In today’s data-driven world, having real-time insights at your fingertips is crucial for making informed business decisions. SAP Data Sphere is a cloud-based solution designed to integrate, model, and analyze data from various sources. In this blog, I’ll show you how to set up a trial account for SAP Data Sphere and create a dashboard using SAP’s sample sales order data. The dashboard will include visualizations like charts and tables, along with predictive analysis. Let’s get started!

Step 1: Getting a Trial Account for SAP Data Sphere

Before we begin, you’ll need access to SAP Data Sphere. Here’s how to sign up for a free trial:

1. Visit the SAP Data Sphere Trial Page:

• Go to SAP Data Sphere Trial.
  
• Note: You will need an SAP account, which you can create for free if you don’t have one.

2. Sign Up for the Trial:

• Click on the “Start your free trial” button. You’ll be prompted to log in or create an account. Once logged in, you can follow the instructions to activate your trial.

3. Activate the Trial:

After creating your account, confirm your email address and follow the instructions to activate your trial account. Once your trial is active, you’ll be able to access the SAP Data Sphere interface.

4. Access Your SAP Data Sphere Workspace:

Once the trial is set up, navigate to the SAP Data Sphere dashboard. This is where you’ll create your data models, visualizations, and analytics.

Step 2: Importing Sample Sales Order Data

SAP provides sample datasets to help you get started. Follow these steps to import the Sales Order sample data:

1. Navigate to Data Builder:

On the SAP Data Sphere main dashboard, look for the “Data Builder” tab on the left-hand menu and click on it. The Data Builder allows you to import, transform, and model your data.

2. Import Sample Sales Order Data:

In the Data Builder, click on the “Import” button at the top-right corner. This will open a menu of options. From the available sample datasets, select the “Sales Order Sample” dataset.

3. Review and Confirm Import:

Once selected, you will see a preview of the data. Confirm that this is the dataset you want to import by clicking “Next,” and then complete the process by clicking “Import”. The Sales Order data will now appear in your workspace under the “Data Sources” section.

Step 3: Building the Data Model

Before creating visualizations, we need to create a data model that organizes the sales order data into useful fields. Follow these steps:

1. Create a New Space:

In SAP Data Sphere, data is organized into spaces. Go back to the main dashboard and click on “Spaces”. Here, create a new space by clicking “Create Space” and give it a relevant name like “Sales Order Analysis”.

2. Assign Your Data Source to the Space:

In the newly created space, go to the “Data Builder” tab. You’ll see the Sales Order data that you imported. Drag and drop this dataset into your space.

3. Modeling the Data:

Now, click on “New Graphical View” in the Data Builder. Select the Sales Order dataset as your input and choose the fields you want to work with, such as:

• Sales Order ID (SALESORDERID) – The unique identifier for each sales order.
  
• Partner ID (PARTNERID) – The customer or partner associated with the sales order.
  
• Gross Amount (GROSSAMOUNT) – The total amount of the sales order, including taxes.
  
• Net Amount (NETAMOUNT) – The amount after applying discounts and excluding taxes.
  
• Tax Amount (TAXAMOUNT) – The total tax applied to the order.
  
• Delivery Status (DELIVERYSTATUS) – The current status of the delivery process.
  
• Billing Status (BILLINGSTATUS) – The status of the billing process for the sales order.

These fields will help you create visualizations that show sales trends, financial summaries, customer distribution, and order status information.

Step 4: Creating Visualizations

With the data model in place, it’s time to create visualizations. Here’s how you can create charts, graphs, and tables:

1. Go to SAP Analytics Cloud:

To start building visualizations, navigate to the “Story Builder” tab in the left-hand menu. This is where you’ll create interactive charts and tables.

Create a Bar Chart for Sales by Partner ID:

1. Click on “Create New Story” and choose a bar chart as the visualization type.
   
2. In the data source section, select the data model you created.
   
3. Set the X-axis to “Partner ID (PARTNERID)” – this will represent different customers or partners.
   
4. Set the Y-axis to “Gross Amount (GROSSAMOUNT)” – this will show the total sales for each partner.
   
5. Customize the chart by adding labels, titles, and colors to make it more informative. For example, label the chart as “Sales by Partner” and add a currency format for the Y-axis.

Create a Line Graph to Show Sales Trends Over Time:

1. Add another visualization by selecting “Add Chart” and choosing a line graph.
   
2. Set the X-axis to “Created Date (CREATEDAT)” – this will plot the sales over time.
   
3. Set the Y-axis to “Net Amount (NETAMOUNT)” – this will display the net sales amount over time.
   
4. Group the data by months or quarters to visualize trends over time, allowing users to identify sales patterns, seasonal fluctuations, or growth over specific periods.

Create a Pie Chart for Market Share by Sales Organization:

1. Add a new chart and select “Pie Chart” as the visualization type.
   
2. Set the category to “Sales Organization (SALESORG)” – this will divide the sales data by different sales organizations.
   
3. Set the value to “Gross Amount (GROSSAMOUNT)” – the pie chart will reflect the sales contribution of each organization.
   
4. This chart will give you a clear view of the sales distribution across different sales organizations.

Create a Table for Detailed Sales Data:

1. Add a table to display more detailed information about individual sales orders.
   
2. In the table, select fields such as:
   
   • Sales Order ID (SALESORDERID) – to uniquely identify each order.
     
   • Partner ID (PARTNERID) – to show which customer made the order.
     
   • Gross Amount (GROSSAMOUNT) – to display the total value of each order.
     
   • Created Date (CREATEDAT) – to display when the order was created.
     
   • Billing Status (BILLINGSTATUS) – to track the billing progress of the order.
     
   • Delivery Status (DELIVERYSTATUS) – to track the status of deliveries.
     
3. Add sorting and filtering options for users to search for specific orders or filter data by criteria like date range, billing status, or delivery status.

4. Now our complete story looking like below:

Step 5: Adding Filters for User Interactivity

To make the dashboard more interactive, SAP Data Sphere allows you to add filters that help users explore the data in more detail. Here’s how we can add relevant filters based on your data:

Add a Date Range Filter:

1. In Story Builder, click the “Filter” icon at the top of the page.
   
2. Choose “Date Range” as the filter type.
   
3. Connect the filter to the “Created Date (CREATEDAT)” field. This will allow users to select a custom date range and view sales orders created within that timeframe.
   
4. This filter will help users analyze trends over specific periods, such as viewing sales in a particular quarter or year.

Add a Sales Organization Filter:

1. Next, add a filter for Sales Organization so users can focus on sales from specific regions or sales divisions.
   
2. Link this filter to the “Sales Organization (SALESORG)” field.
   
3. By applying this filter, users can narrow down the data to only view sales from certain sales organizations, helping them understand performance at a regional level.

Add a Partner ID (Customer) Filter:

1. Lastly, add a filter for Partner ID. This filter will allow users to isolate sales data for specific customers or partners.
   
2. Connect this filter to the “Partner ID (PARTNERID)” field from your dataset.
   
3. This filter will be useful for users who want to focus on the sales performance of specific customers, making it easier to drill down into customer-specific data and insights.

How Filters Enhance the User Experience:

By adding these filters, we give users the flexibility to explore the data in ways that matter to them. They can:

• Focus on sales from specific time periods (e.g., the last quarter or fiscal year).
  
• Analyze sales performance by region or sales organization.
  
• Drill down into data by specific customers (Partner ID), which is useful for customer analysis and segment-specific performance.

Step 6: Implementing Predictive Analytics

SAP Data Sphere allows us to use historical data to predict future trends and identify anomalies. Let’s implement predictive analytics to forecast future sales and detect unusual patterns in your sales data.

Go to Predictive Scenario:

1. Navigate to the “Predictive Scenario” section from the left-hand menu in SAP Data Sphere.
   
2. This is where you can create forecasting models using historical sales data to generate predictive insights.

Create a Sales Forecast:

1. Select “Time Series Forecast” as the type of predictive model.
   
2. Choose “Net Amount (NETAMOUNT)” as the target variable for the forecast. This will allow us to predict future net sales based on past trends.
   
3. Use “Created Date (CREATEDAT)” as the time field to predict future sales trends over time.
   
4. After configuring the model (e.g., defining time intervals such as monthly or quarterly forecasts), SAP Data Sphere will generate a sales forecast based on our historical data.
   
5. We can then display this forecast as a line graph in our dashboard to visualize future sales projections.

Anomaly Detection:

1. In addition to forecasting, you can enable Anomaly Detection in the Predictive Scenario section.
   
2. Choose “Net Amount (NETAMOUNT)” or “Gross Amount (GROSSAMOUNT)” as the target variable, depending on the type of anomalies we want to detect (e.g., unusually high or low sales amounts).
   
3. SAP Data Sphere will analyze your historical sales data and automatically flag any anomalies, such as:
   
   • Sudden drops in sales.
     
   • Unusual spikes in demand for specific customers or regions.
     
4. We can visualize these anomalies directly on your dashboard, helping your team investigate potential issues or capitalize on unusual growth patterns.

How Predictive Analytics Adds Value:

By using SAP Data Sphere’s predictive analytics capabilities, we can:

• Plan more effectively by forecasting future sales and identifying seasonal patterns.
  
• Detect and respond to anomalies in our sales performance, allowing you to address issues like unexpected sales drops or inventory problems.
  
• Visualize predictions easily in the dashboard, enabling your stakeholders to make data-driven decisions based on future trends.

Step 7: Sharing and Publishing the Dashboard

Once our dashboard is ready, we can share it with others or publish it for your team to access.

1. Publish the Dashboard:

• In the Story Builder, click on the “Publish” button at the top-right corner.
  
• Choose the audience you want to share it with, such as specific colleagues or departments, and set appropriate permission levels to ensure data security.

2. Sharing Links or Embedding the Dashboard:

• SAP Data Sphere also allows us to share the dashboard via a link or embed it on your company’s internal portal.
  
• To share via link, simply click “Get Shareable Link” and send it to your team.

Conclusion:

Congratulations! We’ve successfully created an interactive analytical dashboard in SAP Data Sphere using the sample sales order data. With visualizations, filters, and predictive analytics, you’ve transformed raw data into actionable insights. SAP Data Sphere’s flexibility and powerful tools make it easy to create meaningful dashboards for any business use case.

The post Step-by-Step Guide: Building an Analytical Dashboard in SAP Data Sphere Using Sales Order Data appeared first on ERP Q&A.

I’m excited about BDC and the value it brings. Having experienced firsthand the daily challenges of data transfer from SAP systems to data repositories—such as outdated data, transfer failures, and the lack of end-to-end ownership when issues arise—I understand the critical need for a solution that ensures real-time, up-to-date data availability and especially to leverage AI Insights.

To understand the technology behind, I would like to break the process into 2 different scenarios for easy understanding:

1. Use “out of the box” SAP insights App with “S/4 HANA” and “SAP Success Factors” as source systems and understand step by step on how the process works.
2. BDC integration with SAP Apps, Third-Party Apps and SAP BW 4/HANA Private Edition integration

Scenario 1: Use “out of the box” SAP insights App with “S/4 HANA” and “SAP Success Factors” as source systems and understand step by step on how the process works.

Below is the high-level architecture of the business data cloud with SAP Standard out of the box “Insight Apps” and using “SAP Apps”

Steps:

Step 1: Selecting the Right Insight App

The first step in leveraging SAP Business Data Cloud is to identify the right Insight App for your business scenario.

• In the SAP Business Data Cloud Cockpit, you can search for available Insight Apps and install the one that best meets your requirements.
  
• Each Insight App is designed to provide tailored insights into specific business functions.

Step 2: Data Access and Replication

Once the Insight App installation is initiated:

• Business Data Bundles – The relevant business data that has already been structured into a bundle within your business application (e.g., SAP S/4HANA and SAP SuccessFactors) is accessed.
  
• Data Replication – This business data is then replicated to the Foundation Services of SAP Business Data Cloud for further processing.

Step 3: Data Harmonization and Transformation

Inside the Foundation Services of SAP Business Data Cloud:

• Harmonization – The replicated business data is harmonized with other bundled data from different business applications (such as SAP SuccessFactors).
  
• Transformation & Enrichment – The data undergoes processing and enrichment to become a structured Data Product.
  
• Readiness for Analysis – At this stage, the Data Product is fully prepared for analysis, ensuring consistency and reliability across business functions.

Step 4: Generating Insights with SAP Datasphere

Once the Data Product is created:

• In SAP Datasphere, all necessary artifacts for analysis are automatically generated
  
• Visual insights are automatically generated and provided in various formats, including analytical dashboards.

Step 5: Generating Insights with SAP Data Bricks and Insights.

You can enhance your dashboard insights using AI and Machine Learning. Utilize the SAP Business Data Cloud Cockpit to share the relevant Data Product with SAP Databricks, where advanced AI and Machine Learning techniques generate refined results. These results are then integrated back into the data model, enriching your dashboard with AI-driven insights.

Conclusion:

SAP Business Data Cloud enables businesses to seamlessly integrate, harmonize, and analyze data from multiple sources, transforming raw data into actionable insights. By leveraging AI-driven analytics and automation, organizations can make data-driven decisions with confidence. With its out-of-the-box Insight Apps and seamless integration with SAP solutions, it provides a trusted and unified platform for enterprise data management.

The post Understanding the Technology Behind SAP Business Data Cloud (BDC) – Out of the box ‘Insight App” appeared first on ERP Q&A.

“Twenty-four-hour global nature of currency markets, exchange rates are constantly shifting from day to day and even from minute to minute, sometimes in small increments and sometimes quite dramatically” – Harvard Business Services.

In an interconnected world where businesses operate across borders, efficient currency conversion is essential. Currency conversion significantly impacts reporting and business analytics in the following ways:

Translation of Financial Statements:

Multinational corporations translate foreign incomes, expenses, assets, and liabilities into their reporting currency using relevant exchange rates.

Variances in Reported Financials:

Exchange rate fluctuations can result in significant variances in reported financials.

Currency conversion in SAP involves converting monetary values from one currency to another based on predefined exchange rates. This is important for multinational companies and businesses engaged in cross-border transactions.

Exchange rates in SAP define how one currency is converted to another. Let’s explore some significant business use cases where reporting with currency conversion is extensively required:

Case 1: Global Operations and Multinational Businesses

Companies operating in multiple countries need to manage different currencies. Currency conversion allows them to integrate financial data across various locations and ensure accurate financial reporting.

Case 2: Consolidated Financial Statements

Currency conversion in SAP enables the creation of consolidated financial reports in a single group currency.

Case 3: Budgeting and Forecasting

Companies often need to budget and forecast in a specific currency while dealing with costs and revenues in other currencies. Currency conversion allows for accurate planning and forecasting, providing a unified view of the organization’s financial health.

Currency conversion is used when posting financial information where the reporting currency differs from the transaction currency.

Currency Conversion Methods In SAP Datasphere

In an increasingly globalized business environment, companies often deal with transactions and data from multiple countries, involving various currencies. This complexity makes accurate currency conversion a critical aspect of financial reporting, budgeting, and analytics. SAP Datasphere, with its robust currency conversion capabilities, ensures businesses maintain financial accuracy and consistency across their operations.

Steps to Create Currency Conversion

Use Case

Step 01:

The client needs a report in Indian currency, but we have data in Datasphere in USD. So, using the currency conversion, we are converting from USD to INR.

Step 02:

Check whether the connection of the source is in an active state.

• Confirm access to Data flows, Tables, and Views, because running the data flow loads data into the local table before it becomes available in the views.
• To perform currency translation in SAP Datasphere, the following tables must be available in your space:

1. TCURV – Exchange rate types
   
2. TCURW – Exchange rate type text
   
3. TCURX – Decimal places in currencies
   
4. TCURN – Quotations
   
5. TCURR – Exchange rates
   
6. TCURF – Conversion factors
   
7. TCURC – Currency codes
   
8. TCURT – Currency text

• DataFlows

• Tables

• Views

Step 03:

• In this case we don’t have data in the target table, so we have to run the data flow to load the data to local table.

Step 04:

• Here, we are converting the currency from USD to INR.
  
• We have filtered “From Currency” as “USD” and “To Currency” as “INR” to obtain the exchange rate type.
  
• After that, obtain exchange rate types M & P for the scenario.

Step 05:

• For this scenario we have selected the source measure as “Gross Amount,” so we have to change the “Semantic Type” to “Amount with Currency” and “Unit Column” can be selected accordingly.

Step 06:

• We have selected the billing document date as the transaction date because we are using the billing document fact model.

Step 07:

• The conversion from USD to INR is now complete.

Best Practices for Currency Conversion

• Address rounding and precision issues:

Rounding Rules: Apply consistent rounding rules to avoid discrepancies in financial reports.

Precision: Ensure that rounding practices maintain accuracy, especially withlarge datasets.

• Maintain consistency and accuracy:

Accurate Data Entry: Ensure accurate entry of financial data and exchange rates to minimize errors during currency conversion.

Data Quality Checks: Regularly perform data quality checks to identify and rectify inaccuracies in exchange rates and financial data.

• Regular updates and monitoring of exchange rates:

Periodic Reviews: Conduct regular reviews of your currency conversion processes and update procedures as necessary to adapt to changing financial environments.

The post Importance of Currency Conversion in SAP Datasphere appeared first on ERP Q&A.

Many companies are using SAP Analytics Cloud (SAC) for planning alongside Power BI for reporting. However, users often request to utilize SAC planning data within Power BI. This integration can be challenging due to differences in data handling and capabilities. If you are facing similar demands, our blog provides a practical solution to this issue.

In this blog post, we will guide you through creating an export service for SAC that integrates seamlessly with Power BI using Azure Data Factory (ADF). This straightforward setup leverages the SAC Data Export Service API, ADF and Multi-Action API Step to ensure minimal compute usage and cost efficiency.

To demonstrate how this setup works in practice, we’ve created a demo video. Watch the video below to see how updating data in an SAC story seamlessly updates the data in Power BI. This will give you a clear understanding of the real-time integration between SAC and Power BI using ADF.

Prerequisites

Before we begin, ensure you meet the following prerequisites

• SAC Tenant, including Planning Model and related story
  
• Azure Data Factory Account
  
• Azure Storage Account in ADLS
  
• Power BI

Step-by-Step Guide

1. Creating the Data Export Pipeline in ADF
   
2. Triggering ADF Pipeline through Multi-Action API Step
   
3. Connecting Power BI with Azure Data Lake Storage
   
4. Running the entire process

Creating the Data Export Pipeline

The pipeline consists of four main parts:

1. REST Service: Create a REST service for the desired model.
   
2. Copy Table Names: Identifies all endpoints for the specified model.
   
3. Lookup Tables: Outputs the names obtained from “Copy Table Names”.
   
4. ForEach Loop: Uses the SAC REST API to retrieve data from the endpoints and copy it to ADLS.

To help you visualize the above-mentioned steps, we’ve created a short GIF that quickly demonstrates the process.

Setting up REST Service

1. Create a REST Service:

1.1 Set up a REST Service with base URL:
{your SAC tenant}/api/v1/dataexport/providers/sac/{model ID}

1.2 Choose “OAuth 2.0 Client Credential” for the authentications type. We will name this service “REST_EXPORT_API_DEMO”.

2. Generate OAuth Credentials:

2.1 In your SAC tenant, navigate to ‘System > Administration > App Integration’ to find your token endpoint and create a client to get the client ID and client secret.

Setting up “Copy Table Names”

1. Create a REST Dataset:

1. Set up a REST dataset using the linked service “REST_EXPORT_API_DEMO”.
   Name this table “Tables”.

2. Data Source Configuration:

1. In “Copy Table Names” select the REST dataset you just created.

2. Create a CSV file to store the table names. The file path should be:
{Storage account}/{model ID}/Table_names.csv
This will be used as the sink for “Copy Table Names”.

3. Use the just created CSV file as the sink dataset.

4. Ensure that the mapping includes only the table names.

Setting up “Lookup Tables”

Use the sink dataset from the “Copy Table Names” step as the source for “Lookup Tables”.

Configuring the “ForEach” Loop

Under settings, set the “Items” to: @activity(‘Lookup tables’).output.value

This configuration ensures the loop iterates over all table names, using them in the subsequent copy data activities within the loop.

Configuring “Copy Data” within the “ForEach” loop

1. Set up a new REST Dataset: This will be called “REST_API_ForEach”. We will use the “REST_EXPORT_API_DEMO” as linked service, but will now add a dynamic relative URL.

2. Setting up Source: We will use the above dataset as source and the item setting that configured under “Configuring the “ForEach” Loop”. This way we will access the endpoint for each table in the given model.

3. Creating a dynamic JSON sink dataset: The JSON sink dataset is created such that we will have one unique JSON file for each table in the model, which will inherit its name from the model.

Setting the sink in “Copy Data”: We now simply use the above created table along with the “Items” configured under “Configuring the “ForEach” Loop”.

The ADF pipeline is now fully configured and should export the model from SAC into the Azure blob storage, under the path defined in the first step of “Creating a dynamic JSON sink dataset”.

Triggering ADF Pipeline through Multi-Action

Register an App in Azure

1. Go to ‘Home > App Registration > Endpoint‘ and copy the Token URL:
   “OAuth 2.0 token endpoint (v2)”.
   
2. Go to ‘Home > App Registration‘ and click ‘+ New Registration‘.
   
3. Name your app and select ‘Accounts in this organizational directory only (Single tenant)’.
   
4. Copy the ‘Application (client) ID‘, this is your “OAuth Client ID”.

Configure OAuth Credentials

1. Go to ‘Certificates & Secrets’ and create a new client secret. Copy the ‘Value‘ as your client secret.

2. Ensure that the created App, “SAC”, has the permission ‘user_impersonation‘.

3. Assign the app the role of ‘Data Factory Contributor‘:
‘Azure Portal > Data factories > Your Data Factory > Access Control (IAM) > Add Role Assignment‘.

Create the Connection in SAC

Create a connection using the OAuth Client ID, Secret and OAuth 2.0 token endpoint (v2) retrieved in the previous steps.

The OAuth client ID is a unique, autogenerated ID that identifies your Azure application to SAP Cloud Platform and verifies its authorization to access requested resources. The specific OAuth client ID used above is given as the “Application ID” retrieved in the last step of “Register an App in Azure”.

The secret is a confidential password that is associated with the client application. It is used by SAC to prove its identity to the Azure platform. It is the ‘Value’ retrieved in the first step of “Configure OAuth Credentials”.

The “Token URL” is the endpoint that SAC will use to request an access token. The access token is a temporary authorization that is used by the client application to access the requested data or services.

Creating the Multi-Action

• Add a data action: to publish changes made to the story before exporting.

Configure the export API step: To do this, choose the connection made earlier and use the API URL:

The URL is given by:
https://management.azure.com/subscriptions/{Subscription ID}/resourceGroups/{Resource Group Name}/providers/Microsoft.DataFactory/factories/{Data Factory ID}/pipelines/{Pipeline ID}/createRun?api-version=2018-06-01

Secondly, choose the request method “Synchronous Return”.

• Add the Multi-Action to a story which is uses the same model as the one in ADF.

Connecting Power BI with ADLS

Retrieve the Endpoint of the ADLS

• Go to your storage account in Azure where the tables are saved and search for endpoints.
  
• Copy the Data Lake Storage endpoint (not the resource ID).

Configure Power BI

• In Power BI, click ‘Get Data‘ and select ‘Azure Data Lake Storage Gen 2‘.
  
• Paste the endpoint and add the path to the folder:
  https://{storageaccount}.dfs.core.windows.net/{Blob container}/{model_id}

Unpack Data

For each table, unpack the data. Repeat this process for all tables.

Running the entire process

1. Modify the Story in SAC: Make changes and trigger the multi-action.
   
2. Monitor ADF: Go to ADF and ensure the pipeline is triggered and completed successfully.
   
3. Refresh Power BI Data: Refresh the data in Power BI to reflect the changes.

The post Connecting SAP Analytics Cloud to Power BI Using Azure Data Factory appeared first on ERP Q&A.

Sending Data from Datasphere to SAC via OData Service

Our goal for this task is to consume actuals from Datasphere into SAC for reporting and planning purposes. We will combine the actuals from Datasphere with planning data in SAC.

Here is a list of steps we took to import Datasphere actual data into SAC:

1. Identify the OData Source (Data Service URL) in Datasphere

In order for us the identify the Data Service URL we went to the following link. Within this URL, you can see all the assets available within the spaces available in your Datasphere tenant.

Within this URL, we copied the assetRelationDataURL for the dataset we required. This is your Data Service URL. We then looked up our chosen assetRelationDataURL using the URL in the figure below. This will get us the meta data from the OData Service.

NOTE: To view the data from your chosen asset, add the dataset name again at the end of the URL.

2. Lookup your redirect URI in SAC

The redirect URI in SAC is the specific endpoint URL. The URI is the destination to which the authentication server redirects the user’s browser after authentication has been successfully completed.

We added an OData Services connection in our SAC tenant. We then change the Authentication Type to OAuth 2.0 Authorization Code. At the bottom of this window, we can see the Redirect URI.

3. Create the OAuth Client in Datasphere

We now want to create an OAuth client in Datasphere. We need the Redirect URI from the above step to create the OAuth client. We do this by going to System >> Administration >> App Integration and click on the ‘Add a New OAuth Client’. Once you paste the Redirect URI and click Add, you’ll get a list of the following information which you will need to take a note of.

• OAuth Client ID
  
• Secret
  
• Authorization URL
  
• Token URL

4. Create OData Connection in SAC

Within SAC, we go to Connections >> Add a Connection and chose OData Service. You need to now add the following connection parameters:

• Data Service URL = assetRelationalDataUrl
  
• OAuth Client ID
  
• Secret
  
• Token URL
  
• Authorisation URL

5. Import from OData Connection

We now need to create a model in SAC. We chose OData Services as the data source and click the new connection we just made. We then create a new query and select which dimensions and measures you want when building that query.

You can see that we have now consumed actuals data from datasphere to SAC! At this stage you can clean data and fix any issues. You have successfully imported the actuals data from OData Connection!!

6. Create Story and Test

As you can see here, we created a story. We added a table to our story. We chose the query we created above as the data source for the table. We added ‘Category’ as a column and using the version management functionality we can choose both the Actual and Plan category. This shows Actual data from Datasphere and Plan data within SAC.

The post Integration Between SAP Datasphere and SAP Analytics Cloud appeared first on ERP Q&A.

Understanding SAC Performance Analysis

Performance analysis in SAC encompasses an array of metrics, including stability, speed, scalability, and responsiveness. Essentially, it’s about how efficiently the platform handles workloads, from loading dashboards to executing queries.

You can access it by, Choose System > Performance

And there are some tools that can help you with the whole performance analysis paradigm. Let’s discuss them.

Measurement Tool

The Measurement Tool within SAC serves as a compass for gauging your system’s performance. By assessing factors like client time, network latency, and SAC responsiveness, it provides valuable insights into areas that demand improvement. The normalized scores generated by the Measurement tool offer a Measurement against best practices, guiding optimization efforts effectively.

The Measurement Tool tab consists of two Performance tests which are Client test and Network test.

(The above test is done in our internal system where Client score is 83 and Network latency is 280ms.)

• The client Measurement tool calculates a normalized score that can be compared against best practices.
  
• Network tests are essential for measuring client latency and bandwidth to the SAP Analytics Cloud Service.
  
• Both CPU-intensive applications and network performance significantly impact load times for Stories and Analytic Applications.
  
• It is crucial to be mindful of these factors to ensure optimal system performance.

Performance Analysis Tool

SAC’s Performance Analysis Tool revolutionizes the way administrators monitor system performance. Gone are the days of grappling with complex tools and manual data interpretation. This tool automates the collection and storage of performance information, empowering administrators to identify and resolve issues swiftly. With features like Page Load Time and Widget Drilldown, administrators can pinpoint bottlenecks and optimize performance at a granular level.

To begin using the tool, you can narrow down your search criteria by specifying a combination

of Date, Resource, and/or User.

• we have a selected a Resource and chosen a story

• We have used the below story for demonstrating Performance analysis tool

• Upon selecting the Story that we want to analyze; we have presented with the initial view of Performance analysis tool.
  
• The tool calculates the time at which pages, widgets, and model but no specific methods are there to control load time.

Page Load Time

• It’s mainly used to dig deeper into the story by looking at widgets, models, and pages. It gives a list of items sorted by how long they take to load, starting with the slowest.
  
• It displays time for Pages in a deeper level like page preparation time. Maximum/Minimum backend time etc.

Widget Drilldown

• It is used to drill down the widget wise measure’s which is used to calculate Widget time, Network time and Backend time like shown in the below image

• When clicking any particular widget or action it pops up a certain window that displays the front-end time, connected Model and Backend Request.

• If any particular widget is selected it changes the below graph which calculates Median backend time, Median Network time, Median Frontend Time.

Runtime Distribution

• Below image shows the runtime distribution. It displays time for widget, backend, network, frontend.

Unveiling Insights with Statistics and Analysis

The Statistics and Analysis tool in SAC bridges the gap between technical backend operations and user-facing frontend experiences. By providing comprehensive statistical analysis, it offers a holistic view of system performance. From backend query execution times to frontend load times, this tool equips administrators with the insights needed to fine-tune performance across the board.

• The Overview Page displays KPIs for Number of Logons, Frontend and Backend Kpi’s as well as Top 10 Users by Logon Time and Top 10 Stories by Load Time.

• In SAP Analytics Cloud, a model can be used in multiple stories. Sometimes, the model itself works fine, but when combined with a specific story, it can cause performance issues. We identify these combinations that have the most impact on the backend system. This helps us understand which specific combinations of models and stories might be causing slowdowns or other problems in the system.

• We use this charts to see how quickly query get answers and how many times they have run over time. We also check where the time goes in the process of running this query. This helps us understand how well the query are doing and if they’re getting faster or slower over time

The Importance of SAC Performance Analysis

In today’s data-centric landscape, SAC performance analysis isn’t just a nicety—it’s a necessity. Here’s why:

• Optimized Efficiency: By identifying and rectifying performance bottlenecks, SAC performance analysis streamlines operations, ensuring optimal efficiency.
  
• Enhanced User Experience: A responsive and stable SAC platform translates to a seamless user experience, fostering user adoption and satisfaction.
  
• Cost Savings: Proactive performance analysis helps mitigate the risk of system downtime and inefficiencies, ultimately resulting in cost savings for organizations.
  
• Data-Driven Decision Making: With SAC performing at its peak, organizations can trust the accuracy and timeliness of the insights derived, facilitating informed decision-making.
  
• Competitive Advantage: Leveraging SAC performance analysis to continuously optimize the platform provides organizations with a competitive edge, enabling them to stay ahead in today’s dynamic business landscape.

In conclusion, SAC performance analysis isn’t just a technical endeavor—it’s a strategic imperative. By harnessing the power of performance analysis tools, organizations can unlock the full potential of SAC, driving innovation, efficiency, and success.

So, embark on the journey of SAC performance analysis today, and witness firsthand the transformative impact it can have on your organization’s analytical capabilities and bottom line.

The post Unveiling SAP Analytics Cloud Performance Analysis Insights for Optimal Performance appeared first on ERP Q&A.

Canvas Layout: Provides users with precise control over the placement and size of dashboard elements.

Responsive Layout: Designed to adapt seamlessly to various screen sizes and resolutions, ensuring optimal viewing experiences across different devices.

In this blog, we’ll delve into some of the intriguing new features available within these two Page Types. These features will elevate your dashboards, providing enhanced flexibility and usability for your analytics endeavors.

Canvas Layout

Canvas pages are ideal in providing meticulous control over the size and positioning of objects, making them perfect for projects requiring consistency across various devices and screen resolutions.

When developing an intricate dashboard with a variety of charts, indicators, and interactive elements, along with overlapping components that go beyond standard charts and tables, opting for a canvas layout ensures precise pixel alignment and meticulous attention to detail.

SAP Analytics Cloud Canvas layout

Sizing And Positioning in Canvas Layout

Now, with the Optimized Design Layout, you have the option to make charts responsive by switching their size measurement from pixels to percentages. Yet, it is worth noting that tweaking each chart’s size and placement entails manual work. The Canvas layout is best suited for specialized needs where its benefits outweigh the extra effort required.

Let us picture a sleek dashboard in a Responsive page. Widgets and icons adapt well to different devices, but they struggle to stay centered. Though seemingly small, these alignment issues can detract from the dashboard’s professional appearance.

Now envision the same dashboard recreated within the pixel-perfect Canvas page. Every element stays precisely where you positioned it, accurately.

At 100 percent zoom, we can see that the charts are perfectly aligned.

At a zoom level of 60%, it is evident that the widgets are shrinking in size.

This drawback in the Canvas layout highlights its limited adaptability. However, this concern can be overcome by adjusting the Width and Height parameters as percentages. This modification allows charts and widgets to dynamically resize and adapt to different screen sizes, enhancing overall flexibility.

Canvas Layout, concisely:

• Allows pixel-perfect control over object positioning.
  
• Ideal for creating complex layouts with intricate designs.
  
• Enables overlaying objects to create visually appealing dashboards.
  
• Fixed layout ensures consistent appearance across different resolutions.

Responsive Layout

Responsive pages are specifically crafted to adjust seamlessly to diverse screen sizes and resolutions, catering perfectly to dashboards intended for viewing on mobile devices or any other platforms with varying screen dimensions.

When prioritizing accessibility across a range of devices, fostering collaborative development, and future-proofing your design, Responsive layouts come across as the preferred choice. Their innate adaptability and capacity to provide an optimal user experience make them well-suited for most storytelling needs within SAP Analytics Cloud..

Rules configuration in Responsive layout

Responsive rule configuration provides designers with the ability to create dynamic and adaptable layouts tailored to their content. By establishing rules for various devices, designers can manage the positioning, sizing, and visibility of widgets, ensuring a consistent and optimized user experience. This configuration enables content to seamlessly adapt to different screen sizes, ensuring a visually appealing and engaging presentation across desktops, laptops, tablets, and smartphones.

Upon examination, when the device is set to ‘auto’, all widgets and charts appear correctly.

However, upon switching the device setting to ‘iOS Small Tablet’, it becomes evident that the widgets in the highlighted top lane are collapsed and illegible. To address this issue, responsive rules can be implemented to dictate how lanes behave across different devices.

By activating the ‘Responsive Rule Configuration’ within the right panel, designers can specify whether widgets should be ‘Free’ or ‘Auto-Flow’. Additionally, adjustments to the width and height can be made, as necessary. After modifying these dimensions, it can be observed that the widgets are now appropriately placed, ensuring a seamless and optimized viewing experience across various devices.

Responsive Layout, concisely:

• Automatically adjusts the layout based on the screen size and device type.
  
• Supports responsive rule configuration for controlling element positioning and visibility.
  
• Provides flexibility in arranging widgets within lanes.
  
• Widgets are arranged and sized using a flexible grid system that adapts to different screen sizes.
  
• Set specific rules for how elements adapt to different screen sizes.
  
• Rendering on mobile devices for mobile-first design
  
• Built-in scripting support for advanced customization

Canvas Vs. Responsive

Choosing the right tool for data visualization and analysis

The primary objective of SAC Stories is to craft immersive and visually compelling data narratives. These narratives serve as dashboards or reports, presenting data insights in a concise and engaging manner for effortless comprehension. When it comes to SAP SAC data visualization, selecting between Canvas and Responsive Page hinges on the unique requirements and preferences of your organization.

The challenge for businesses in choosing between Canvas and Responsive Page lies in weighing the trade-offs between precision and adaptability. Canvas offers precise control over layout and positioning, ideal for intricate designs, but requires manual adjustments for different devices. In contrast, Responsive Page ensures adaptability across devices but may sacrifice pixel-perfect alignment and creative freedom. Businesses must assess their priorities, considering factors like design complexity, user experience, and resource availability, to make an informed decision. Here are some factors you should consider while deciding between choosing a canvas or responsive layout:

Design Requirements: Determine the level of precision and control needed for your project. If pixel-perfect alignment and intricate designs are essential, Canvas may be the preferred choice. For projects prioritizing adaptability and accessibility across diverse devices, Responsive layouts are more suitable.

User Experience: Consider the end user’s experience and the devices they are likely to use. If your audience primarily accesses dashboards on mobile devices or tablets, Responsive layouts ensure optimal viewing experiences. However, if consistency in layout and design across different devices is critical, Canvas layouts may be more appropriate.

Collaboration and Future Needs: Evaluate the collaborative nature of your project and the potential for future updates or changes. Responsive layouts facilitate collaborative development and accommodate future changes more seamlessly. Conversely, Canvas layouts offer more creative freedom but may require additional effort to maintain consistency and make updates.

Conclusion

In conclusion, choosing between Canvas and Responsive layouts in SAP Analytics Cloud depends on the project’s specific needs and priorities. While Canvas offers pixel-perfect control and creative freedom, Responsive layouts provide adaptability and accessibility across various devices. Selecting the right layout ensures a harmonious blend of design precision and user experience optimization.

The post Responsive vs. Canvas in SAP Analytics Cloud with Optimized Design Experience appeared first on ERP Q&A.

Required:

• Datasphere tenant.
• Power BI.
• Need to create a database user for the particular space in the space management.
• Host name, Port number, Password, Database username.
• Need to add External IPV4 Address in the IP Allowlist.
• Hana ODBC client.
• Need to add credentials in Power BI.

Datasphere part:

Login into Datasphere -> Space Management -> Choose the space and select Edit.

Click Create and Make sure that you have enabled Expose for consumption by default

Copy Database Username, Host name, Port, Password.

Go to System-> Configuration-> IP Allowlist-> Trusted Ips

EXTERNAL IPV4 ADDRESS should be added here, not Internal IPV4

To get External IPV4 Address

• Open Command prompt and enter curl ifcfg.me
• Then run curl ifcfg.me in command prompt and add that IPV4 Address in the IP Allowlist.

Add and Save the External ipv4 address here.

ODBC PART:

Need to install SAP HDODBC driver SAP Development Tools (ondemand.com) in the system.

Open ODBC in the system

Click Add

Select HDODBC

• Give any meaningful name to Data source name, description.

• Database type: SAP HANA Cloud or SAP HANA Single tenant (both will work fine).

• Already copied Host URL in datasphere space, Paste the copied Host URL.

• Click Test connection

• Paste the Database username in Username and Password.

POWER BI PART:

Power BI-> Get data-> ODBC.

Select the created ODBC Connection

Paste the same Username and Password which copied in datasphere part, click “Connect”.

Successfully Power BI consuming data from SAP Datasphere

Dashboard built in Power BI

From this POC:

• Data from Datasphere can be consume via OBDC connection 100/100.

• Since ODBC connection is possible, we can consume data into any third-party reporting tools that we want.

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