Accelerate greenfield S/4HANA deployments

Hello. Hello, everyone. So thank you very much for joining our partner webinar for today. I see that there are some partners from France as well as from Malaysia. So thank you very much for giving us your time. And today, we'll be talking about how to accelerate your Greenfield s four HANA deployments. And just a quick introduction, I'm Marielle from the global marketing team here at Happy Slots, and we are so happy that you, have given your time for us. And just a few reminders before we actually delve into the topic, this is actually recorded. So if you have any colleagues that you weren't able to, invite for this session, don't worry. We'll be sending an on demand replay for this one, and it will be shared after this webinar through an email follow-up sent to all. And if you have any questions, any, at any point in time, you can actually see the question box at your right with a question mark. You can send them through that, and we'll be answering them all at the end. And just to share, we've been doing, these webinar sessions for partners, for this year. And today, we'll be talking about data extraction, which Daniel will be sharing with us today. And for, the end of February, we also have a session focused on strategic, selective data transition, and that is gonna be in the American time zone. So if you are actually, interested in that, you can forward an on demand video on that. And without further ado, I'd just like to introduce our speaker for today. We have Daniel Parker. He is our solutions manager here at Avis Labs. And take it away, Daniel. Thank you. Thanks, Mariel. Welcome, everyone. So, yeah, as mentioned, we'll have a look at some, solutions and products we have today to help around, S4HANA adoption and S4HANA transition. But, first off, just a little bit about APS Labs. So, you know, so we're a software development and r and d company, at heart. We've been building predominantly SAP, based solutions for well over twenty five years now. So we'll show you a couple of things, related to that today. But very much r and d focused and about building solutions for our customers and our teams to help with problems in the SAP space. And we're part of the bigger group, Elephant. So it's a larger group of organizations predominantly working in, in IT field, not always, just SAP, but working together on larger projects and larger, offerings that we can bring and help with. So, yeah, today, as mentioned, we'll look at, S4HANA adoption. So, kind of regardless how you look at it, it's yeah. Whether you look at any sort of bridging or extension and things like that, SAP might be bringing around, ECC six. They very much do want all customers to be using, S4HANA, at some point. And we do know that main, end of mainstream support date is approaching. So we're seeing more and more customers thinking about how they're actually going to, move forward to S4HANA, or maybe potentially move away from SAP even. This some here, we've got some research from, ASUG, twenty four. This was, put together. So we see reasonable distribution between, greenfield and brownfield approaches for people moving to S4HANA, and, a reasonable number of hybrid or potentially selective type deployments. So for me, what I see here is potentially fifty percent of those that have moved, to S4 have done it in some kind of manner where there is a requirement for manual data extract, and a requirement for potentially transforming data before loading to a new environment. And there's a lot a high chance that have left behind legacy systems and legacy datasets, which might still have a compliance need or a regular regulatory need for the business to hang on to. And we also see this from Gartner research as well from last year that, the S4HANA public cloud edition is still growing and building as a thing. And, you know, a bit over two thousand customers, they're estimating on the public cloud edition of s four And, a little bit deeper in the analysis for that, for that, for that stat is around fifty percent of those customers have come on since the announcement on the, the Grow offering for public cloud. So in that area, we sort of see that broken down to a couple of pain points in terms of data, which is where we'll look today at our, product based approaches and offerings that can help. So the first is very much around legacy data extract. So getting data out of an ERP system, in some kind of manner that you can take forward to that greenfield is to have a deployment or that hybrid, selected data, deployment. So, typically, this is difficult to scope and define. The data model of SAP is complex, so working out what tables have what data and what subset of data is relevant to can be quite, problematic. Manual and labor intensive to get the data out as well and often lack repeatability and automation. So as you move through project cycles, you might not be getting, speed up or ease of use for those extraction and move, of the data cycles. It's still being a problematic step. And lastly, there may be need for manual transformation. So there could be parts of the data you need to send some fields or maybe renumber something, and you're having to do that manually before taking it to the target. So we'll look at a way that we can help with some of those, pain points. And the other part of the puzzle is around what I often call pretty much the ignored scope. So that's the legacy data archive. So if you're taking your subset of data forward to S4HANA, especially greenfields, and public cloud, you can't bring all your historic data into the public cloud deployment. So there's a big focus on that go live. You know, everyone goes to the party. Everyone gets a t shirt. Everyone's happy we're live on s four, but you're left behind with a legacy data set, some kind of, legacy system, SAP or non SAP, and you want that data for reference and compliance needs. So what do we do about that? So we'll have a look at the way we can solve that pain point as well. Yeah. So product based approaches that we can, help around those areas. These things we're looking at today are based around our, software suite called, data sync manager. So that's a core part of, a lot of the components, that we look at today. But it's a suite of solutions we've been developing for well over twenty five years, SAP centric, at this time. But it helps our teams and our customers do a variety of data management across SAP landscapes, so scrambling, copying, building systems, redacting data, whole different areas, even reporting and managing all data. But today, the focus will be looking at extract and archive. So we're just looking at two applications of the data sync manager suite in terms of how we can get data out and how we can move data to, to an archive solution to help with those pain points we were, discussing a moment ago. Alright. So let's move on to our first problem area, which is that, extract of data, for the need of loading to the target and doing that in an efficient, and repeatable manner. So object extractor is the component we talk about, for this, and it's about getting accurate data extracted from an SAP system. So the drivers and benefits, around, object sync are very much about, reducing project risk and reducing project costs. So if we're thinking about where, starting a greenfield, move, we wanna get a subset of data. We wanna do that in an intelligent tried and tested way. So with Object Extractor that we'll demo in a moment, you can see that we support thousands of standard object types, within SAP, making it easy to select the data as a business object context rather than thinking about underlying tables. And as mentioned, this is part of data sync manager. So our tried and tested solution that's been, building systems, refreshing systems, scrambling, managing data in SAP for many years. We have flexibility around selection of the data as well. So maybe looking to bring just particular plans, particular company codes, particular sales orgs, or maybe just set, object keys. So we can manage that with the extraction, and bring that across, as part of the export easily. And we can control the output. So I'll show you three example outputs today, which is standard offerings that we can actually build and manage, custom output offerings as well. The other key driver or benefit, again, project, cost and risk reduction. So it's how we can template and automate. So once we've defined what we're gonna extract, we can create a re repeatable process. So we can save the selection objects into templates and repeat the use of those. We can schedule those. We can even automate those potentially through an API so we can trigger the extraction as needed. And lastly, we can build in transformations to the extraction. So as we pull the data out, not only were we writing it in a format that helps us load, but we're a we can be, you know, adjusting values or maybe redacting or losing a value, you know, renumbering a company code, maybe dropping out a sensitive piece of data, which isn't appropriate to load to the target. So those kinds of, options are possible. And then, and then lastly, you know, we're here to help in our teams. So we had large consulting teams around the world who, been working with the data set manager product set for many years. So we're familiar with how to define output formats for you, help with the identification of the data, and understanding what's happening with the source SMP system. So towards the end, I'll give you a quick look at today, an analysis process we have to help understand what is the footprint of the data, what is the extensions we might need into zed tables and zed fields, and they can also help with, running the data extract. So that is kind of the key things around, object extractor. But if we look at, a basic sort of slide walkthrough of what it's about, so from the source SAP system, we're going to select particular data or data objects that we want to extract out. We have a selection screen. This is where we can template. This is where we can put filters around, you know, plant sales, or company code depending on the type of data, and we can save those selection options and easily repeat. Then when we actually extract, we've got the three standard output formats. So we're writing two flat file. It could be a JSON, XML, or CSV, but we can also build formats. So we've done work with our customer to build the data into a format that was easily loadable into the, SAP migration workbench, templates. And then the output is used however we like. So as mentioned there, the migration cockpit is the obvious area where we would, bring this data, to the target import into the greenfield s four. We're also doing work with a test automation, company at the moment where we can populate, test automation scripts. So we take data in a format so they can replay the creation of a sales order and those kinds of concepts. But again, once we have this data in a usable format, we might push it to a data lake or even have ideas about training an AI model after we've sanitized the data on the next structure. Alright. So let's have a look at that in action. So I'll jump into an SAP system, setting up an ERP system to give you an idea of, how it runs. Typically, in this context, we're extracting from a SAP ERP, but we can support, other types of, SAP system as well. So this is the launch screen for our object extractor functions. What I'll step through is a little bit like that slide, diagram. So I'm gonna create an export. That's why I'm pulling the data from the system, that has what I want to send elsewhere. See there's some custom content that's being built in this demo system. But up here, I've saved myself a favorite for a material, which will use it. But, if I extend out some of these, menus here, we can see the beginning of the range of existing objects and existing content we have service as part of that thousand odd objects we have, which are predefined. So we've built out the table structure and the relationship of the data. So if I extract a customer hierarchy, I'm going to get the records for all the related tables for that with that object. So that is part of what we provide with the solution. But, if I use material as an example, we'll work through this little road map here, as we go through the process of selecting what we want to extract, and then running the extract. So on the selection screen here, I have options, at the top here to define what I wanna extract. I can put in particular material numbers. So for this case, I'm gonna put a range, which will give me a subset of data, that we can have a look at. I can do some filtering down the bottom here, so I might wanna filter by, a particular plant. You know, if this is a different type of object, maybe it was a sales order, might have, you know, sales org or sales group as a filtering option. So these will vary depending on the type of data. We can adjust the fields that are visible here as well, as part of that solution. And finally, over on the left here, we have what we call the integration tree. So the material object or the material data by itself may need some supporting information. So if there's linked characteristics or classes or cost elements, we're gonna extract those automatically because they're enabled in this integration tree. But at the same time, the objects in this integration tree, the majority of them are executable items themselves. So I may extract materials, and then I may run cost element as its own extraction rather than bringing them, with the material. So next up is a preview. So we've picked the range of data we wanna bring. On the preview screen, it's going to show us exactly what we found. So within that range of two hundred odd, material numbers, it's picked up twenty one keys, that's kind of extract. So this is the data I'm going to pull out of the system to a flat file that I can then use, elsewhere. Next up, we have transformation options. So essentially this will allow us to build a rule set, with repeatable logic where I may wish to adjust or change something. So if I, have a look at this as an example here. So this is a basic policy, which is more geared to scrambling, but it gives you an idea of what's possible. So I can do things to particular fields. So in this case, for a business partner, I'm going to adjust the tax number, you know, the VAT or ABN, whatever the context is. I'm going to apply some change to it. I might randomize it. I might clear it. So this is a, a policy we can build for the transformation of the data as we extract it. And underlying that, we can build, you know, add that code if needed. Usually, we're just using some simple Excel like formula language or maybe even just existing options. So it could be setting a constant, could be clearing a field. But there's a lot of flexibility there, in terms of what is possible to transform the data, as it extracts. And then lastly, execution. So we're gonna define where we're gonna send the data. For this style of export, we're always sending it to file, at the back end. So it's always being extracted to the SAP application server. We have what's called a route defined, so that's defined in the administration of the system. And that's pointing to a particular physical file system on the, on the back end host. So these extracts all go into the data center, so to speak. And then it can be they can be picked up from there, managed, and pushed to where the target is or where it will need to be sent forward. So we can't pull the data out to the front end with the solution here is the key point. At this point, I picked my, export format. So we have, JSON, XML, and CSV, which I'll show what look like in a minute. And there's a couple in here which others have been building. So there's, some migration workbench, couple of versions of that, and then some automation stuff as well. I was talking about the automation tool set. But, we'll run this one as a JSON extract. I can run it in the foreground. I can in the background. I can schedule. I can put notifications. So this is where we're beginning to see the automation, and I can template all this as well. So if I hit save, saves all the options as the template. I can repeat that template, when needed. So let's execute that. So it's running in the foreground, but it's not actually sending data to the front end. It'll send the data to the back end. So that's the job there. It's a very small amount of data, so it ran very quickly. But if we drill in, we'll see the object tree of what we pulled out. So there was those twenty one of the materials. We can see each of these are being extracted. And there's the keys for those. If I have a look at the statistics, for each one, I can see, you know, there's a series of records around mandatory tables, but no, data groups. So we can see the high level here kind of what we've brought out and what happened. And then we get a message log as well. So if there's any errors or issues, we can see what happened. And basically what happens at the end here, you can see that we bundled everything up into a zip file, that's available on the backend server. So that's the kind of quick walkthrough of that. So beforehand I prepared a couple of earlier, so I manually downloaded some examples of that same data set, just from the backend server. So this is just displaying it in a simple text editor. So I've got a JSON version so we can see the way it's, structured out, with a variety of data there for all the different table types and fields. There's the, the XML version, similar kind of thing. So for each of the keys, we have the, the various, fields and tables and data elements defined. And then lastly, we see here CSV. So when we pick the CSV option, we don't have that option of creating kind of structured setup the way we can with JSON or XML. So we end up with a CSV file for each of the main tables for part of the object, that we're extracting. So in that case, there's a bundle of, CSV files, which are we've got the fields and then the data spread out like that in them. So they're just examples of the standard, output types. But, as we saw, as we were stepping through the, with the solution, there's other ones in there that people have been, building as custom ones around, you know, changing the format that comes out from to more like, what the migration workbooks should expect or changing the way the CSV looks or the formatting of the XML so that is possible, as well. Alright. So that's object extractor. So the intention of that is to, access data in the SAP system, the legacy source system, in a business object, in a model of the data, concepts rather than a table view, and bring it out in an intelligent manner that we can then take forward for the greenfield, import or give us flexibility around use of that data. So, licensing model for that. So, typically, it's being used around a project. So, it's a subscription or a lease within the project. So it was part of that export conversion. We had a monthly subscription cost across the project lifetime. Typically, it's a minimum of three months, which would be a quite a short running style of project. And we base that price point on the production or the source system size that we're extracting from. We support all ECC six enhancement pack levels. So those screens and that functionality and the features I was showing you a moment ago, we can do that with any, ECC six system. So we can define custom output types and transformations, and those kind of concepts. If we're looking at something like ECC five or four seven, even four six and lower, we do have, a little more limited support because of technical reasons in terms of how the product's changed and the development environment of the NetWeaver stack has changed. But we do can support ECC five four seven and do have some limited support as well for four six. So if you are seeing projects on much older systems like this, there is still ways that we can help with that kind of product set. Right. So that's the, yeah, the data or the object extract, for use for ingesting to, a greenfield system or some kind of hybrid subset built system, where you're looking for manual data load. The other end of the puzzle, as we just described upfront, was the data archive. So at the end of the project, we're potentially left with a legacy system or a legacy, data subset. So this is where archive central comes in. So this is a place where we can sunset an application, to a cloud hosted SaaS solution, but it's, secure and role based. And, we can attach in, both structured and unstructured data together. So it's a simple way to bring, that legacy data to a more usable format. So the drivers around this, very much sunsetting a legacy system. So, post that conversion to s four, you might be left with an old ECC or maybe a non SAP ERP system. And then there's operational cost and risk around running that. So, you may need to keep that available, keep that data visible for compliance or governance reasons for future audits, but you don't want the operational risk of the hardware support licenses, maintenance and updates for security reasons. So this is a way to bring that data out and put it in the SaaS solution without having to worry about those areas. Another big driver is around divestitures, so splitting a business. So you might be carving out a company code as a sale or receiving a business unit as part of a buy. So this is a way to put that subset of data that's relevant to the section of the business that's being sold into a read only, archive that can go, as part of the sale. But, the area that's important today to today's discussion is around that business or system transformation. So moving to S4HANA, moving Greenfield, moving to, like, a subset load, and we're leaving behind, a large volume of data that still has a compliance need. So this is where we can, load that to the ArchiveCentral solution. So what is Archive Central? So as I hinted to a little bit, it's a SaaS solution. It's cloud based archiving. So it's a solution we've built. We host it with a large, hyperscaler so we can place it geographically as makes sense, full on for the customer and their data that, that we're gonna host. And, we take care of managing the solution and managing the relationship with the hyperscaler. The customer is just, accessing and running the, and using the data in the archive. We can bring multiple datasets into the one archive. So the examples I'll show in the demo is very much about getting some data from SAP. But we have customers with multiple, sets of non SAP systems in the one archive. So one customer in Australia has, implemented, ECP, payroll with SuccessFactors with SAP, and they retired three non SAP payroll solutions. And we merged all three of those into the one, archive central instance where they can view all of that legacy data, as well as move ahead on their SuccessFactors payroll system. As you'll see when I run the demo and we move all the data from SAP, we can bring the data model along with it. So we're using what will look very familiar after the object extracted demo, where we can bring out the table structure, column names, your text names, information like that through the archive to help build up the structure of where the data is gonna sit. And then we can attach in unstructured data so we can run reports, captures PDF, or attach other types of data into the system as well. It's, yeah, the modern solution so we can do retention of data, periodic data removal to help comply with things like GDPR and those kind of modern privacy laws. And lastly, being a new modern solution that we've built, so we've got a variety of certifications, and it, yeah, covers typical modern enterprise grade security type concepts. So the data is always encrypted, whether it's traveling or at rest. We can support various single sign on, methods and support, MFA, those kind of concepts as well with the solution. So what does it kind of look like when we're doing a pool of data from SAP into Archive Central? So, again, we're gonna go through a process where we identify and select the data that we wanna take out of the SAP system. And very familiar to the fall, we have a screen where we can manage and refine that selection. So it's pretty much the same kind of process as we saw for Object Extractor. And then lastly, we can run a direct connection. So unlike the Object Extract where we're looking to get the data into a file to manage to upload elsewhere, In this case, we can run a secure RFC connection directing to our, ArchiveCentral. So from a from an archiving point of view for the legacy data, we don't have to have the data at risk between the SAP system and ArchiveCentral. We could, once, once we start the process, we can just push it straight through to the archive. And then lastly, as mentioned, we support more than SAP, in that archive. So it's an agnostic solution. We can load a variety of different datasets and data types. So there is support for flat file upload as well. So we may get data from non SAP systems in CSP or similar, and we can upload those. And, typically, that upload process is used as well for attachment data. So we might be capturing reports in SAP as a PDF, and then we use the flat file upload to load those. But, typically, the bulk of the data goes via a redirect connection. Alright. Let's have a look at Archive Central then. So I'll start sort of in the front end. I'll show you how that look and feels, and then we'll show a, an example archive of data from SAP Direct into the solution. So this is the web front end. So we can customize the URL. So it makes sense for the customer that's using it. And then the basically, the data model here is we have workspaces. Within a workspace, we have what's called collections, and the collections can hold, different sets of data. So you can see here there's material, central person, classes, and characteristics. And then within the workplace I mean, sorry, within the collection, we have tables and fields and the underlying data. But, typically, what we're doing is we're managing or reviewing the data from a workspace, and then we can secure that, for particular users or particular roles. So in this situation, I've got some finance, so two different types of HR data and another non SAP finance system. So I can have roles and particularly users attached to each of those. So only the HR people can see success factors in HCM and the finance team can see finance and IFS. The ending team can only see materials management. So there's the ability to segregate the data. If I drill into finance as an example, this simple workspace here, I can see collections of data. So there's vendors, cost centers, accounting documents, balance sheets visible in it. And then we also have saved views, which are essentially basic reports. So we can build up a view, save it, publish it here, and we can also create them. As you see down the bottom here, I've got a series of different employee saved views, which I can save to favorites at the bottom of the screen. If I drill in, for example, here to vendor, so this is the list of vendor objects that have been pushed into the archive from SAP system. If I drill through here, we can see that we have a sort of a mocked up layout, which is a little bit like what you would see in the SAP system. So this view is modifiable and editable by, by an admin at implementation time. So we can set this up to look and feel the way we want and have the correct layout and sections on it to make sense for the customer and the type of data. And if I click through these tabs, we see different reference information, and content has come as part of the archive. The other thing we can do if I jump back a couple of screens is we can see some attachment data examples. So here I had a series of balance sheets, that have been uploaded. So if I drill into one of these, so that is a report that's been run to give a subset of data at a point in time, captured that as a PDF and loaded it into the archive. So the two are sitting next to each other. So the larger example of, things sitting next to each other, if I drill into, an employee collection, I'll just have a look at here. I've already done a search or a filter on this for a particular employee. If I drill into this guy, again, we can see a layout, which kinda makes sense for HR data. It looks a little bit like p a twenty because this came from SAP. But embedded here, I've got an audit trial log. So, again, when we've extracted that data, we've captured an audit log for that person and attached it in. And if I drill into the pay information, we also have payslip details. So I can click on a generated payslip. So we've captured, line item results information, generated a payslip, and attached it as well. So there's quite a lot of flexibility in terms of the type of data you can bring in, how you can structure, how you can model it and make it look. And then lastly, we'll just jump into materials management, and we'll have a look at the materials master data here. So these are a bunch of materials that come from SAP. And, again, these are gonna layout and a look which makes sense for that style of data, and different teams, that can be done in there. Alright. So that's sort of the look and feel. As said, we can create roles and security to control which workspaces and which collections people can access. There's also a concept of, hiding sensitive fields as well. So maybe something like date of birth or salary field in payroll, you might hide except for the specific people. But it let's have a look at how we can push data from an SAP system to that archive. So here we've got the launch screen, the archive extractor. This is gonna look quite familiar after the object sync run through. But if I create an export, I've got a similar looking, launch screen here again. So we've got the same kind of list of different objects and different options for data we can send. I'm going to do that same set of materials that we did last time, just to give a consistent feel for what we're doing. But again, you know, similar selection screen. I can template this. I can say that I can filter on particular areas or pick select particular range of objects at different integration options if needed. We've got selection preview again. So we've seen that same twenty one list, of objects. Now this time we're calling this redaction options. Last time it was we're trying to push data to a file, so we might wanna transform it. In this case, we're gonna push it to an archive. So I might identify particular fields where I actually want to drop the data or mask it or change it before I put it into the archive or cleanse it in some manner. But it's the same idea again. We'd build up a policy, a rule set so we can logically do that as we push the data. So it's automatically making that change. And then lastly, on the execution, this is where things change a little bit. So instead of going to file, I've got a HTTP connection defined. You can see there, obviously, file is possible, but usually the preferred misspent is to stand straight across HTTP. I say HTTP, but that's an SSL secured connection as well. So we should share a certificate from the archive central, instance back into s trust and SAP and set it up as a proper secure connection. But I'll kick this off from the background, but all the same options are there so I can schedule this, send notifications, run on multiple processes if it's a large load. But we'll kick that off. That will push to a background job that we can see here on the monitor desk. And so that's just starting and getting underway. If I quickly jump back to our archive central instance, go into administration, and we can see our import tasks. If I'm quick enough, there we go. We can see that same job running now. So it's started selecting the data from SAP, packaged it up along with the data model and the structure and information about the tables, and it's pushing it across, to the archive central instance. So these are the tables. So we're picking material. These are the possible tables that are, that are part of, of the object. Somewhere in there, there'll be some of the records that are coming across. We'll see what collections we're loading. So it's going to go into, an existing material connection. It's adding twenty one old records to it. If I refresh again, so we've loaded the data, and then we're just configuring the collection and some flows off and finish off tasks. But essentially, we can then drill through on that and, find the data that we uploaded. I think that might have been one of the numbers that we had. I think I had it somewhere. No. I just have it on my clipboard. But, essentially, it's loaded in, and we can look at it and manage it like we've seen before, in that other screen. So that is the way we can essentially, take the guesswork out of loading that in, because we're ingesting the data and stepping through that process of loading it. And on the source side, when we're running that extract, which has just finished here, we have options to template and automate, you know, schedule the jobs, maybe drive them via, via an API, those kinds of ideas. So that is the context of getting datasets from SAP into Archive Central in a repeatable manner, with some certainty in terms of what was selected, what was loaded, and then what becomes. Alright. And then lastly, the licensing model, around that, how that works. So in this case, we are loading the data to a SaaS solution that we host and look after. So the model is around the, ongoing lease and ongoing subscription for the management of that solution. So we always have what we call a base subscription, which is essentially your first source system that you load, and then we have add ons depending on how big that is or if you have additional source systems. So a base subscription is, you know, that one source system, up to a billion rows, up to half a terabyte of unstructured data, and up to twenty five users, connecting to review the read only archive. So this is, yeah, typically fulfilling probably eighty percent of needs in terms of the type of stuff we archive. So most people are sitting around the base subscription. That, example I gave earlier of, an Australian customer with three, payroll systems they brought in. So they would have bought two additional source system, add on licenses. So that gave them the right to run three source systems in that one combined Arc of Central instance. And then from time to time, we see very big systems which have a lot more rows to load, or maybe there's a lot of attachment data. More rare is people more people needing to access the archive. Typically, it's, just a subset of people, maybe, particular finance teams or particular HR teams, that need to connect for compliance or type reasons. But that is the basic kind of license model. So the customer would expect a yearly subscription cost to maintain the data and maintain access to it. And then there's a one off implementation cost as well, just to ingest and load the data. At any time, if they choose to leave, we can, extract all the data and provide it back to them as well. Or if they no longer require, we can securely destroy and, decommission the instance as well. Alright. So that's sort of a look at those two, solution sets and, how we can help. So I did mention a couple of times in terms of, analysis or how we can understand the complexity of the SAP system. So this little looping video here is showing what we call a system analysis report. So this is a way we can gain some understanding about an SAP system to help define the scope for the use of, object extractor or an archive central implementation. So we run this in an SAP system. It gives us a view of the data volumes, the data footprint, you know, how much AMM data there is, how much HR data there is, what table is large, what table is small, zed tables. And then from that, we can get some insight in terms of what, an implementation might look like or what is the best way to approach using, object extract or the best way to make use of, ArchiveCentral. So if you are looking to gain a bit more insight with a customer, this is a great way to, take that next step instead of giving them certainty around pricing approach and what we can do. As I mentioned, it's a simple process to be loaded into the system. There's no business data, no identifiable data, and it's a way of giving us effectively a metadata snapshot of what's in the system. And lastly, I'll just give a quick example before we wind up on some homework for you guys. Sorry. That was a homework. We'll jump back. So an example of a project coming out of, South Africa as a financial services provider. They had an ECC six system, and they're in the process of moving to Estahana, cloud public cloud, I believe. And so we've used exactly as I was showing and explaining. We've used Optics Extractor to get data in a format that they can easily load into migration cop copied templates. And then we've used ArcMap Central to, take the rest of the data or the remaining data in the RTCC system and load it to a legacy archive for later view. So that is pretty much a cut and paste, use case of what we're talking about with the products today, getting the data out, the small subset out for the Greenfield implementation with Object Extractor, and then taking the leftover data into ArchiveCentral so they can retire that ECC system. Alright. And then here's your homework. So, we have a partner workspace in ClientCentral. So ClientCentral is our support platform, which is available to partners as well as our, direct team customers. So this is a great place to come. So client central dot I o, you may already have access. You may already have access. You may already have a log on. But within here, you can find a lot of, information about, products, services, and offerings that we can work with you on as a partner. And there's information in there as well in terms of branding, assets, flyers to give a, you know, two page PDF about archive central or about object extraction and other things we can do, with the solution sets. So this is a good place to come to get a bit more information. There's also links to knowledge based articles and videos and things like that to give you a bit more, information on the solutions. And then there's a lead logging, section as well. So if you're looking to, register something, can get in contact with one of our account teams or sales teams, you can come through here, log a lead, and they'll be in contact as well to, to help out, around, positioning and, helping you on a project. Alright. I will wind up there. I'll call Marielle back in case there's any questions that we want to Yep. Ask and got. Daniel for sharing about Object Extractor as well as SmartCare Essential. And, indeed, we do have a couple of questions before we end today's webinar. Just on Object Extractor, we have someone who's interested to know on how can the data be transformed during export. So, for example, changing a company code from an old to a new value. Yeah. Sure. So, we had that transformation screen as we're stepping through, on the extract. In that, we basically define a rule set. So, as long as we know well, that's a perfect example. So changing a company code, we're just looking for the company code fields within the data, that we're extracting, and we just define a rule that would substitute from the old number to the new number. So whenever we extract data, if the old company code number, say one thousand, is there, we'll just substitute it automatically to two thousand. So, that's part of the extract. Yeah. So that will always happen once we set that rule set up and call that during the extract. Also, on Archive Central, since you showed about the multiple source codes that, a recent customer had, so they were three. So the, someone's curious on how can we actually test and verify whether everything that's been archived is correct and complete. Yeah. Sure. So when I moved the data from SAP into Archive Central, we saw that series of green greener sliding tabs as the data loaded. So that's a technical way we can check what was extracted and what was imported. So we know rows and data that came out and have successfully loaded the rows and data. So that's our technical check in terms of what was extracted, what was loaded. The other way from a functional point of view, we usually advise customers to try and role play or try and pull out data to satisfy an audit requirement. So to sit down and say, every six months, I'll get these questions from an auditor or from an internal compliance team. How am I gonna find that data? How can I, you know, run a report or extract the data to get it out? So that's how we advise customers to think about the testing process. Think about how you would satisfy an audit requirement and even mock or role play that audit to help test the data. And, last one on ArchiveCentral as well. They're curious on what is the business value of having the ArchiveCentral solution over a basic data dump of required SAP data to a one file? Yeah. Sure. So I think the main thing is that we can structure the data and show it hierarchically. So if you're just pulling the data from SAP, you know, even something as simple as, you know, a material or a customer or a vendor, that's gonna touch, you know, tens, twenty, thirty tables. So to put that in flat file, you end up with a whole series of individual files with data that you need to look at and check and reference. Whereas Archive Central, we load all of that into a coherent data model, and you see those screens that we're looking at. So we can build a screen to show everything related to the customer or the material or the vendor in one spot. So you're not having to, put together disparate datasets whenever you need to find information later. It's available to you in one view. Yep. Alright. That's about it for questions today. So thank you again to everyone who joined today's webinar on how to accelerate your greenfield Sperana deployments. And, we'll be making sure that, all of the relevant links will be sent to everyone in a follow-up email. And if you have any more questions about Object Extractor or perhaps Archive Central or maybe if you have already qualified a customer that may be a great fit for these solutions, please reach out to us, and we'd be happy to help you out with RFIs and RFUs. And, also, you can just, take a look at some of the sessions that we'll be also doing on the twenty sixth of February. Again, we'll be doing that, SDP webinar, which is available in, the Client Central partner workspace as well. So that's it, everyone. Thank you very much, Daniel, for today. Have a wonderful afternoon, Nedrun. Bye.