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In this short video, we are looking at the basic concept of how data can be visualised in Archive Central. On this demo system, we have our test data grouped by different functional areas. So if I am a Finance user, my authorisation might only allow me to see this Finance workspace. In the Finance workspace I am now introduced to our concept of Collections. So a Collection is effectively our generic term of a data model. So we are grouping all the data that is related to this data model inside this Collection. And if we pick, for example, this vendor record that is coming from an SAP system, we can take a look at how this is used. So if I pick the vendor that I am interested in I can now see this vendor. And while the user interface at first looks different to how users might know it from the SAP world, we grouped the data in a very similar fashion. So if you would take a look at vendor records in SAP, the data would also be grouped by Address, Control, Payments transactions, Contact persons and so on. And we can do the same not just for master data but also for transactional data. So if I pick my example of Accounting Documents further down before drilling into one of the examples, every time I have data in such a tabular view, I have different options to choose from. So on the left hand side I have my filter criteria, where currently I am filtering down all the documents to the ones from fiscal year 2012, while also selecting on multiple company codes for which I have data on. But further than that, I can add filters on all the database tables and fields that are part of this collection. Also, what I can do if the data is displayed in such a tabular view. On the top right hand side, I can go to the table settings. Change the way the data is displayed like adding specific fields to this view. Group them in a specific order or in general prepare the display of this data. Because now I could either save this report again to reuse it later on, or if an auditor requests to have this subset of data provided, we can now download this data if we have the sufficient authorization on the system, and hand that over to the auditor. And inside one of these examples, we can again see as in your source system, you have the data related to the document header in one specific section; but you can also see the line items in a separate tab. And you could drill down into these line items as you could on your source system. The same is now possible for data that is also coming from different systems or different functional areas. Now I want to introduce you to the concept of Saved Views. Our Saved Views are effectively simple reports that can be built on top of Collections. In this example, I have a Saved View that is built on top of Employee Data that is also coming from an SAP system and visualising and grouping my employees by country of birth. If I'm not interested in all the employees that were born in Germany, I can simply drill down and take a look at that if I click in the corresponding section of my graph. We can now see on the left hand side also the filter criteria DE that it has created based on where I just clicked in the graph. And if I now just take a look at one of these records, and one point I want to highlight here is if we go to the Pay Information and select one payroll period and just drill down into this period; what our extractors can do is not just bring across everything that was stored in the database in the transparent format, but as you can see, our extractors can also handle any attachment or unstructured data that might be generated by reports on the source system, such as a payslip that is now, in this example, being downloaded and preserved on Archive Central in the PDF format displayed inline in my browser. And again, if my user has the authorisation, I could download this PDF on the top right hand side and provide it to whoever requested that data, such as an auditor. And another option to highlight in this section are these tables on the bottom side here. Such as the RT table or CRT table. SAP HR experts will directly see that these tables originated from clustered tables within my source system. So in this case, our extractors were able to transform the clustered tables into a transparent format so that we can very easily take a look and report on these tables. And also, if I click on one of these table headers, I again have the same options as we had for the accounting documents. Create specific filters. Save it as a Saved View or change and download the data that I prepared in this example. And the same approach goes for data that is coming from different systems, different functional areas. It is very flexible and I hope that this small introduction gave you a good understanding of how flexible the solution can be.