Name: Creating Custom Analytical Queries with Semantic Tags - SAP S/4HANA Cloud Technology Topics
Uploaded: 2020-12-21
Duration: 688 s
Description: In this video, you will learn how to create a custom analytical query in order to consume semantic tag KPIs.

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<div><div><div><div>In this video, you will learn how to create a custom analytical query in order to consume semantic tag KPIs. <br></div></div></div></div>

Creating Custom Analytical Queries with Semantic Tags - SAP S/4HANA Cloud Technology Topics

Hello and welcome to this video on creating custom analytical queries with semantic tags in SAP S4HANA Cloud. My name is Sascha Zyga and I am a technology consultant for SAP S4HANA Cloud. In this video, you will learn how to create a custom analytical query in order to consume semantic tags. Our goal is to provide an understanding of the key concepts of semantic tags and custom analytical queries. We will build a profit and loss report to demonstrate the key user capabilities. Let's take a look on the agenda. We will start with an introduction of semantic tags and custom analytical queries. Moreover, the use case of building a profit and loss report will be explained. Transferring the theory into practice, I will provide a system demo, which starts with the key user perspective. After the configuration, the end user will take over and launch the report. Traditionally, financial reporting has required double or even triple maintenance of account groups and financial statement versions for reporting. Semantic tags are a financial infrastructure tool that you can use to simplify your work. The key users maintain a financial statement version, set up a list of tags and assign these tags to the items. These standard key figure definitions can be used in any report. With custom analytical queries, key users can create own reports and analyzes. They transform and organize raw data, delivered from the data sources, into a meaningful grid which can be interpreted by the business users. Using the tool, key users build analytical queries based on pre-delivered sources or from scratch. They can select the required fields from the underlying data sources, set filters, add restricted or calculated measures and preview the query results. Let's take a look at the use case in order to connect the dots. On a high level, the semantic tags are on the left side and a plan versus actual comparison is on the top. In the middle, the data will be aggregated. Additionally, filters will be set which is displayed on the right side. Using the terms of the app custom analytical queries, so-called restricted and calculated structure elements will be used for the KPI structure. To include measures on top, restricted and calculated measures are the right choice. As restricted structure elements, the semantic tags will be utilized. One for the revenue, another one for the cost. With the calculated structure element, the profit will be calculated. In order to differentiate the actual from the plan data, the planning category is used in combination with the amount in company code currency. And as said, the profit is calculated. The same applies to the delta between actual and planned data. At the end, some filters are used on the ledger, the financial statement version, and the ledger fiscal year. How does this structure result into meaningful measures? Let's look at some examples. In the first row, the end users will see the actual revenue, the plant revenue, and the delta in 2020. In the second row, the actual costs, the plant costs, and delta in 2020 are available. And in the third row the actual structure element layer provides the actual profit, the plant profit and the delta in 2020. Let's take a look in the system looking through the eyes of an analytics specialist. As a prerequisite, the semantic tags were already maintained in the financial statement version. With the Custom Analytical Queries app, analytics specialists can create custom reports based on CDS views. From the SAP Fiori Launchpad, go to Query Design and choose Custom Analytical Queries. Click Create. In the New Query tab, fill out the query name and select the data source. In this scenario, the CDS view I Actual Plan Line Item Semtag will be used. Click OK. In the General tab, fill out the label. Click Field Selection. Select the fields ledger and financial statement version. Those fields will be used for filtering. Click Display. Click Add and choose Add Characteristic Structure Element. Confirm the message. Enter a label and a name. Restricted Structure Element is the right choice. On the right side, scroll down until you see fixed values. Here you can define your semantic tag. Click add restriction, search for semantic tag and choose it. Click on the value help of include and choose the semantic tag. Remove any other unnecessary restrictions. Repeat these steps for the other semantic tags. Next, a calculated structure element will be added. Click again add and choose add characteristic structure element. Enter a label and a name. This time select calculated structure element. Click edit to open the formula. Enter your calculation. Next, the measures will be added. Click Add and choose Add Restricted Measure. Enter a label and a name. Change the measure to a mounting company code currency. Scroll down until you see fixed values. Here the key user can define the restricted measure. Click add restriction, search for plan category and choose it. Click on the value help of Include and choose the plan category. Click Add restriction, search for fiscal year and choose it. Click on the value help of Include and choose User Input Filters. Click New. Enter a label and a name. Select a new filter and click OK. Remove any unnecessary restrictions. Repeat these steps for the other restricted measures. Next, a calculated measure will be added. Click again add and choose add calculated measure. Enter a label and a name. Click edit to open the formula. Enter your calculation. Click Filters. Choose Ledger. You can either set a fixed value or user input values. If you choose user input values, provide a default value. Repeat for the field financial statement version. Finally, save your draft. If the report is ready for consumption, click Publish. In order to validate your result, click Preview. The prompts with the user input values will open. From this view, it's an end-user perspective. The end-users can flexibly filter the report and click OK. The profit and loss structure will be displayed. Back to the structure, you see how simple it was to realize a profit and loss report combining semantic tags with custom analytical queries. In this micro-learning, you have learned about the general concept and capabilities of semantic tags and custom analytical queries. While building a profit and loss report, you understand the key user tools like how to use characteristic structure elements, semantic tags, restricted as well as calculated measures and filters. Thank you.