly prevalent. I look just outside of Chicago in the midwest. Here is
Mike Nader: we also go into some more legacy sources as well. So I've got at least ten customers that I can think of it in quota that leverage us against legacy as four hundred systems to pull data in and to tie that, together with their more modern erp or other operational
Mike Nader: sources.
Mike Nader: No, no need to add a data source today, but did want to point it out. That was done as part of our marketplace deployment, and
Mike Nader: you can on your cloud account while you have at the trial running, you have the ability to connect into other data sources. So you have access to an environment
Mike Nader: you can put your credentials in there, not dissimilar from how you, you know, get external data in excel
Mike Nader: and pull that data into in quota.
Mike Nader: Now, if you would go over to the schema tab for me here across the top,
Mike Nader: what we're going to do now is take a look at the schema we have deployed out,
Mike Nader: and you will see in your environment three of them we see four in mind because I deployed that Ap. One out earlier as an example. But you should see your online store. You should see your gl. You should see your order management.
Mike Nader: So let's look at the gl first.
Mike Nader: So in your schema list. Here, go ahead and click on oracle Ebsg:
Mike Nader: you will have fifteen tables. They will be related to each other
Mike Nader: uh, through our joins more on that momentarily, and about twelve million ish rows of data,
Mike Nader: and that breaks down across a series of sources in here. So if you click up in the upper right corner you click on diagram,
Mike Nader: it'll give you the layout of the information that we've pulled in.
Mike Nader: So we brought that data in, and we've created. We packaged a set of relationships
Mike Nader: on the information. So the the really neat thing about the way in court is engine operates is that as long as we can understand how things relate to each other
Mike Nader: within,
Mike Nader: let's say, one scheme, or even across schemas. And that's by the way, what you see right here in order management how things are related from orders
Mike Nader: to the G. Or from the gl over to the orders.
Mike Nader: It's all fair game to ask questions about,
Mike Nader: so I don't have to think about
Mike Nader: bringing the data in. And then how do. I have to build three or four data marks or a bunch of additional cubes to model it.
Mike Nader: I can just work with it.
Mike Nader: And the very practical outcome of that is for business teams, financials, and financial teams, accounting teams in in particular.
Mike Nader: I now have
Mike Nader: immediately from the sub ledger side,
Mike Nader: which is my Xl. A. Here all the way up to the balances, and and frankly can then
Mike Nader: push that data
Mike Nader: all the way out to my
Mike Nader: other processes on the consolidation or the plan side,
Mike Nader: pull the relevant data back and continue to work
Mike Nader: a unified environment for analysis without having to jump through a dozen different places.
Mike Nader: If you click on any of these lines you'll see how we've related them. Those are all editable.
Mike Nader: If I were to click on any of the lines and then say, click to pencil
Mike Nader: Here
Mike Nader: It'll show me how we've related it, and if I wanted to change that,
Mike Nader: I wanted to add an additional condition.
Mike Nader: I want to add a filter in
Mike Nader: for more of a day to minded or a data engineer type person. I can do all that here for. Now, Don't, let's just leave. Let these sit.
Mike Nader: We'll walk through the other portions of what we've we've got.
Mike Nader: If you wanted to look at any of the underlying tables themselves.
Mike Nader: Close the diagram.
Mike Nader: Come to the list here
Mike Nader: and let's go. Let's say, gl balances.
Mike Nader: I'm just going to click on that.
Mike Nader: So as we pulled that data into in quota. We replicated that and became that digital twin.
Mike Nader: You're going to see
Mike Nader: the information as it came in
Mike Nader: where we have started to give you a little bit of a pretty your name to it, but you can change those. Put those semantics on for your for the users,
Mike Nader: you know in a moment, in in any number of ways
Mike Nader: we'll leverage that information to build out our semantic structure, our business schema and our
Mike Nader: our analysis.
Mike Nader: I really wanted to go back and look at the base sequel or how it was extracted. We can go all the way back and do that, but for today.
Mike Nader: I'm going to leave it here. With these two exceptions. If you hover over any of the columns you're going to see a couple of really cool features. First off you will see the ability to preview the data that a little eyeball you can see what's in there.
Mike Nader: Get an idea of
Mike Nader: the nature of the information.
Mike Nader: But if I also want to look at, let's say, like period name,
Mike Nader: I can also start looking at how that data is used across my encoded environment
Mike Nader: now chosen really
Mike Nader: simple one here. But I can do that all the way from data source out to analysis. Frankly, all the way from analysis back to the data source. And so really, anywhere inside of inquiry, you see that little helix
Mike Nader: That's what your outputs going to be. You're going to be able to look at the
Mike Nader: the lineage of information as it flows through.
Mike Nader: And so,
Mike Nader: especially if i'm trying to do something, let's say, in a reconciliation process or a data validation piece.
Mike Nader: Something's off for any reason. And people ask, Well, how'd you get there? Where it? What What did this depend on within Corda, we should be able to do that to click for you,
Mike Nader: and that is a a common mantra for us. How do we make it? Simpler? Fewer clicks to go through.
Mike Nader: I'm gonna cancel up there.
Mike Nader: We'll work with this data today a little bit. I want to go over to our business scheme
Mike Nader: and play with that a little bit.
Mike Nader: But before we do that we're going to take a quick look,
Mike Nader: click on the scheme of the tab.
Mike Nader: Come down to the
Mike Nader: Ebs order management. So again click on the schema tab or off it, and on it come down to Ebs order management.
Mike Nader: In here about four million rows of data.
Mike Nader: We have fifteen tables, and you get a break out here of my customer transactions all the way down to the custom transaction lines.
Mike Nader: Um, my parties, my payment parties, my orders when they shipped. So I've got some visibility into that bookings Billings backlog along with
Mike Nader: the more aggregate financial information.
Mike Nader: So following along, click on the business. Schema tab for me now,
Mike Nader: and what you will see in here
Mike Nader: again. I've got one extra one from the demo I did earlier.
Mike Nader: You're gonna see three business schema, financial statement,
Mike Nader: your order management, your online store. So let's go into our order management.
Mike Nader: Go ahead and click on that
Mike Nader: Now
Mike Nader: our business schema
Mike Nader: simply, and I use this analogy a lot.
Mike Nader: They are just a a window.
Mike Nader: I have not changed your data. I have not loaded it a second time. Here
Mike Nader: they are not. You don't have to worry about if I incrementally feed transactions every fifteen minutes
Mike Nader: that I've got to come back here and do something special with the business. Schema. This is simply a way to drag and drop, or, frankly, even if I wanted to build it as if I were building a report,
Mike Nader: create a way for
Mike Nader: the business community to engage with. The data came out of I Erp
Mike Nader: in a
Mike Nader: familiar, more simplified fashion.
Mike Nader: So in here, in the business scheme, and three dots on the right of any of these
Mike Nader: click on that and select edit.
Mike Nader: Now in the lower left corner all the way down here.
Mike Nader: You're going to see collapse all go ahead and click on that for me,
Mike Nader: and then i'm going to expand this one level.
Mike Nader: What you see in this tree on the left. That's where the data came from.
Mike Nader: This is your erp. This is what we brought in.
Mike Nader: What you see on the right,
Mike Nader: or simply what we have dragged into,
Mike Nader: or in this case populated for you
Mike Nader: a set of columns,
Mike Nader: and in many cases renamed those in a way to make it really really simple
Mike Nader: for people to understand.
Mike Nader: So if we talking about, let's say master data processes,
Mike Nader: you can feed that master data into in quota and apply it.
Mike Nader: That's what your users would see.
Mike Nader: You don't have to impact the source. The Rp. You don't have to wait for it to run through a a lengthy
Mike Nader: data, warehousing data like process to get it there,
Mike Nader: and that's not to say we're counter to that. In fact, we're complimentary to it. Very much so. But you can very quickly make changes here. So if I wanted to add a column error, or I want to add a calculation in,
Mike Nader: and we'll do the ladder in a moment. You can do that.
Mike Nader: Go ahead and click, cancel here,
Mike Nader: and finally
Mike Nader: come out to the content paying for me,
Mike Nader: and you will see two folders in your case. So again I deployed. This accounts payable one earlier. It's part of that demo.
Mike Nader: If you click on the ebs order management
Mike Nader: you'll get one folder and three additional dashboards in there gonna get your order and billing overview your trial balance and journals to the ar sub ledger and an income statement analysis.
Mike Nader: So if you would click on the
Mike Nader: um order and billing overview for me,
Mike Nader: and then you should all have an output
Mike Nader: that provides
Mike Nader: three tabs.
Mike Nader: Now. Quick set of filters across the top,
Mike Nader: but going from, or an orders overview to the billing details
Mike Nader: all the way through the delivery details.
Mike Nader: and Then we're going to back up.
Mike Nader: We'll review one of the dashboard. We're gonna go in and back up a step to the business schema and start making some changes.
Mike Nader: So again i'll I'll ask our to share, you know. Grant anybody. Um, if you're monitoring questions, is anything come up that we need to uh pivot over to, or are we still rolling along?
Mike Nader: Wonderful. Thanks So much. Yeah, absolutely.
Mike Nader: So let's take a look at this and make sure the other piece contents out there for us. I'm going to click on the content. I click on the breadcrumbs across the top, but click on the Content pane.
Mike Nader: I'm gonna go back in the order management again.
Mike Nader: I'm going to click on the trial balance and journals.
Mike Nader: So make sure that paints in for us,
Mike Nader: and if we're missing some data there it's clear the filters out There we go. So if you come in here
Mike Nader: and we'll fix that in a moment.
Mike Nader: I'm gonna do it again. Go into the o two dashboard for me.
Mike Nader: I got a bad time variable set. So
Mike Nader: up top here what you seeing are filters in in quarter, so I can now change it by ledger, change it by period, name for right now let's just get rid of that filler. Just so we can see the information coming in,
Mike Nader: and what you will see in this particular dashboard is the ability not only to
Mike Nader: look at by account segment as an example. But then also,
Mike Nader: if I want to drill to
Mike Nader: different report, I can go all the way over to
Mike Nader: you. Know the journal headers that align to that exact account,
Mike Nader: and see those reflected again. These are package as far as in quota,
Mike Nader: but very, very simple for us to do, and very, very simple, More importantly, for you to do in your own environment.
Mike Nader: I just want to make sure those were in place,
Mike Nader: and then we will uh,
Mike Nader: we'll start doing something from the ground up here as far as building out a couple of insights.
Mike Nader: All right. Come back if you would, for me, come back over to the business scheme aside. Click on that again,
Mike Nader: and i'd like you to go into order Management
Mike Nader: ebs,
Mike Nader: and what we're going to do is
Mike Nader: is click explore data for me here
Mike Nader: in the upper right corner.
Mike Nader: Now, this is gonna bring you out to the in quota visualization interface. We bounce in and out of it a few times.
Mike Nader: I like starting from the business scheme just makes it a little simpler for us to.
Mike Nader: You know I don't have to select data sets
Mike Nader: in this Ui. Everything starts expanded.
Mike Nader: So right up here in the upper left, See these three dots.
Mike Nader: More options click on that and select collapse to table level.
Mike Nader: So we've got
Mike Nader: essentially six uh
Mike Nader: six dimensions and six views that we're going to work with here, and we'll build something out
Mike Nader: just to get an idea of how the Ui works, and then we'll go. Add a calculation or two, and and play around a little more um
Mike Nader: artist, your grant. Just I want to double check. One thing.
Mike Nader: We've got about twelve, fifteen minutes remaining in the session. Correct,
Mike Nader: perfect. Just wanted to make sure I was tracking it the right way. That's just fine. Alright, So in the Ui here, what i'd like you to do
Mike Nader: is select for me.
Mike Nader: I always start with
Mike Nader: Yeah, a list table just to make it a little simpler. So let's let's just start with that today. What I want you to do is expand the customer,
Mike Nader: folder, drag the customer name into the grouping dimension.
Mike Nader: No,
Mike Nader: the difference between the group and in the measure dimension. I could drag it down here, too. It's just gonna give me a list. Hence the name, the the insight listing table, a list of customers. If you are a power bi or tableau shop or micro strategy or cognos,
Mike Nader: you can one hundred percent connect into one quarter.
Mike Nader: We look like a postgres database, although internal, we are not.
Mike Nader: But you can connect into us and do everything i'm doing here.
Mike Nader: So we're simplicity in the workshop. We're using this ui.
Mike Nader: But if you're thinking, oh, man, I gotta use power bi back in my office, by all means we support it. And, in fact, Microsoft's uh
Mike Nader: is not only a technical partner. Record us, but a funding partner, of course,
Mike Nader: but I want the customer name up in the grouping dimension here. That's just going to make it sort automatically for me.
Mike Nader: And then from an a metric perspective, if you'll expand the orders folder there,
Mike Nader: let's look at the order amount. You know something dead simple. Just drag that to the measure for me,
Mike Nader: a bunch of emptiness. And that's okay. It was intentional.
Mike Nader: So
Mike Nader: because we just told in court it to list the data.
Mike Nader: You're not getting any level of aggregation at all. We have one hundred and ninety-nine thousand nine hundred and sixty two rows down here.
Mike Nader: It's just throwing all of the information out there.
Mike Nader: So i'm gonna have you change the insight type here.
Mike Nader: I want you to go from a listing table to an aggregate table.
Mike Nader: And what you're going to get now
Mike Nader: is an aggregation down to four hundred and fifty-nine rows
Mike Nader: of that data.
Mike Nader: But I promise you we won't lose the ability to get to the transactions,
Mike Nader: and you'll see there is your one blank customer name.
Mike Nader: So let's get rid of that
Mike Nader: for our session today.
Mike Nader: So go back over here on the left, take customer, name, and drag it, and drop customer
Mike Nader: into that filter shelf.
Mike Nader: Now I can
Mike Nader: do all kinds of things in here in respect to how I filter things out,
Mike Nader: but just to expedite it, make it easy, click on the drop down
Mike Nader: and select not null, but just get rid of the empty,
Mike Nader: and we're going to do a couple of other things on this
Mike Nader: up top. Here,
Mike Nader: go back to the grouping dimension click on the pill, click on customer. Name
Mike Nader: a little arrow there, and it's going to give you some options we have in here.
Mike Nader: So what I want to do is, you know. Let's uh,
Mike Nader: let's take
Mike Nader: order amount, and I want you to drag that
Mike Nader: and drop it in that sort by shelf,
Mike Nader: so and you'll notice right now it's descending.
Mike Nader: You can click on that. It'll change a day sending. But we're gonna change that to uh to descending here, or excuse me, you know. Leave it as descending,
Mike Nader: and there's all kinds of ways to format data. And in order to put your
Mike Nader: yeah currency symbols things for today
Mike Nader: we'll leave that, you know. We'll leave that to a further conversation. But so we sorted this down.
Mike Nader: We've put the order amount in.
Mike Nader: Let's put the unit selling price up there as well, Mary,
Mike Nader: and
Mike Nader: then I could say, All right, We've got a unit selling price, and then do I have
Mike Nader: my ordered quantity.
Mike Nader: Not. What's happening
Mike Nader: is um
Mike Nader: aggregating the selling price. Here we'll fix that in a couple of minutes
Mike Nader: but as an idea you have four hundred and fifty-eight rows broken out by customer removing the empty customer name. In this case
Mike Nader: we sorted it by order amount
Mike Nader: So I want you to go up to the gear in the upper right corner.
Mike Nader: Click on that gear to bring up the options,
Mike Nader: and your second or third option down. Max Rose limit
Mike Nader: type in the number twenty-five for me.
Mike Nader: So really what we're doing here,
Mike Nader: creating a top twenty five report the
Mike Nader: by revenue.
Mike Nader: And if I want to put a title on that and click up there and make that the title or the subtitle.
Mike Nader: But again, with an eye on our time, which is right now to click save,
Mike Nader: we get the stave as dialogue, select new folder,
Mike Nader: and i'm just gonna call mine uh
Mike Nader: customer
Mike Nader: sales
Mike Nader: click. Add,
Mike Nader: Then i'll select my new folder
Mike Nader: click save and name my dashboard Here i'm just gonna call it sales
Mike Nader: and click, add.
Mike Nader: So what we're doing here for what we've done here
Mike Nader: deployed the data from marketplace leveraging one of the business schema,
Mike Nader: just quickly built out a piece of content, and i'm gonna have us build out a couple more. Then I'm gonna have to go back and put a formula on some of these as well,
Mike Nader: so you can see how that operates. It will finish up in just about five minutes on on the hands, on portion,
Mike Nader: so to make it really simple.
Mike Nader: Let's Do you know, really quick, I should say, let's do this
Mike Nader: Hover over your insight,
Mike Nader: My table here,
Mike Nader: and you're going to see three dots
Mike Nader: as an option
Mike Nader: click on that, and I want you to select duplicate.
Mike Nader: So now what I would like you to do.
Mike Nader: Go to your second
Mike Nader: insight. Your chart click the pencil for me. The pencil is going to let you let you add it that
Mike Nader: it just gave us a starting point in here.
Mike Nader: Come up to the drop down, up top,
Mike Nader: and I want to break that down.
Mike Nader: Let's break that down into a
Mike Nader: the doughnut chart.
Mike Nader: So this is giving me my
Mike Nader: all my customers here, mine
Mike Nader: full set
Mike Nader: again. I want you to come up to the gear for me.
Mike Nader: Let's make this
Mike Nader: twenty-five again,
Mike Nader: and what you'll see is in corda.
Mike Nader: Create your top twenty-five and then it buckets. Everybody else merged together
Mike Nader: into one category
Mike Nader: so we're, more or less getting the same type of view here now, just broken down by percentage.
Mike Nader: But I want to have a little more fun with this. So what I'd also like you to do Then I here, please, is
Mike Nader: we're going to drag in some item information,
Mike Nader: and we'll see where we can
Mike Nader: pull that
Mike Nader: together. So we've got our customer.
Mike Nader: Let's break that down by,
Mike Nader: let's say item. So take item. Next, let's take item description right here.
Mike Nader: Drag that into the chart right below customer.
Mike Nader: I'll show you the impact of that in a minute.
Mike Nader: Now, under the covers. What's happening is we're doing all of this,
Mike Nader: and it's important to note, and you don't need to follow this necessarily set, it clicks.
Mike Nader: But as you're working in in
Mike Nader: what we're doing under the covers,
Mike Nader: we are dynamically moving across the data sets that we brought in,
Mike Nader: and that can be billions of records. It can be dozens and dozens of tables and data data source areas. It doesn't matter. This is what we were designed for. Now, this is a really simple example
Mike Nader: of in court, of working across the data sets.
Mike Nader: I have seen these so complex that you have to zoom in the even read the name of one table,
Mike Nader: and the users don't need to know that.
Mike Nader: Frankly. Once it's set up on the data engineering side. You don't need to know that as either the goal is to provide the information out.
Mike Nader: So we've got customer name. We've got item description. Those three metrics click. Save for me,
Mike Nader: i'm gonna do two more things, and i'm gonna I'll uh
Mike Nader: have us finish up
Mike Nader: the neat thing about dragging item in the way we did
Mike Nader: is if you click on anything inside of the chart,
Mike Nader: what you've essentially done is set up a drill path that allows us to move by some of the descriptions
Mike Nader: from
Mike Nader: one dimension or one
Mike Nader: attribute
Mike Nader: to another automatically. Just put a drill down implicitly.
Mike Nader: So let's do one last thing
Mike Nader: here.
Mike Nader: I want you to click the plus for me
Mike Nader: in the upper right corner that I want that past. I'll slow it down upper right corner. Click the plus to add insight, We're going to do one more.
Mike Nader: And
Mike Nader: again I'm going to collapse to the table level here.
Mike Nader: What I want us to do is go to this orders
Mike Nader: folder. Just drag all of that and drop it on the measure,
Mike Nader: and we have one hundred and ninety-nine thousand lines in there.
Mike Nader: Just click, save for me.
Mike Nader: So on our our quick dashboard. We've got three insights
Mike Nader: bottom. One is one hundred and ninety-nine thousand rows.
Mike Nader: Now, if you hover over that middle insight
Mike Nader: You're going to see this little grip bar
Mike Nader: that lets us move some things around. I'm going to move mine around,
Mike Nader: and i'm just going to drag it up here and put it to the left up top
Mike Nader: and save it.
Mike Nader: So I've got an aggregate table. I have got a breakdown of
Mike Nader: my top twenty-five with a drill into item, and I've got the detail below it.
Mike Nader: And if I come to this table and I want to say, okay, Where did this order amount for two hundred and ninety-three thousand? Come,
Mike Nader: and I click on it.
Mike Nader: These are the orders
Mike Nader: that aligned to that and that happens automatically in in quota, because
Mike Nader: we never got rid of the transactions. That's what we're doing. That's how we operate.
Mike Nader: And so every time you work with data here you get to preserve that relationship and the ability to get to the details.
Mike Nader: Now I know we're right at the hour, and I wanted to do one other thing for us on a calculation perspective.
Mike Nader: So if you bear with me for thirty seconds, let me show you something
Mike Nader: and Don't worry about trying to follow along. But I do want to give you an idea of adaptability how agile it is to work.
Mike Nader: So i'm going to go back to the business scheme for just a minute,
Mike Nader: and
Mike Nader: I'm going to go into that order management side, and i'm going to just quickly create a calculation for us, and I can do this on a report. But let me
Mike Nader: show you how easy it is to change that window
Mike Nader: and
Mike Nader: make it really easy. I put it right up top.
Mike Nader: Now build out a quick ratio for us.
Mike Nader: And so what I can do here
Mike Nader: he is.
Mike Nader: Okay, i'm gonna go to my orders.
Mike Nader: So I add here, yeah customers or my orders at Orca lines. Oh,
Mike Nader: I will take a look at
Mike Nader: So let's go on, mount,
Mike Nader: and i'll divide that by
Mike Nader: today. Just something silly, taxable amount, just to give you an idea
Mike Nader: what it looks like to pull that together.
Mike Nader: But now
Mike Nader: what we just built out
Mike Nader: using that same data set.
Mike Nader: If I wanted to go and
Mike Nader: work with or add an insight,
Mike Nader: I can use
Mike Nader: that new calculation
Mike Nader: immediately here, because
Mike Nader: it's immediately available to me.
Mike Nader: And so it's just part of
Mike Nader: the data set
Mike Nader: as soon as I click save
Mike Nader: i'm going to leave it at that today, and and it was just to give you an idea of how in court it was operating in, and the way it works.
Mike Nader: Happy to answer any other questions you might have. I appreciate everybody's time today, and Patience, and for indulging me for about three extra minutes. Um! And for the recorded team on the phone. I'm gonna pause and see if there are any other questions that we can answer.
Ardeshir Ghanbarzadeh: All the questions that chat have been answered. So thank you. Everybody for for joining us today. Just one last
Ardeshir Ghanbarzadeh: a quick reminder. Don't forget to register for our upcoming and Corda data App week, December sixth, through the ninth we have a great line of speakers. Uh, so please would register for uh the event at in quota dot com um Thank you again for joining us today for the presentation. And thank you, Mike, for taking us through the virtual hands on lab. Hopefully. Everybody got a chance to um get the record experience, and there will be a recording of the the presentation available on demand, and about twenty-four to forty-eight hours on our website. So please do check in on that. If you want to review
Ardeshir Ghanbarzadeh: some of the areas that Mike went through on the virtual hands on that, Thank you again for your time today, and have a great rest of the week.
Director, Product Marketing
VP, Business Analytic Solutions