Step-by-Step Guide: Building an Analytical Dashboard in SAP Data Sphere Using Sales Order Data

Introduction:

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!

SAP B1 Certification Preparation Guide

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.