5 Key Considerations for Upgrading OBIEE to a Modern BI Solution

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Nick Jewell: Welcome everybody to this webinar key considerations for upgrading obe.

Nick Jewell: To a modern bi solution, we are going to let everyone in the audience trickle in and we'll go ahead and get started in just two minutes time, but while we wait.

Nick Jewell: A little housekeeping So if you have any questions during the presentation today, please type them into the Q amp a box that's provided by zoom and we'll do our best to address these at the end, during our q&a session Thank you so again we'll be starting in just two minutes time.

Nick Jewell: Fantastic welcome everybody so once again, this is the webinar for key considerations for upgrading Ob IE to a modern bi solution.

Nick Jewell: we'll get started in just one minutes time we'll just let everybody joined the webinar and get settled before we begin.

Nick Jewell: And again, to reiterate a little housekeeping if you have any questions during today's presentation drop them into the Q amp a box and we'll do our best to address them at the end of today's session so we'll get started in just one minute time.

Nick Jewell: Alright let's get this webinar underway so we're going to be discussing key considerations for upgrading Ob to a modern bi solution.

Nick Jewell: My name is Nick jewel i'm in quarters senior director of product marketing and i'll be introducing our topic today and moderating the Q amp a towards the end of the session

Nick Jewell: i'd also like to introduce David Bach away who's our senior sales engineer in the UK and he's going to be sharing with us a fantastic practical demonstration later in today's webinar so let's get things underway.

Nick Jewell: I will start by taking a look at some of the key challenges that our customers face when it comes to working with legacy reporting environments.

Nick Jewell: And where upgrades inside an existing vendors stack don't always offer much improvement so.

Nick Jewell: First up it's often really difficult to achieve timely insights against operational data and Ob requires a traditional star schema in order to present data to users.

Nick Jewell: And with that comes all the usual data pipelines atl data engineering needed to load incremental changes from source Oracle environments.

Nick Jewell: into your analytics layer now, as you can imagine, this is a slow process, both to build.

Nick Jewell: but also to run multiple times per day, and in fact most customers resigned themselves to the fact that Ob reports are usually run on an overnight batch and the data will be, by definition, somewhat stale when it arrives.

Nick Jewell: Talking about star schemas it turns out it's actually pretty hard to model, the data that comes from these complex business applications as well with obe and also with.

Nick Jewell: A see we have an admin tool that we typically use to design the logical the physical the presentation layers about design.

Nick Jewell: And this really isn't a user friendly tool it's really complex it's got lots of functionality that almost gets in the way of preparing data for business consumption.

Nick Jewell: it's really a core developer tool that actually requires quite advanced training to get the benefits and that means whenever changes are required.

Nick Jewell: there's usually significant delays in getting those changes actually deployed into production.

Nick Jewell: Then we hit the bottleneck of oracle's dv product not working well with more complex models and the additional problem of incorporating multiple data sources so that insights can actually be built up across multiple enterprise systems.

Nick Jewell: It turns out that getting the data pipelines to the systems and making sure that everything is lined up in oracle's rapid file database is actually a real challenge.

Nick Jewell: And, of course, all of this before we try and actually run our reports, where we're hitting that relational database with millions of records.

Nick Jewell: And we've traditionally required significant efforts to performance tune these environments in order to deliver a fast query response.

Nick Jewell: Trying to query against the data structure of an aarp system where the data is often stored in hundreds of back end tables with complex joins often ends in frustration, or simply failure because of Sub optimal performance.

Nick Jewell: And behind all this we're dealing with a pretty complex technology stack there is a whole supporting cast of atl tools Meta data management.

Nick Jewell: And then the visualization layer itself, it takes an incredible amount of expertise, just to maintain this fragile pipeline to deliver value into your business so.

Nick Jewell: If you're nodding along to these pain points stay with me, I want to talk to you about a better way to tackle these common issues with the quarter platform.

Nick Jewell: Now, probably the biggest change that you see when you migrate away from tools like obe to modern platforms, like in quarter is the time to value.

Nick Jewell: When you don't need to reshape or remodel data from the underlying business applications in order to provide insights for your users, you are getting analytics magnitude faster than before and it's a real differentiator.

Nick Jewell: By removing the need to transform or aggregate data from the source, we get to eliminate virtually all of the transactional traditional data pipelines.

Nick Jewell: And the data modeling that you'll have seen in your legacy data architectures, and that means you get to deliver data faster than before now we've regularly seen customers go from.

Nick Jewell: let's call it a single painful overnight batch load to running refreshes against that era P platforms every 15 minutes which completely changes the game in terms of operational insights.

Nick Jewell: And it's it's not just the load that changes, thanks to the way that in quarter has been developed we've created an innovative new way to actually query the raw operational data.

Nick Jewell: From the source systems and that results in dramatic improvements in query speed, now we call this direct data mapping David will be telling us all about it in the DEMO that's coming up.

Nick Jewell: And then, finally, one of the biggest pains in business intelligence is the reliance on kpis or high level metrics now sure they look great in dashboards.

Nick Jewell: But they don't help with follow up questions Why was this metric so high, this month, why did a particular sales region struggle to close the deals.

Nick Jewell: This question can only be answered by drilling down into the details which are often unavailable when all we have to work with a summarized star schemas with data at the wrong granularity.

Nick Jewell: So, within quarter all usable data from the source is available for analysis drilling from a top line KPI.

Nick Jewell: into those roared transactional details well it's just a click away and the numbers are always going to reconcile.

Nick Jewell: that's the kind of capability that can change an analytics culture overnights when all of those details are instantly accessible.

Nick Jewell: So, in fact, the cracks have been showing in so called modern data architectures for some time.

Nick Jewell: we've reached this states where multiple layers get developed, each with their own cottage industry of supporting tools and technologies.

Nick Jewell: Starting with data from source systems over there on the left hand side landing into data lakes, often in raw extracts straight from those source systems and then.

Nick Jewell: I guess progressively refined through enterprise data warehouses into business data marts those stocks gamers that we talked about where data gets presented to the business in more simplified terms.

Nick Jewell: Each of these layers results really in a significant loss in data integrity data decisions and that engineering have stripped away upwards of 90% of the original data.

Nick Jewell: And we end up with the creation of multiple copies of data in silos across the organization.

Nick Jewell: So how about we take a look at how we can simplify this whole process, what if you can take the business data, as is ingested enrich it.

Nick Jewell: deliver it, so the business users can work with all levels of that data from fundamental transactional levels outputs as quickly as possible.

Nick Jewell: So this means that you get access to operational analytics at that most granular level in the same platform.

Nick Jewell: As your typical sales HR marketing analytics that might slice by product category location, region or other features.

Nick Jewell: So again, by taking the data, as is landing it into the platform we're making everything available for analysis, not just that final 10% of your data, the whole dataset and effectively future proofing a data process for your end users.

Nick Jewell: So whatever questions get thrown but, at the data of by your users everything from these underlying business applications is present.

Nick Jewell: At this most fundamental level without the need to step back and write new scripts apply new data engineering to load or move the data.

Nick Jewell: So if I show you a simple comparison between traditional bi and data analytics pipelines and the in quarter approach over to the right hand side, we can see, first of all.

Nick Jewell: it's a number of different steps involved considerably less than the quarter approach.

Nick Jewell: Over on the left, all those traditional steps and all of the components identifying and extracting data into a staging layer refining the underlying database schema to hold that data.

Nick Jewell: These tasks alone easily run into months, perhaps longer to complete and only then does the bi process of actually building a presentation model.

Nick Jewell: Developing the end user components again this takes weeks, months or longer as well, now as someone who's led teams through this approach myself i'd say that six to 18 months as an estimate.

Nick Jewell: To go end to end is pretty realistic, maybe even a little optimistic, given that last box, because, of course, when users get to experience the data in a bi tool.

Nick Jewell: there's always new questions that need to be answered that just weren't considered at the start of the project and back through the cycle, we have to go.

Nick Jewell: Whereas within quarter, this approach is completely streamlined a full initial load from an aarp system will take a day or two.

Nick Jewell: followed by a drastically simplified pipeline that opens up the data to the end users in a matter of days.

Nick Jewell: what's really revolutionary, though, is that all those subsequent questions are available to be answered, almost immediately since we've loaded all the source data into in quarter it's available to serve answers to new questions.

Nick Jewell: And that's the secret to how we cut a traditional 18 month program down two days or weeks.

Nick Jewell: So let's have a look at some of the benefits Well, first of all, if you're in it we're talking about simplified data engineering.

Nick Jewell: As we saw in that previous slide if you can eliminate all of that time consuming atl your data pipelines, as well as remove the need to model, the data in these intermediate or refined layers.

Nick Jewell: you're going to save time you're going to save money, all the things that your CIO cares about.

Nick Jewell: But it's not just about upfront work it's reducing the need for ongoing maintenance and your wider analytic solution to the business so.

Nick Jewell: baseline reports and kpis get delivered faster, but with a self service front end users can build their own content against a semantic layer and in quarter that requires little to no support from it.

Nick Jewell: New data sources can be acquired and added gracefully to this model over time they become instantly available with full data fidelity to your end users.

Nick Jewell: From the business perspective well it's about getting to the insights faster against your operational data that's also fresh from the source system, helping you to make those critical decisions where it matters most IE right now.

Nick Jewell: So let's take some of those capabilities that matter most of the business and putting quarter to the test versus your legacy Oracle bi environment now we've talked about the ability to drill down.

Nick Jewell: and being able to start with that top line KPI and drill to see every component transaction.

Nick Jewell: To build reports well we've all experienced the pain of obese taking minutes hours longer.

Nick Jewell: To render our reports in quarter has the performance thanks to direct data mapping and a modern data architecture to make this process a sub second experience.

Nick Jewell: we've seen the pain of the traditional analytics cycle, where new data can take weeks or months to acquire and ingest into a data warehouse now down to minutes within quarter.

Nick Jewell: And finally, knowing that you've got accurate results after a series of complex overnight data pipelines well.

Nick Jewell: Honestly, who knows we've historically put a lot of trust into those processes and the end up looking pretty opaque.

Nick Jewell: Within quarter we don't reshape we don't transform the source system data, so we get instantly reconcilable results we get immediate confidence from our analytics.

Nick Jewell: So let's see what this looks like for a real customer deployments and here we're seeing some of the real world pains for a finance team.

Nick Jewell: Trying to analyze their receivables data on a daily basis now this legacy approach on the Left running obe.

Nick Jewell: After an informatica data pipeline using extra data so it's a pretty expensive stack already.

Nick Jewell: shows that just opening the aging dashboard for this finance team took 54 seconds just opening the dashboard nearly a minute before we can even get working.

Nick Jewell: Moving data from source system to Ob I was 70 plus minutes rolling 12 times a day, and once they're no drill Downs no ability to search within the dashboard.

Nick Jewell: Limited interactivity with even a simple filter operation on that dashboard taking more than a minute to complete.

Nick Jewell: Now contrast this within quarter which replaced this legacy stack with a platform that delivered over 96 refreshes every single day.

Nick Jewell: With a data pipeline taking just four minutes to get from the source er P into the dashboard.

Nick Jewell: Being fully interactive on millions of transactional records with clicks taking around one second each so allowing real analysis at the speed of thought.

Nick Jewell: And another example of what we call a pov or proof of value we're seeing customers struggling to migrate.

Nick Jewell: and upgrade their analytics stack to Oracle analytics cloud against E business suite so for a billion dollar revenue company.

Nick Jewell: These folks were suffering with a real i'm going to say perfect storm of challenges, oh AC reports taking eight weeks or longer to implement leading to this huge backlog of work for the reporting teams.

Nick Jewell: To add to this pain, they were relying really heavily on consulting resources to actually complete the migration, but really suffering with delivery so after a million dollars spent on tools technology and consultants.

Nick Jewell: They had barely to oasis dashboards live and not even stable enough to consider retiring that obs states.

Nick Jewell: With data pipeline loads taking 18 hours for full refreshes and over three hours for just simple incremental updates So hopefully.

Nick Jewell: Not too many of us on the call today will have found ourselves in this situation, where I think the team leader of this company said we paid for a Ferrari, but we're driving a Honda civic two years later.

Nick Jewell: So enter in quarters proof of value, where all of this customers Oracle EBS data was loaded into the quarter platform in just three hours.

Nick Jewell: Our data Apps applied in 36 hours to present insights and a multi source system consolidation delivered in just four days over a three week engagement period.

Nick Jewell: The quarter data Apps offer a pre built solution for critical business challenges, and for this customer resulted in the immediate delivery of over 80.

Nick Jewell: Fully customizable dashboards with plenty of room for extend stability and self service consumption.

Nick Jewell: So this immediately could replace those too complex array see reports that the customer had just simply been struggling to make work in production.

Nick Jewell: It immediately remove the need for atl star schema development or even data warehouse reengineering.

Nick Jewell: But it also preserved the integrity of that source data, meaning no data reshaping transformation and basically allowed the business to curate their own views of the data for their end users.

Nick Jewell: Now, another example, this time from extremely well known fortune 50 media company, where we replaced their backend financial reporting in Oracle over a six week period.

Nick Jewell: And once again look at the numbers we're seeing fast intraday refreshes meaning analysts get to work with the absolute latest data.

Nick Jewell: but also great to see that the end user experience things like searching directly within the dashboard leads to such great results for a finance team just looking to find nuggets of insights in their data sets.

Nick Jewell: So if we turn to a product comparison between obe alessi and in quarter, we can probably break this down into some familiar categories.

Nick Jewell: If we start with time to insight, there are some major disadvantages in the Oracle stack around accessing data directly from the source and also accessing that data in a timely fashion.

Nick Jewell: In quarter is simply hands down a more effective solution here for maximizing value.

Nick Jewell: With self service analytics although AC is definitely an improvement on legacy obe in quarter leverage is it's direct data mapping capabilities to make drill down performance orders of magnitude faster than even alessi.

Nick Jewell: When we consider each of the solutions in terms of being a more complete platform it's clear to see that Ob an array see exist really as point solutions that still require.

Nick Jewell: Other major investments such as a back end data warehouse et al pipelines and lots more besides in quarter has this incredible total cost of ownership or T co by eliminating many of these components from the picture.

Nick Jewell: And then finally down at the bottom, the ability to support augmented or advanced analytics is handled in in quarter, through its spark based architecture.

Nick Jewell: Allowing scalable data science and machine learning across the full extent of your business data, meaning that data science teams can work on a unified complete data set alongside your business or your finance analysts.

Nick Jewell: And as we've talked about a bit as part of the quarter platform we offer pre built dashboards and business schemas that provide this huge head start.

Nick Jewell: versus the cold start the most analytics teams get when working with EBS are trying to migrate off obe.

Nick Jewell: predefined templates and logic gets you directly to the data that matters from deep within these applications themselves.

Nick Jewell: Offering key metrics sample reports data visualizations and analytical self service based on in quarters deep experience of working with Oracle customers.

Nick Jewell: And the results of using these data Apps these blueprints can be dramatic often customers deploy data APP as part of their solution that often working with all the data for accounts payable general ledger inventory management end to end within the same day.

Nick Jewell: And let me finish up here before we get on to the DEMO by just talking about another elephant in the room.

Nick Jewell: So you might not just be migrating away from an obe maybe you're a part of a larger migration path from Oracle EBS over to Oracle er P cloud, which can lead to many new concerns.

Nick Jewell: Around supporting the business with critical operational insights through reporting and dashboards during or after this migration now in our expertise this migration requires a serious level.

Nick Jewell: of attention to detail data warehouses data pipelines reporting, all of this might need to be rebuilt, to support your future state.

Nick Jewell: So, facing a data migration might sound like the right way to go in order to mitigate risk over the lifetime of the project.

Nick Jewell: But it introduces its own challenges like delivering simultaneous reporting from both environments during that migration and possibly beyond the migration in fact we've seen customers start this process and still require access.

Nick Jewell: To their legacy system for two to five years afterwards and there's also concerns about exactly how much data needs to be migrated or off boarded.

Nick Jewell: To a historical data platform next there's the issues of data visibility, can you connect to the er environments via public api's or dedicated connectors are you actually reliant on expensive database backups.

Nick Jewell: To access all of your data and even once you have the data with all the complexity of an aarp system can you actually make sense of the raw data in its original format.

Nick Jewell: Finally, during the whole process it's really a lift and shift of what you've already done.

Nick Jewell: The goal here is not to just simply reinvent what you already have by copying and pasting.

Nick Jewell: it's more about digital transformation you'll want to reevaluate how business decision makers actually consume the data.

Nick Jewell: And this is where moving away from spreadsheet extracts and towards a more modern self service model actually makes the most sense, and of course for modernization projects like this in quarter platform officer high performance.

Nick Jewell: easy to implement a solution to ensure a successful delivery, as well as a smooth migration.

Nick Jewell: So thanks for listening, let me hand over to David at this point so he can show you exactly what the platform looks like in action.

Nick Jewell: And how it performs against these kinds of complex business data sets and just before I handover please remember drop any questions that you have into the Q amp a window will do our best to answer these after the DEMO David, I have a excellent.

David Barkaway: I presume you can hear me.

Nick Jewell: I can.

David Barkaway: You want to stop stop sharing, so I can share my screen so.

David Barkaway: let's.

David Barkaway: Just share my screen tell me when you can see my desktop.

David Barkaway: will see coming through clearly for me.

David Barkaway: Fantastic Okay, so what i'm going to show today is what you're actually seeing now is the quarter client environment.

David Barkaway: As you can see, everything is browser based and what i'm going to do is i'm going to basically switch between multiple personas and starting off with the sort of the the business user but also looking at building schemers building reports for my next year I was an analyst or potentially.

David Barkaway: The person that's actually building the infrastructure for the end users is going to include everything that an end user would see but.

David Barkaway: As I said, we're going to be bringing in data and creating schemas and what i'll finish off on is.

David Barkaway: What Nick was actually mentioning around the accelerators the data or Apps or what we used to call the blueprints so.

David Barkaway: When we're looking specifically at Oracle as a source directly rp environmental potentially fusion, so what we'll do is we'll look at the pre build content around that as well.

David Barkaway: So just to start out, then what i've actually got is, as I said, i'm looking at from an end user perspective, we can see the folders here and what we'll do is we'll start out with a customer 360 so.

David Barkaway: I can go into here to this folder and what I can see, as I can see a customer threes yeah I see all of the dashboards here and i'm going to drop into this one.

David Barkaway: Which is actually a sort of a customer retention dashboard and this actually takes data from multiple sources.

David Barkaway: Now you saw how quickly that refreshed to give you an idea, this is running in a cloud environment and it's on a single node, but that was 2 billion rows of data that we just processed create this dashboard okay.

David Barkaway: The main table you'll see it's got half a billion records, but none of this data was aggregated as Nick was saying earlier.

David Barkaway: we're using the lowest level of granularity of data that we're ingesting we haven't got summary tables we haven't got aggregate tables.

David Barkaway: All of these kpis at the top level of built on the fly, and this dashboard is actually built from multiple sources of information, this information is coming from Oracle.

David Barkaway: We want some salesforce jira data we've also got a turn probability model that we're executing.

David Barkaway: That uses features that were sourced from multiple sources and then calculates at this year, an aggregate level here but it's we drill down, we can see down to each of the customers.

David Barkaway: Now we're in a modern modern bi environment here, and obviously interact and see the value to nothing drill down on it, we can drill down and look at staples and yet again, you know you've seen a refresh here, this is working on that.

David Barkaway: Millions 2 billion records and you can see, the data coming in from multiple sources, and you know I can drill down further right down to.

David Barkaway: You know invoices payments etc now how do we actually build this solution so i'm switching now to more of the to the analyst or the person that's building the actual solution.

David Barkaway: So we obviously need to define our data sources, so let me just go across the data, you can see all of our different data sources here, I want to and i've got the authority to do so.

David Barkaway: I can go in here and a different data source, you know we've got data lakes we've been to be running on as your aws job to sector.

David Barkaway: or tcp aws sourcing data, and it can be you know your applications, you know your oracle's SAP these other environments, or we can even be bringing in real time data over Kashgar.

David Barkaway: So as Nick was saying we've got that sort of ability to ingest data during the day that intraday processing either in batch you know micro batch or real time.

David Barkaway: But you know we've got our connections now i've got one here called online store what I would do is once i've got my connection i'd go into schemas and I could create a new schema so let's get into my schema wizard and just call it we're gonna.

David Barkaway: And what am I connecting to i'm connected to my online store I give it a description.

David Barkaway: I press next now what we do, and this is where the direct data mapping terms things we interrogate that source system to understand what data exists.

David Barkaway: And how that data is connected so i'm going to select all of these tables now this isn't a massive environment, this is got 32 tables now Oracle.

David Barkaway: A rp you know the EBS or the fusion environment it's a lot more complex so i'm going to create the joins automatically and what we do is we actually create a physical structure.

David Barkaway: To bring this data in so it's creating the schema and it's working out how the actual data is connected and how it in what data exists and how it's actually connected it builds.

David Barkaway: That structure for me, so I can see the sales order header here and it's worked out how that transactional operational system works okay so isn't it was saying earlier.

David Barkaway: We don't need to create a complex analytic structure we don't need to fundamentally change the structure of this data to analyze it.

David Barkaway: What we actually do is we work out how the system actually works how interconnect and once we've loaded that data we can start exploring and analyzing that data.

David Barkaway: We haven't had to create a complex, multi stage pipeline to analyze this data, so if I switch back to the actual schema that we had earlier with 2 billion records so here it is here.

David Barkaway: You can see i've got my 2 billion records in here we've got 14 tables 31 joins and what we can see actually is, we blended data from multiple systems we've used a bit of information.

David Barkaway: Out of Oracle Okay, so we brought in transactions, all of the actual transactions, we blended that with the salesforce data and we've got some data from jira we've actually put it through an analytic model to calculate churn, as we saw earlier.

David Barkaway: But, as you can see here we've got a large table 470 million records we've ingested from Oracle.

David Barkaway: And it worked out the joins from the Oracle side and then we've connected across to the other, the other applications, but what I can do now what you know we've been just did this data, and we as.

David Barkaway: was explained earlier by Nick you know we can load, we can do a full load, or we can do an incremental load and once we got that data we can start working with it, so now.

David Barkaway: i'm looking you know i've got into analyzer we've got multiple different visualization types in here whether it's your ground graphs or tree maps maps sunburst etc, and we can bring in third party visualizations as well, but what i'll do is i'm just going to create a pivot table here.

David Barkaway: And what i'm actually going to do.

David Barkaway: Is i'm actually going to bring in data from that large table, so you know transactions line lines or hundred billion records and it's bringing in quantity.

David Barkaway: And so quantity ordered let's bring them in as a measure.

David Barkaway: let's bring a invoiced.

David Barkaway: spell it all quantity invoiced.

David Barkaway: Next, bringing in revenue amounts, as you can see, I can just search across the structure where the new males.

David Barkaway: And we'll do this by let's do this by and sales channel.

David Barkaway: Sales channel next the party tank.

David Barkaway: And we'll bring it all let's bring in month.

David Barkaway: landing.

David Barkaway: Page brings in as the colon also going to sort this by with one month number sorted by month number and we'll do that ascending and what will also do is i'm just going to filter this data and just saying, where it's not now.

David Barkaway: So, as you can see that's basically.

David Barkaway: It we've got a sort of a preview of the data here i'm just going to give it a name let's call it pivot.

David Barkaway: and say that.

David Barkaway: And we'll save this.

David Barkaway: into my dashboard.

David Barkaway: and

David Barkaway: give it a name.

David Barkaway: Okay, so did this earlier saying it's cool it 22 in the description.

David Barkaway: Right.

David Barkaway: Now that's created the actual dashboard i've got my tablet VI VI pivot table in here what i'll also do initially I Nick referred to this earlier.

David Barkaway: let's actually bring all of the transactions lines that remember that was half a billion records let's bring that into the environment as well it's cool it's the detail.

David Barkaway: of it, spelling.

David Barkaway: And like say that as well.

David Barkaway: So now we've got the sort of the High Level pivot I can see my sales channels will have different sales channels notice down here we've got our 470 million.

David Barkaway: records here.

David Barkaway: Now I could obviously sort around scroll through those if I wanted to but it's highly likely that I interact with this, and I would filter and i'd say well let's look at consumer electronics.

David Barkaway: hey we're down to 8766 and maybe i've seen a normally in February and filter on and now we're down to the last 414 roads, which is feasible to August and scan through that but it gives you an idea of.

David Barkaway: The ability to obviously interact as a business user word which is i'm looking at the high level of my summaries, which are built on the fly.

David Barkaway: I seen a normally I want to drill down to that lower level detail so as, as we said earlier, we are ingesting at the lowest level of granularity as possible.

David Barkaway: And we have that ability to drill right down to the detail we're not hitting any limits we're not being restricted by the actual model we're ingesting that raw data unable to analyze that.

David Barkaway: without any restrictions at scale Okay, so the other aspect to this is, you know we built this business schema and that's what i'm interacting with here.

David Barkaway: To the end user, they probably want to use business terms that they're familiar with, so what we actually have is, we have the concept of the business schemas, and this is where you're able to provide a nice friendly.

David Barkaway: layer semantic layer to the business user that in terms that they can understand, so it provides you with that ability to do you know enhance the data.

David Barkaway: To govern access and provide a nice interface for the end user to interact with so here, you can see, the actual business terms that user.

David Barkaway: would have they can have the labels, the descriptions and obviously we can share this to the Meta data with glossary is and.

David Barkaway: Enterprise glossary that you might have within your organization Okay, so what that's basically you know sort of an end to end process what i'm going to do now is i'm going to switch into.

David Barkaway: Here we're talking about Oracle so.

David Barkaway: i'm going to switch user and i'm going to show you the data or Apps or blueprints as they were cold cold previously, so this is a multi tenant environment so i'm going to switch into.

David Barkaway: EBS cold environments changing.

David Barkaway: And just logging.

David Barkaway: and, hopefully, I remember yeah so nice clean environment here, and you know this is the actual data and so as before we've got a connection so here we're connected into back end Oracle environment.

David Barkaway: And we've populated this with sample data, so please don't ask questions about the data it's a sample data set is not a sort of a live data set because it's a demonstration environment.

David Barkaway: But i've got my connections into the back end systems, and you know if we look at the content as Nick was saying earlier.

David Barkaway: This is all yeah you would know how we would go about it sort of a proof of value or live system is we're connecting into the source systems and we just load the actual templates like the template and it has, in the case of this environment.

David Barkaway: dashboards and reports it's got the schemas pre built it's got the business schemas pre built now obviously you may have tuned in adjusted your back end environment and there's a.

David Barkaway: You know, you may want to adjust and change things, but this is a massive acceleration in the delivery of insights to the end users it's not.

David Barkaway: weeks, months or as Nick was saying, potentially is delivering we're up and running within the proof of value in some cases hours days and going live within a few weeks.

David Barkaway: So, as you can see here we've got the financials supply chain manufacturing projects HR analytics now, you may not be using all of the modules but they provided out of the boxes standard so, for example, let's go into financials.

David Barkaway: So here we can see under the financials folder you've got the cash management got your employee expenses fixed assets GL payables receivables if I go into payables.

David Barkaway: Let go and have a look here you got your your aging holds and discounts supply performance if I look at the high level payables overview.

David Barkaway: yeah we have a class before high level KPI so I can see the invoices on whole total invoices discount savings and discount percentage, we can see with where we spend most on certain categories, who are my top suppliers.

David Barkaway: outstanding payments to suppliers when are we doing payments during the year.

David Barkaway: which you know, looking at the actual discounts we're getting from our suppliers and then we can see, you know the actual details and as before, if I wanted to I can filter so I look at consolidated supplies, we can see the invoices to them.

David Barkaway: Or the invoices received from them, not the actual payments, because we may have certain payments on hold so far you're here, I can see, we got.

David Barkaway: 20 invoices on hold to this supplier and if I want to look at the detail, we can just switch across between temps and see that information, so you know these are obviously looking at.

David Barkaway: The actual specific aspects to this, but you know, this is obviously the visualization now.

David Barkaway: In most as as Nick was alluding to earlier, a lot of the work is actually in bringing that data rings, so we have all of these pre built content from a visualization perspective.

David Barkaway: And if you wanted to we've got headless behind you could put your tablet or your power bi On top of this, as well.

David Barkaway: Rather than using if you didn't want to use the visualizations here, and you had people trained up and they were happy with another visualization tool, it might be somebody in accounts that may wants to may want to plug an excel spreadsheet into this is data they can do so.

David Barkaway: But the hard part generally is bringing that data in.

David Barkaway: An understanding, where that data is coming from, to actually build these reports and, as I said earlier, once we have our connection to the source system it's the actual schemas that are where we're ingesting that data and with operational systems like Oracle.

David Barkaway: Oracle EBS or fusion, etc, these are complex environments there's potentially thousands of tables.

David Barkaway: That you can be sourcing data from and we've pre built, you know you can see accounts payable here see accounts receivable calendar common financial common common.

David Barkaway: we've actually mapped all of this and define the schemas for you already So if you want to do HR or expenses, all of this is pre built So if I go into accounts payable.

David Barkaway: We can see it's 33 tables there's 136 joins in this case we have loaded a 30 million records, but we can see.

David Barkaway: what's actually built for you okay so we've got our purchase orders here on this, this is where all the invoices are, and you can see, all of the joints into the other in other systems and what the 31 joins so.

David Barkaway: 250,000 rows of data we're populating, but you can see where all of the actual links are to the other systems that we need to incorporate to create those reports now.

David Barkaway: we've got the roar tables coming in we've also applied some business logic, you would also have in the Oracle environment as well, so we've got our materialized views, and that applies business logic to the data.

David Barkaway: To enable us to have a light for light report that you would see within the Oracle environment.

David Barkaway: Now, as I said before you don't expose this directly to the end users, you can you're not they don't want to see all of these columns that we're ingesting so we have in a similar fashion, we have the business schemas so these business scheme as a defined for the end user.

David Barkaway: And the persona of the end user base civil asset maintenance manager your collections manager.

David Barkaway: You can see some here some.

David Barkaway: schemas here for tablo and, as I said, we can be exposing this to third party visualization tool, so they get a business schema it's a government interface.

David Barkaway: They see us like a sequel database and they can query query this environment as well you've got your general ledger imagery and the ones that we were using for the dashboards that we were looking at earlier.

David Barkaway: Things like payables holds and discounts, you can see, this is much more business friendly the pain see your calendars and the ledger the suppliers, the buyers discounts invoices, etc, the sort of terms business terms at the end user would be familiar with.

David Barkaway: So just to give you an idea of other areas okay back into content here.

David Barkaway: For example, if I was looking at getting back into the financials.

David Barkaway: I may be looking at the general ledger, for example, I know that Nick mentioned this earlier if I go into my trial balance, for example, I can look at my GL journals.

David Barkaway: Okay, so we go to journals here, and if I see an anomaly, I can go drill right across and down into the details, if I click on this, I can do straight across into my accounts receivable.

David Barkaway: can see all of the actual journals the actual entries and write down to the detail in here.

David Barkaway: And if I go back in, for example, one of the other things that we see a lot of is the end of end of month closing a lot of organizations struggle with that.

David Barkaway: So you know from our perspective, we have a lot of customers that are using this capability to accelerate in the month closing bringing it down from potentially a week or so down to a few hours, so what we will be using there is i'll be going into the reconciliations.

David Barkaway: And you know I will be going maybe into the account summary.

David Barkaway: And you know I can see the variances here all right, and if I wanted to drill down, I can just go down to one of these Okay, and I can go straight into the details and I can see anything that sort of mixing any variances and obviously apply those and accelerate.

David Barkaway: You know the.

David Barkaway: End of month closing and reduce the amount of time for us to actually work on that so as you can see a lot of value provided out of the box here.

David Barkaway: It is massively powerful you can obviously extend and modify what's actually within here, and as I showed earlier, you can obviously blend this data with other sources of information.

David Barkaway: So, rather than having to focus on those complex etfs pipelines, you know this is built, out of the box to address the key things that we see the key reporting requirements that we've seen with a lot of our customers around Oracle and other sources and.

David Barkaway: yeah being able to focus on adding value, rather than that day to day reporting requirements, so what i'll do is i'll stop there i'll hand back to Nick.

David Barkaway: Then we can address the Q amp a.

Nick Jewell: Fantastic Thank you so much, David and you know what I mean some of that some of the slides you're showing them some of the some of the views of like the the ap schema alone, you know with my with my data warehousing hat on.

Nick Jewell: I look at that with horror, I think this is nine to 12 months of atl work and pipeline work in quarter has that data hub it's just all available out of the box Thank you so much for a fantastic DEMO.

Nick Jewell: Okay um so we're into the Q amp a and basically at this point i'd like to make sure if there's any questions that we haven't answered for you get them into the Q amp a now.

Nick Jewell: In my admin view I can see a couple of questions already and I think the first one is for you, David So the question is how do we support data snapshots for historical reporting.

David Barkaway: So we support both dense and Spar snapshots on our box so absolutely we can bring that data in we can you apply it to table and schema level, and we support the snapshots both dancing sparse within the actual platform.

Nick Jewell: Excellent Thank you.

Nick Jewell: let's have a look, we got got a couple more so than the actual two questions I have seemed to be around customers looking to migrate not only.

Nick Jewell: obe but also maybe the underlying.

Nick Jewell: platform, so this says as a customer we are migrating to Oracle cloud era P does in quarter support this.

David Barkaway: Apps absolutely yes, so we have a lot of customers that are doing that migration.

David Barkaway: And what you're able to you use these you're able to use we have you know the flavors of blueprints specific the overall you know we're on premise, but also the.

David Barkaway: The the cloud environment and we can we can accelerate that that migration for customers moving across to these environments quickly get them up and running, with the new environments.

David Barkaway: apply the blueprint or data APP as you saw and absolutely support the cloud environment, as well as on Prem.

Nick Jewell: that's that's wonderful so that actually is my final question for you, then so So what is the state of play for our European customers when it comes to on Prem and the cloud right now.

David Barkaway: So you know we can, if we're agnostic to how we're actually deployed as a platform, so we can be deployed on premise, we can be deployed in the cloud.

David Barkaway: it's up to the customer we're very flexible with our deployment model.

David Barkaway: it's in it's up to the customer, how they want to deploy and we support all all of the major deployment models.

Nick Jewell: Fantastic well that just leads me to say thank you very much to you, David for a great DEMO today.

Nick Jewell: And thanks to all of you for joining us and learning a little bit more about upgrading from obe to modern analytics.

Nick Jewell: Now if you'd like to take the quarter platform for a test drive yourself head over to cloud.in quarter.com slash sign up to get started for free today, so thank you very much, everyone have a great day we'll see you on another webinar really soon.