It’s no secret that Oracle Business Intelligence Enterprise Edition (OBIEE) is nearing end of life with no more product development. For many, this is an opportunity to take advantage of the elasticity and scale of the cloud for their analytics workloads. Oracle Analytics Cloud (OAC) seems like the logical path to the cloud. However, OAC is built on top of the 15-year-old OBIEE framework, which was designed for on-premises use. OAC, therefore, retains a lot of OBIEE’s limitations while still requiring a lift and shift to the cloud. Welcome to your new nightmare.
Watch as we discuss the four reasons why you should rethink your Oracle Analytics Cloud migration. We’ll explore how a modern data and analytics platform like Incorta addresses the limitations of OAC and empowers your business to achieve faster insights and create new business value in the cloud.
You’ll learn how to:
- Avoid time-consuming ETL and star schema modeling, so you can be more responsive to the business and focus on innovation versus maintenance.
- Quickly load data for each module from the Oracle database and make it available to view in prebuilt dashboards in near real time.
- Optimize the performance and agility of leading visualization tools (e.g., PowerBI and Tableau).
- Quickly create a self-service semantic layer on top of the source data.
- Run lightning-fast reporting queries on transaction-level data, regardless of table structure.
Mike Nader: Good afternoon, good morning everybody, thank you for joining today's webinar will get going in just a second my a few few seconds, my name is Mike at your moderator today.
Mike Nader: Joining myself as a team Mohammed part of in quarters development solutions team will do a more formal introduction, as I said in just a few seconds.
Mike Nader: Again, good afternoon or good evening depending on or Good morning, frankly, depending on your your time zone Thank you again for attending our session today for things to consider what the or Oracle analytics cloud migration i'm going to be your moderator today, my name is Mike nadir.
Mike Nader: With me presenting with me today is going to Mohammed.
Mike Nader: Just a quick overview of ourselves so both of you and i've been in the analytic space for quite some time i'll admit to introduce himself momentarily.
Mike Nader: i've spent the last 25 years focus pretty heavily on enterprise performance in corporate performance management analytics really, with a focus on on driving operational analysis into business teams, whether that's finance whether that is.
Mike Nader: Other areas of business but that's been really the core of my work, both as a professional in professional services, I spent some time with the wise and managing director worked at other consultancies.
Mike Nader: And i've also spent about my career in the software world with Oracle with in court i'm with with some other vendors matina going let you introduce yourself.
Mateen Mohamed: hi good morning good afternoon everybody just machine why i've been i've been with the encoder for the past six years and no booking.
Mateen Mohamed: Of the solution engineering department designing various analytic solutions or all I have what 20 years of experience, work with Oracle implementing Oracle applications doing performance tuning etc and worked as.
Mateen Mohamed: Your targeted media to their organizations so yeah so today i'll be joining with the MIC on the or the labia usc and then migration topic.
Mike Nader: So thank you, Sir, so what today's session is going to focus heavily on on away see and.
Mike Nader: And really the concept of connecting into let's say Oracle fusion, we will talk along the way, on a number of topics as they relate to pulling data into and working with data from these systems, whether it's premises base whether its cloud based so.
Mike Nader: we're going to be using Oracle analytic cloud and Oracle EBS and Oracle Fusion is examples, but in many respects the processes are going to talk about are going to be similar regardless of the source and regardless of the target.
Mike Nader: Now, when we're going through today, if you have any questions by all means, please just put those in the chat box, we will try to get to some of those in sequence, as we go through, and we will also get to.
Mike Nader: leave some time at the end for Q amp a so i'm going to walk through just a general overview just to set the stage from a.
Mike Nader: challenge perspective of integrating these technologies material jump in as we go along, but want to make this very conversational so again the team, if you have any.
Mike Nader: thing to add, you know, please.
Mike Nader: Point those out and stay tuned at the end for some announcements that we're going to have on upcoming events around.
Mike Nader: Oracle and go in and data pipelines in general, so I said, we have a question there somebody's got their hand up.
Mike Nader: Just take a look.
Mateen Mohamed: At it done Julie.
Mike Nader: If you don't mind just to avoid Mike any microphone dishes can you go ahead and put the questions directly in the chat.
Mike Nader: And I promise we will get to as many and I think that's the first one that's out there we'll get to as many of those as it all possible during the session today.
Mike Nader: And then, once if we can't get answer was bitter email and we'll send you a written response john, but we should be able to get to through most if not all of them.
Mike Nader: So when you know I think about integrating and the work that i've done over the years i've worked with you know EBS i've worked with SAP i've worked with jd edwards i've worked at peoplesoft.
Mike Nader: And i've worked with both cloud and premises based analytics environments.
Mike Nader: When I think about the nature of the challenges that we face and integrating the data or putting making the data relevant ready for the business teams.
Mike Nader: It is somewhat universal now you know the slide here talks specifically about EBS and EBS, as we know, was a very complex data environment, you know 50,000 ish table hundred and 30 hundred and 40 different subject areas.
Mike Nader: What you will hear consistently from a business perspective and we'll go back and forth a little bit you know this is maybe more of an IT conversation this slide that when I get to the next one.
Mike Nader: I want to talk about what it means for the business, but if I think about it from an integration perspective.
Mike Nader: What we're really talking about from our our four points are really difficult to achieve timely insights and what we mean by timely is that necessarily just fast fast is important talk about performance in the bottom, but also how relevant is the data coming in, as a 24 hours old.
Mike Nader: 36 hours old.
Mike Nader: Five minutes old what's the right level of latency so that.
Mike Nader: This is seems to support it with the information they need how much does it cost to model into to maintain that that data.
Mike Nader: What we've found in the industry and it's just not an end quarter state and it's more of a general one that.
Mike Nader: There is an enormous of course complexity to taking any of these crp environments and putting those in a fashion that's consumable from a you know, a standard business question perspective.
Mike Nader: and driving the cost out of that is material maintaining it budget but it's also material to the first point.
Mike Nader: timeliness somebody wants to make a change somebody has an unanticipated question this becomes.
Mike Nader: A key area focus I mentioned speed, a moment ago, from a performance perspective.
Mike Nader: I sort of look at this is table stakes users expect queries to be fast they expect their reports to come back timely.
Mike Nader: yeah as an example.
Mike Nader: On last client I worked with.
Mike Nader: Before you well as services client work with the expectation was that report should come back and under 10 seconds at 20 seconds users we're simply going to abandon the system in general and then we're going to start downloading the data and doing their work and excel.
Mike Nader: This isn't a tie writer and or push back against excel it's just really speak to the nature of the expectation that technology set for us.
Mike Nader: You can get the anything on your phone today in a matter of seconds data wise or or many things from your travel to your bank statements anything else that's expected from a an analytic system at work as well.
Mike Nader: And then, when we look at the larger tech stack most customers that in quarter works with aren't single GL single entity type organizations, so you have 10 sources 20 sources, we have one customer of ours that takes more than 50 sources and pull those together and and quarter.
Mike Nader: The The point is that not only from a data architecture or landscape from the source perspective, but then.
Mike Nader: is going to be diverse, but as we move out to the the consumption of that the delivery of information.
Mike Nader: you're going to have users, that are consuming that data with and excel consuming that data within power bi consuming that data within tablo and so in that data within.
Mike Nader: purpose built applications it just adds to a an ever increasing whatever increasing complexity of ever increasing costs as well in between, we think the you want to add me just to the four points here.
Mike Nader: Is this cover off on today.
Mateen Mohamed: Just sell want to add on the performance thing like sometimes as a workaround people go with maintaining.
Mateen Mohamed: aggregation of data at various levels, so that at runtime they can improve the performance but it comes with its own cost of maintaining and the latency of the data so that's not the.
Mateen Mohamed: solution to improve the performance so that's where it comes in, and then, as we go forward, we will expand how in quarter solve the performance issue.
Mike Nader: yeah cluster.
Mike Nader: Right.
Mike Nader: And we'll get into more of this as we go along, but the way we tend to think about this, and again.
Mike Nader: sort of an IT conversations to have but stay tuned we're going to draw this down to the business a little bit think about the variety of sources, the myriad sources out there.
Mike Nader: Whether those are in, you know as an example netsuite Oracle workday SAP salesforce and scores of other systems.
Mike Nader: The process that we have been trained, is it personal to follow over 2030 years or longer is and i'll put these more modern terms you're going to pull the data into a data lake.
Mike Nader: In this particular case we'll call it the raw data zone and we're extract that and you're going to have 100% of the data sitting there.
Mike Nader: In its format that's a pretty normal thing that we refine that data in the warehousing environment we further refine that.
Mike Nader: And curated in the you know the data martyr business own and then, finally, we hand that out to the of the variety of destinations, whether that straight analysis, whether that is an upstream system like an oracle EP vcs or FCC s or or one stream or Anna plan or.
Mike Nader: A procurement IDP from a demand planning perspective there's going to be any number of targets over there, but what you will notice along the way, is you start with 100% of the fidelity.
Mike Nader: You go down to 40 you're going to end up 10 or 20% and really what we're talking about, then it goes back to my previous point on aggregations.
Mike Nader: it's what we're trying to do courses answer a set of business questions, and as we get more and more focused to the right of the diagram you get fewer and fewer questions, but the systems are designed for those.
Mike Nader: The negative of that, of course, is when you have an unanticipated question I need something that's in the 40% well that's not in the business data is on I gotta go back.
Mike Nader: If I need something that's in the 80% on that's in the raw data zone I gotta go back and i've got to do rework and what that really looks like from a business perspective is this and.
Mike Nader: i've suffered from this i'm sure any business user it's on the session today what's the recording or frankly, if you're an IT person talk to your business teams, this is a process they're very, very familiar with.
Mike Nader: Because they'll have a question let's say during the month and close cycle, they need to do some analysis on how to balance situation or look down for a reconciliation on a number of transactions.
Mike Nader: You get on with it you're asking for information, you might get lucky, and it could be something that's available now very often it is, I can maybe give you a data dump but to really do this in a more repeatable fashion we've got a number of weeks of work to do.
Mike Nader: And then it goes back to a standard process.
Mike Nader: And then you're going to do it all over again constantly as new questions arise, and of course we don't anticipate all the questions.
Mike Nader: One came up a couple of weeks ago I was having a conversation with an existing customer and I just happen to mention a personal note, I wanted to buy an air fryer and then we're getting more expensive because it happened to be the same week that palm oil exports were curved in Indonesia.
Mike Nader: Which is the vast majority of or a large supply of a vegetable based oil are plant based oil extracts are using cooking globally.
Mike Nader: That puts global pressure on olive oil on corn oil and everything else, and an air fryers are tied up in Shanghai because of covert lockdowns I mean those are the kind of questions if somebody gets into.
Mike Nader: I have a business that depends on palm while or set of cooking oils, to do something well now my material cost is going up, and that was done unexpectedly you can't anticipate what the markets, going to cause you to ask or cause you to analyze.
Mike Nader: This process, in short, really doesn't support agility to respond to those types of questions.
Mike Nader: matina anything you want to throw in here.
Mateen Mohamed: yeah on the previous slide down my once you get to the aggregate level it's becomes very difficult to drill down back to the transaction if somebody wants to know.
Mateen Mohamed: The detail.
Mike Nader: transaction so.
Mateen Mohamed: That that's one area where in kota or solves this issue as well.
Mike Nader: So when we talk about in quarter, you know I tend to talk about it really in these six ways to about relevant insights simplified data access and so on.
Mike Nader: And I just i'm not going to read the slide we can make these available, but what I wanted just key in on.
Mike Nader: Some of the terminology, or what I mean relevant insights the focus and relevancy is about how frequently users get the data, not just about the speed and not just about the right metric on the screen.
Mike Nader: If I have the data in the environment let's have 100% of the data I can make any metric for you very quickly, generally speaking, potentially.
Mike Nader: But it might getting the right information in the right order of time, am I getting an incremental feed every five minutes something i'm quarter supports and something that I will harp on over and over again, because.
Mike Nader: While not every single table and every single piece of information needs to be fed in in today or into our.
Mike Nader: Critical pieces of information do have to be and that's often overlooked that process 24 hours late and.
Mike Nader: Sometimes is means the data is not relevant, a longer because that that decision you have to make is perishable and you have to make it, whether you have some of the data or all of the data you're talking about simplifying access.
Mike Nader: that's going to be about taking that that data supply chain which you just saw on the previous slide going through these steps, but instead of going through these steps simplifying the steps down.
Mike Nader: Speed, as I said earlier that becomes a matter of table stakes, but i'll say it again you've got to have the relevancy from the transactions occurred.
Mike Nader: it's flagged and it is included in your analysis as fast as possible to make it as relevant as possible, so incremental matters and will continue to matter.
Mike Nader: seamless drill down moving from a top line number if i'm trying to look at.
Mike Nader: It was using a balanced example earlier or I want to understand what is driving a certain set of.
Mike Nader: Of GA costs, out of line, and I want to drill down to the transactions, I shouldn't be able to do that seamlessly out of the box.
Mike Nader: I shouldn't have to add requested them or have something else built in the background, it should just be part of the experience.
Mike Nader: And then security of course record level low level in quarters built that way, but that last point on the slide and this goes really good the complexity of the environments and just.
Mike Nader: The continual expansion of technologies and platforms, if people use either source data or consume it, it is a about a multi source need.
Mike Nader: In quarter was built from the ground up to handle multiple sources multiple earpiece combination of the RPS and flat file pulling in industry data and mixing that with with the others.
Mike Nader: It is native to the functionality in court has built and every client that i've worked with it in kota and I can think of frankly.
Mike Nader: Is leveraging and coordinate and a multi source environment, I mean some greater than 50 next one, my mind over 60 different sources pulled together and and quarter, but machine you're nodding as as something else you wanted to throw under.
Mateen Mohamed: yeah now and I spirit I was.
Mateen Mohamed: mentioning that see in quarter, we do not for extracting the data from the source, we do not run complex joints on the data source we extract the data as it is table by.
Mateen Mohamed: table and then be computer joints on autopilot hence it's very.
Mateen Mohamed: fast to the extraction and the instrumentals or also can be run faster and too often depending upon 28 so and also like we do I mean we can pass some intertek try to do the parallel extraction of the data.
Mateen Mohamed: So these are some of the things related to them how we achieve the speed in the extraction of data from the soaps, whereas in other applications they run the complex joint a tech job, while running the extract.
Mateen Mohamed: yeah.
Mike Nader: So you know matina i'll throw this over to you again, but in just a minute, but think about you know the difference in process right now and again i'm talking more of the technology piece will will go back over to the business piece momentarily but.
Mike Nader: In quarters focus and the way we operate take the data from the various source systems and we will replicate.
Mike Nader: As much of that data as as relevant, and I say, that is, do you need 20 years of history, for your analysis is that what you're looking to do, or you want the current year and three prior but will replicate 100%.
Mike Nader: From fidelity perspective of that data as as desired pull it into the important platform and we make it available in a simplified fashion semantic fashion.
Mike Nader: To business users, whether that's on the finance side on human resources supply chain HR and the hcm perspective.
Mike Nader: manufacturing, there are a whole host of options from a data subject area so think of it as consume merge the the data sets together.
Mike Nader: letting quarter understand the relationships on a day to sell by day to sell so users can ask questions anticipated or on anticipated and then get their answers timely.
Mike Nader: Now.
Mike Nader: machine i'll let you talk a little on this process and respect to the EBS applications, if you don't mind and.
Mike Nader: you're doing.
Mike Nader: You know, essentially Oracle aarp cloud and Oracle EBS premises base it's a pretty common use case that our customers come to us for.
Mateen Mohamed: yeah me having the pre built.
Mateen Mohamed: Little Prince onboard the Oracle EBS and or the rp on for various business areas and modules, for example, be covered.
Mateen Mohamed: The supply chain, the order to cash and procure to pay and i'll give a brief DEMO and overview of what the content, which we have pre built and most of the time we have noticed that during the deployment of in Qatar with our people.
Mateen Mohamed: blueprints 80 to 90% there is a fit and sometimes the restaurant or 40% customizations they need to do, but all pretty much 80% we power, the content.
Mateen Mohamed: And we have developed a connector to get the data from the Oracle cloud era P, so we get it through the Oracle bi CC use em so or cooper's the data into the ucs storage and then we pull it.
Mateen Mohamed: And we identify the various data types columns keys, etc, so we have customers who uses as a multi source both Oracle EBS and Oracle cloud so i'll go into the DEMO in a bit, can you go to the next slide.
Mike Nader: Absolutely so.
Mike Nader: The way you know, to give an example of some of the things we've been talking through and i'm a team was just walking through a example of how we pull Oracle EBS and premises based or excuse me fusion cloud and on premise based Oracle EBS together.
Mike Nader: I want to make a broader point in respect to in just i'm using one example here from one portion of a business and given my sort of.
Mike Nader: background and building officer finance solutions, I was thinking more along those lines, is that you know activity in an organization activity and accompany drives your data.
Mike Nader: The.
Mike Nader: Data process that pipeline we've been talking about doesn't often, unfortunately, support the activity in.
Mike Nader: And really if I boil you know finance you department finance whether that is corporate or divisional or supply chain versus fba let's just boil it down to really three key areas.
Mike Nader: How much money do I have working capital what am I going to end up with what's my operational capacity to support that.
Mike Nader: And to grow it and what's my profitability against it, and I can go through hundreds of thousands of metrics and they'll feed into those buckets generally speaking.
Mike Nader: But anything you do from an analysis perspective it's it's going to be driven from your orders your capital assets your wages projects your bank statements, all of this is going to be necessary.
Mike Nader: To really feed that in and a lot of that information is also going to come out of your consolidation system your forecast your plan your reconciliation environments and a whole host of vendors that that focus on and do that.
Mike Nader: But that middle that's where a lot of the effort is spent, and when I think about where it is in quarter provide the most value.
Mike Nader: For our for business teams and, frankly, for it organizations as well is bridging the gap between the far left of this diagram.
Mike Nader: And then the user consumption or even system consumption on the far right of it, it really speaks to something it looks more like this.
Mike Nader: Whereas in quarter becomes that environment we hold the transactions if we're going to keep with our.
Mike Nader: With our finance example think about all the transactions to support your sub ledger accounting processes.
Mike Nader: And the day and, frankly, the operational feeds and other fields that need to come in to provide further research around it.
Mike Nader: And quarter pulls all of that, together, allows you to do direct access on it from a financial perspective.
Mike Nader: But but also allow you to have that data feed into your forecast system.
Mike Nader: Provide feeds and help in the clothes not we're not a closed platform, but if you need information or a GL balances feed or you need to come into the sub ledger.
Mike Nader: From a reconciliations perspective and look at things in quarter provides that access and can provide the information out and it really becomes a practical application of all we're talking about in regard to instead of walking all those individual steps that process before.
Mike Nader: Making quarter part of the larger data strategy we're not saying you have to remove all of your warehouse you may have invested already in your data lake and your data warehouse in a number of parts of that data pipeline all important.
Mike Nader: What encoded becomes is that layer that allows you to very quickly deploy those data sets out to business communities and frankly merge.
Mike Nader: Those critical business systems together and part of that can be reading from your warehouses and from your your other is of the data strategy that you've got in place.
Mike Nader: And one of my very favorite diagram slides is the one you've done in the spring, here, and what I liked about this is.
Mike Nader: That what it in terms of the fact that a customer of ours, made it for us, we didn't make this slide this was the realization of what they did in the past and where they've moved to now.
Mike Nader: And again, I don't want you to think oh I can't have a warehouse and in court it's not the point.
Mike Nader: One is if every question that a business user asks requires you to walk through the pipeline on the left and every new question that you haven't planned for that it's going to take you an enormous amount of time to support a never ending supply of questions.
Mike Nader: Whereas on the right, what this particular customer was able to do within quarter was take what was about a six month process let's say four to six months and turn that into one to two weeks.
Mike Nader: While they were still doing some things on the left, because they have a data strategy, they were following and they wanted to start pull things together, in a way that apply their master data applies a level of of.
Mike Nader: Unified your risks to the information but it's that difference and that agility in the analysis is that is what allows your organization to move forward, and again I love this because we didn't create this, this is a customer story and the reflected back to us as why.
Mike Nader: The team any thoughts or to sign on.
Mateen Mohamed: yeah I mean you're mentioning the two minutes ago about the conciliation.
Mateen Mohamed: yeah I mean it becomes very important during, particularly the period closes and a quarter process and then, if somebody wants to know what is the my main GL.
Mateen Mohamed: And how it is made up of Sub ledger entries so in in court out, we have various ways of drilling down from the general ledger to the sub ledger sub sector so that helps in particularly during those and off your end of quarter closing.
Mike Nader: yeah and I think you got to show some of that the DEMO here which we're going to get.
Mateen Mohamed: here.
Mike Nader: So i'm going to go back to this diagram we showed earlier, this was the more traditional way what machines going to show you today is something that looks more in depth.
Mike Nader: And it's a much more agile way to approach supplying data for those on demand questions was more agile questions.
Mike Nader: And it's a very much as a different process.
Mike Nader: So.
Mike Nader: disparate technologies data visibility availability agility self service, those are really the themes that you're going to run across.
Mike Nader: And we're focusing pretty heavily today in respect to our examples on Oracle EBS.
Mike Nader: Fusion and then you know migration always see and looking at you know hey, these are the kind of problems, you need to solve but, regardless of the source and regardless of the target it's going to be a similar conversation.
Mateen Mohamed: and also my.
Mateen Mohamed: Men, some of the customers, they may have the EBS and then move to fusion or the power, the rp not all the models sometimes they don't go with the GL with a fusion and also then maintaining the two different.
Mateen Mohamed: environments vitamins and then now with the obe and we have seen Presidents lot of challenges Medical School yeah.
Mike Nader: Absolutely, I mean we're we're focused on so we'll talk about and we'll show in the demonstration the instant transaction level drill down.
Mike Nader: Quick rendering of analysis table stakes there we'll talk about adding new data yeah we've been convinced between to pull something up and throw file and not I don't know if we've got.
Mike Nader: To something readily available religion show how we put the data in and then because we don't change the nature of it that we have that transactional fidelity you always have the ability to go from.
Mike Nader: here's my aggregate gross margin cost of goods sold down to the supporting transactions and see what posted.
Mike Nader: So you've got instant access to the more accurate results with an eye on I need to cut down internal audit costs or I just simply need to speed up the amount or reduce the amount of effort, my team's put in.
Mike Nader: To doing operational and you know financial analysis as an example on a monthly basis i've worked with clients that spend.
Mike Nader: Thousands 5000 8000 plus hours monthly just pulling the data together to present those reports to the various business teams that isn't sustainable.
Mike Nader: So with that between one and I let you go you take it away the blueprints i'll stop sharing that you pick it up, you can walk through.
Mike Nader: The demonstration i'll come back and just summarize a few things in a blueprint base.
Mateen Mohamed: Sure thanks Mike all, let me share my screen.
Mateen Mohamed: yeah this is so, or DEMO and got our environment connecting to Oracle EBS.
Mateen Mohamed: DEMO vision database, so how we extract data is the initially define a data source connecting to, for example, in case of EBS.
Mateen Mohamed: So we can have a university connection to the actual Oracle database connecting through Apps or somebody only user and then, once we have the connection, what we do is we create to various physical schemas and then, for example, we have.
Mateen Mohamed: Created the equivalent of for Oracle EBS schemas, for example, for the accounts payable ap for receivables er et cetera and within each of these schema.
Mateen Mohamed: We have created the underlying the database tables for example if you're familiar with the financial the customer transaction customer transaction line yielded switch and etc.
Mateen Mohamed: So we individually extract all the tables and then, once we have the data within the physical schema then we create the joints within the.
Mateen Mohamed: schema so basically, for example, if you look at our customer to our X all and it will have joints and define to our next lines all n GL distribution all etc, as the child tables.
Mateen Mohamed: So, once we have the data extracted and the joints are pre computed etc within the quarter, then we have a layer, which is a semantic layer or business schema it's nothing but a virtual.
Mateen Mohamed: warehouse you can say and to hide the complexity of the physical tables schemas and columns etc we create this business layer, and it is exposed to the.
Mateen Mohamed: business community to create dashboards on demand, for example, let me open this order management business view so it has been brought in all the dimensions for the calendar with position item.
Mateen Mohamed: Customers etc, and then also be have the transactions, the sales order header sales order lines, etc, and then we have P computed the some of the metrics that users are interested in, for example, what is the on time percent and.
Mateen Mohamed: In there are various measures which we have created based on the other, physical columns and then put it into this side it is.
Mateen Mohamed: made available to the business community of.
Mateen Mohamed: So, and then the various if you want to do some bucketing the age bracket so we have created various formulas are technologists our pre built.
Mateen Mohamed: content is pretty much covers 80 to 90% of the business need so once you have the business schema then users can go ahead and.
Mateen Mohamed: Create your dashboards and insights I will walk you through some of the content, which we have developed as a template.
Mateen Mohamed: So we have covered the various modules the financial supply chain and value chain, etc, and within the supply chain of, for example, if you look at the order management, we have covered.
Mateen Mohamed: areas in the booking backlog and billing so you will see all the metrics here, so the general.
Mateen Mohamed: pattern is we create all the metrics on the top and then some aggregation and then the detailed level transactions.
Mateen Mohamed: So you have all the information about the booking then billing and backlog, for example, if you want to.
Mateen Mohamed: Have a filter on a particular customer, for example, if you want to know what's going on with the imaging innovations.
Mateen Mohamed: Then you will see the details here and then also if you go to we have created various tabs etc.
Mateen Mohamed: To get the information about the shipping the returns and the billing etc so everything will be filtered out the based on the filter which you have done in the initial tab so that way you will you can focus on particular customer sector too low, if any discrepancy is there.
Mateen Mohamed: Let me walk you through one.
Mateen Mohamed: Use case.
Mateen Mohamed: Which is typically.
Mateen Mohamed: helpful during the reconciliation process, so we have within the financials so the GL module and then we have created some reconciliation dashboards that links to the sub ledger's, for example, if I take the account summary so within that we have this inside.
Mateen Mohamed: Which details out if there is any variance so this month, is it right, so there is a variance between the amount some posted in the general ledger and in the sub ledger.
Mateen Mohamed: So now, this is from the consultants summary not for what will drill down to what exactly is happening, why there is a difference, then, for example, let me see unearned revenue, so there is a difference here, so I can.
Mateen Mohamed: drill down from here this account to the actual air account transactions, then it will list out.
Mateen Mohamed: What is there in shield, but not in the sub ledger Similarly, if there are any on poster to journals so there seems to be unflustered journals within this year so that's the reason there is a discrepancy of the amount, so we have a similarly created the various.
Mateen Mohamed: Reconciliation dashboards.
Mateen Mohamed: And then, our content covers various modules the.
Mateen Mohamed: Core financial the payables receivables general ledger and then, if you look at on the supply chain side, we have a purchasing order management and inventory, etc.
Mateen Mohamed: So, particularly the purchasing the the procure to pay cycle, it covers from basically from the requisition to purchasing.
Mateen Mohamed: to getting the items inventory and then paying to the vendors so i'll walk you through one of the Sir dashboard, which is the procure to pay summary so here is the business flow that it covers from bill position, all the way to the paying to the supplier.
Mateen Mohamed: So yeah we have all the details on the various metrics on the requisition the open purchase orders and purchase order details, etc.
Mateen Mohamed: On one more thing which I wanted to show you is about as Mike mentioned the Multi source, so there are customers who use us both the EBS as well as.
Mateen Mohamed: Put examples of fusion, I mean within the Oracle landscape, there may be other customers will be using some of the recipe for some functional areas, etc, but let me show you a simple example where the data from the EBS and the fusion is brought together as multi source into one.
Mateen Mohamed: environment so.
Mateen Mohamed: He log in here.
Mateen Mohamed: Alright, so in this environment to I have a data coming from both a fusion and Oracle EBS, so this is one of the dashboard where the customer has implemented the general ledger for the fusion and the simulators are still in the EBS so this.
Mateen Mohamed: detailed data is.
Mateen Mohamed: about the various sub ledger details, so if I want to drill down from this sub ledger detail to the actual EBS transactions, then I can we have this.
Mateen Mohamed: drill down capability, so what we have done here is at the general general level, we have the data from the fusion coming up, and then to get to the publisher details we have.
Mateen Mohamed: This written down, and then we can, for example, if you look at it, there are.
Mateen Mohamed: For this particular agenda there are 10 journal lines, so if I go to the actual UBS er has action, so you will see this, there are 10 lines, and it will give you all the details, how much is the.
Mateen Mohamed: debit account or credit account etc, so the because we have noticed that, while upgrading from the EBS to fusion or Oracle yet the cloud.
Mateen Mohamed: Of sometimes the customers go by module by module initially they started with the general ledger and keeping all the sub ledger system in the EBS so we have Bob what I saw was this and then again, you can also, for example, if you have other sources of not only Oracle EBS or Oracle.
Mateen Mohamed: cloud era P, for example, for human resources, etc, if you have some other application, then we can bring that data as well, and then link it by common entities common dimension so between all these data sources, and then you can build a dashboard and insights.
Mateen Mohamed: Side by side.
Mike Nader: salting no money want to put you on the spot, with a couple questions there.
Mateen Mohamed: sure.
Mike Nader: grow a business perspective.
Mike Nader: yeah what you were just showing that the drill down.
Mike Nader: How much configuration, did you do.
Mike Nader: To set that up when you when you deploy this.
Mike Nader: Nobody was at all custom built part of blueprints.
Mateen Mohamed: So there's very little configuration is needed, so we have just the data sources connecting to both the fusion and Oracle EBS and then we have our defined some common dimensions, for example, the.
Mateen Mohamed: ledger the calendar, etc, which are common to both the EBS and to the Oracle fusion cloud, and then we have defined the joints between those.
Mateen Mohamed: tables and schemas and then build this.
Mateen Mohamed: So it's a very minimal configuration that is needed from the physical perspective and from a business perspective that they don't need to do anything just bring the data from the various what schemas.
Mike Nader: 17 on that, I mean just to be clear, just to be clear.
Mike Nader: What I want to do is.
Mike Nader: To you know sorry about it wrong button there to be you know just make sure that I understood from a configuration perspective, you talked about the come on common dimension all that's already done it's.
Mike Nader: Sitting inside of blueprints that are provided by by in quarter.
Mike Nader: One of the things that would also be interesting and it's much more of an IT kind of question or maybe request and you may already be doing this in the DEMO is.
Mike Nader: How can I show, I mean, can you show the complexity what it would look what it does look like inside of the recording engine.
Mike Nader: to merge those together, I don't mean complexity like it's hard, but that incorporated is solving a very complex problem in a very simple way and merging these resources together.
Mateen Mohamed: yeah I mean I can show you, for example, this is on the.
Mateen Mohamed: EBS side, for example, this on dashboard is built for the pod seeds so.
Mateen Mohamed: I can show you the complexity behind this dashboard, for example, the query plan so how it is physically map, so we have about.
Mateen Mohamed: The schema on the customers and suppliers, so you are so suppliers and then the actual ESP or so does the query plan so i've been.
Mateen Mohamed: Behind the scene, all these tables are joy within in quarter, based on their primary key foreign key references, and so this is one thing and then for the actual.
Mateen Mohamed: Fusion and Oracle EBS let me a dog going into another environment and show you the details, the actual query plan so here, I have some issues showing the query plan me out here.
Mateen Mohamed: Okay, let me go to this.
Mateen Mohamed: To the car so i'll show you the actual query plan how it looks like.
Mateen Mohamed: So you see, here we have from the fusion, there is a calendar for the fiscal period, etc, and some for the ledger and then from the EBS we have all the details.
Mateen Mohamed: You know this is Finn common from the accounting, the link is between the.
Mateen Mohamed: chart of accounts are down, and from the fusion or the general ledger and fiscal calendar that sector, so the common link between these two is by defining a joined between the account and then, when I go drill down from here to.
Mateen Mohamed: UBS er here, I will show you the link.
Mateen Mohamed: So here, you see, on the link between both the EBS and the fusion, it is going through the sub ledger.
Mateen Mohamed: table which we have created and it joins both the real journals from the fusion and the actual er transactions, which is the sub ledger by the offer SLA tables which i'm sure the archives miscommunications very familiar with.
Mike Nader: Exactly, so I get my you know, one of the reasons I asked that question is, you know from an IT perspective which you're just looking at there is potentially billions of transactions and even in our particular DEMO environment it's.
Mike Nader: hundreds and hundreds of millions, if not billions of transactions.
Mike Nader: dynamically being aggregated and present it up from the business team you don't have to build that you don't have to build the calculations you don't have to build the schema team is just showing.
Mike Nader: You have access to the questions.
Mike Nader: And, and you have built something new, it is presented in a very simplified way.
Mike Nader: So if you were doing a new analysis, it would be very easy to generate something I mean, I know I sidetracked you i'll let you get back to it, I know we've got about.
Mike Nader: 15 minutes left, I want to reserve pivot the last seven eight minutes for any questions we may have so i'll let you jump back in here new DEMO.
Mateen Mohamed: yeah I was just mentioning that, like all those complexities hidden behind.
Mateen Mohamed: The business schema so from the user perspective, they just need to drag and drop for various dimensions and various metrics or subtract from our business schema, which is a virtual data warehouse a photo sizzle yeah.
Mateen Mohamed: Any questions i'm pretty much done with our DEMO here Mike.
Mateen Mohamed: Can.
Mateen Mohamed: We can go back.
Mike Nader: And I will go ahead and take that back just stop sharing i'll share again yeah.
Mike Nader: we'll jump into the.
Mike Nader: You know, really a summary of the blueprints are went through us give me a second here.
Mike Nader: So you'll hear us talk to them in court about blueprints he's a really business analytic accelerators.
Mike Nader: That allow you to do you know you've got fusion cloud and premises CBS as the example the team was just walking through you have an accelerator for that and that's what.
Mike Nader: We have an accelerator for that SAP we have accelerators for that, and even the combination of any of those systems together.
Mike Nader: But we're focusing directly on Oracle today, in particular, this is a sampling of the types of accelerators this analytic accelerates provide on the ap side ar van supply chain management inventory procurement and spend really focused on supporting your key this is processes.
Mike Nader: And you know as an example and in a lot of words, in the slide but the point is just some examples of the type of analysis that are just provided to you.
Mike Nader: And that you can extend either from a business team perspective or, if you have a sore a centralized analysis team, they can easily extend and work with.
Mike Nader: But.
Mike Nader: What we wanted to get into and just kind of provide really you know one example, there may be two of things that didn't our customers have done within quarter.
Mike Nader: And for sake of honoring our nda we won't put you know the customer names out, but you go from.
Mike Nader: One refresh a day of data for not even a day, sometimes 36 hours to this particular customer gets data in because incremental matters 96 times a day.
Mike Nader: Their user Community instead of simply downloading and leveraging the data and offline fashion.
Mike Nader: got is now gotten used to engaging with the information when they want it not radically different than when we engage on our phones but i'm looking for something they go to one quarter to get them and frankly we've got it and a 99%.
Mike Nader: Improvement i'll call approval rating on the work that was done within quarterly, and this is not a small organization it's in the 10s of billions of dollars of.
Mike Nader: worth they are for new questions we talked about that data process they're able to do that, generally speaking, in less than a day because their teams replicating.
Mike Nader: The broad access or the broad data sets and then simply know something's missing and that semantic with a business rather than machine machine extract the column in name of the way and let the users now have access to it.
Mike Nader: And they're big three challenges they wanted to solve or they wanted to minimize the cost, both in hard dollars in time spent in the atl or frankly even ality elt process, they wanted to look at a.
Mike Nader: An alternative to Oracle business intelligence application so we got a.
Mike Nader: In that.
Mike Nader: What they had premises base was disappearing and, frankly, when they looked at obe no way see their intention was well.
Mike Nader: We want something that's more dynamic, more agile, in a way, we can we can leverage that and in quarter really help them tick all the boxes and just the fashion with walking through today.
Mike Nader: Another example.
Mike Nader: From one of our.
Mike Nader: Our newer customers.
Mike Nader: And this is was their experience in instead of leveraging alessi.
Mike Nader: They went down the path of encarta.
Mike Nader: set of taking eight weeks to build a report now they do this in in days.
Mike Nader: Instead of weeks and months.
Mike Nader: So i'm gonna leave it at that, for now, I know we have we're running right to about eight minutes left in the hour, I do want to reserve some time for questions.
Mike Nader: I have gotten a few direct questions have been sent over the moderator or the you know the direct feeds on the check to see if any other questions have come in and i'll jump over to the.
Mike Nader: chat Okay, so no other questions i'm going to go with the for that that we got and I got directly submitted and in between, please take a look on the under chat as well.
Mike Nader: First question that's come up is you we've been talking about on demand or or agility essentially what the question was you talked about on demand analysis early.
Mike Nader: We run into a problem of impromptu report data request what's the reality of a true business user building and reusing report.
Mike Nader: i'll give my answer and minimum a team that you've been doing this longer than I have an encoder.
Mike Nader: How complex is that if you work today to take somebody that is forget the joints forget the scheme I forget the data complexity they're going to build something how hard, is it to build something in your opinion.
Mateen Mohamed: Sorry i'm late, I missed that.
Mike Nader: On on the report side you're a power user business user.
Mike Nader: I want to build a new piece of analysis.
Mike Nader: Right, how you know how hard, I mean I gotta get my answer momentarily but i'd love to get your take on it first.
Mateen Mohamed: yeah I mean no well, it is very simple, like on demand analytics since we have already pre.
Mateen Mohamed: built the business schemas and it hides all the complexity of the physical schemas etc, so they can simply drag and drop columns from the various.
Mateen Mohamed: Business areas so because we have segregated over content in the logical layer by various business areas so client, it can be granted access to appropriate business users and they can build dashboards very creepy yeah.
Mike Nader: So my experience answer the same question and in quarter and i've dived deployed a variety of technologies Oracle non Oracle.
Mike Nader: From an analysis perspective and from a power user they want to build their own reports perspective and call it report call it a dashboard call it analysis I don't really want to split hairs between those today, and you can get a longer conversation.
Mike Nader: But because in quarter deploys the data out in a simplified structure i've got all i've got a billion transactions.
Mike Nader: But what i'm looking at when I go to one of those published subject areas is here, are they 10 measures on one and if I want to calculate another it's very excel like an APP regard if I want to do that.
Mike Nader: Here are the dimensions, I want to use I drag and drop those and so you've got.
Mike Nader: 30 some odd different visualization types built into one quarter pivot tables aggregate tables, but just as importantly in quarter allows you to if you're a power bi customer use power bi.
Mike Nader: Against the quarter that's fine for tablo and that's your ui of choice used to have level against power bi.
Mike Nader: encoder allows external natively allows external reporting and visualization tools to talk to our engine to talk to our data structures can pull it out.
Mike Nader: And from an IT answer on that we emulate postgres for nosql internally, but the point is it's simple, it would be no different than connecting power bi can use that one as an example, into a data source.
Mike Nader: And then, working with it.
Mateen Mohamed: I mean, in simple words might incorporate acts as an in memory database Pablo sets kind of tools yeah.
Mike Nader: next question I got my team is good, we talked to can you secure the data and we talked a little about security at least i'm not.
Mateen Mohamed: Sure yeah.
Mike Nader: But it was security specifically.
Mateen Mohamed: yeah and.
Mike Nader: aligning the light source applications.
Mike Nader: To the users, how do we do that.
Mateen Mohamed: yeah so i'm in now, as most of the users Member from the Oracle application some backgrounds and then are the source on the Oracle applications, whether EBS or.
Mateen Mohamed: cloudy rp so Oracle secures that data access by various responsibilities and in the Multi our concept by all baggies etc so in in quarter, we also secured the data.
Mateen Mohamed: By users access to those responsibilities we bring those responsibilities and the various organizations that user have access to, and then we put security filters at the object level, so when I log in.
Mateen Mohamed: I get to my multi org ID and the responsibility ID and the data is filtered by those columns and I see my own data, so the data access is very important and then different users.
Mateen Mohamed: They see what they have access to their data only they do not see the overall data so yeah maybe honor that application source that related to security mechanism.
Mike Nader: yeah yeah I mean you know layman's terms you get to see what you get seen the source systems and we can wrap well yeah they're connected with directly.
Mike Nader: And allow and encode automatically replicates user access like against dbs and other systems, because we have the the transaction same tables infidelity we can secure it the same way, so.
Mike Nader: yeah i've designed security systems i've spent eight weeks on a design yeah i've also designed for in court i've never spent more than about two days on a design it's pretty quick process.
Mike Nader: I think we have time for maybe two more questions, and I do want to put a plug in for our upcoming data pipeline.
Mike Nader: So event for me, but let me just run these last two questions that we've got and I know there's a few more than a trickle, then I will will provide written answers to those folks that that sent them over.
Mike Nader: machine any examples you can just quote quickly if top of your head on performance improvements compared and let's just.
Mike Nader: Report, or even two things i'm going to add to this question, what do you have you seen from a performance improvement from just a query retrieval and what have you seen from performance improvement and I got to build a subject area and in quarter versus like say obe array see.
Mateen Mohamed: yeah man, we have some real examples one example, like one of the larger social media company, they were using the Oracle or bi for the accounts receivable module and their aging.
Mateen Mohamed: During the period pros particularly they needed to have the ar aging report and within the rbi it was taking more than an hour to do all the.
Mateen Mohamed: The aggregation etc, and to particularly while applying the fields, etc, whereas in in quarter when we converted it to in quarter then within.
Mateen Mohamed: Five six minutes because the data and the dashboard was built and rendering the data so since I mean done the main advantage of in court is we do not do the complex, multi level joins on the source at all, we do not.
Mateen Mohamed: Keep the data warehousing so since the giants are prepared.
Mateen Mohamed: And then in memory and so that studies and now it is very fast yeah.
Mike Nader: So there was again so there's just three or four other questions that have come in just for sake of time and energy to be cognizant of everybody's calendar.
Mike Nader: we're going to take those I will return, and I will write those up and again send those responses over to folks but as we finish up and again thank you so much for giving us the opportunity to present today.
Mike Nader: In to talk a little about the platform happy to get on it and have one on one sessions, I truly I firmly believe in the technology, having worked in.
Mike Nader: A variety of roles i've never worked with a thing that works, the way in quarter this, but the last thing I will put in is just a conversation, or at least a.
Mike Nader: Stop the share here for a second because i'm i've been wanting to get my.
Mike Nader: There we go i'll put that back up on the screen.
Mike Nader: I want to.
Mike Nader: just throw in conversation, or at least a plug for two things if you want to try and quarter in quarter has a cloud platform and you were as soon as this pops up onto the screen here we'll talk about.
Mike Nader: You if you want to come in come into cloud encoding calm and you can sign up, it is.
Mike Nader: You can sign up for free trial work with your data on it and work with some of our folks to think about what problems we can help you solve and also very important to us upcoming in this is an event that I think we have.
Mike Nader: 3000 plus people registered, for it is a virtual event talking about data pipelines.
Mike Nader: we've got a variety of noted keynote speakers, not the least of which is Thomas Korean from Google speaking and among of among dozens of other presenters.
Mike Nader: That is coming up on the 26th that we encourage you to register for this, you can find the registration on our website, or if you just look for zero gravity registration.
Mike Nader: and your favorite web browser machine to get to the right page, and again I appreciate everybody's time today if there are any further questions i'm happy to hang out and answer those but i'm going to go ahead and pause stop sharing here and then we'll let the session and.
Mike Nader: Have a good rest of your afternoon.
Mateen Mohamed: yeah thank Thank you everybody on.
VP, Business Analytics Solutions
Technical Director, Solution Engineering