To BDC Part 4: Generating Data Products from S/4HANA into BDC and SAC

Let’s talk this week about something we all love but rarely define clearly: “data products”.  Or, data as a product. Not datasets. Not queries. Not dashboards that break when someone changes a filter. We’re talking about clean, documented, reusable, governed data assets with a purpose—a customer sales view, an inventory position, a profit margin rollup—delivered at the speed of business, not the speed of your BW ticket queue.

With SAP Business Data Cloud (BDC), you finally have the platform to turn your SAP data into strategic, shareable products. So how do you do it?

In this post, we’ll walk through a step-by-step example of building a data product using SAP S/4HANA standard extractors (CDS based extractors are required for datasphere – see below), flowing through Datasphere, and surfacing beautifully in SAP Analytics Cloud (SAC). We’ll even throw in the BW PCE version of the same flow for those of you modernizing at your own pace.

The Use Case

Let’s say you want to build a Customer Sales Performance dashboard for your sales and finance teams. It needs to pull data from SAP S/4HANA and show metrics like:

• Net sales by region and customer
• Open orders
• Returns
• YTD targets vs actuals

Oh, and it needs to refresh daily, be filterable in SAC, and reuse definitions across future reporting projects. Here’s how you do it.

Example High level Step-by-Step: From S/4HANA Extractors to BDC Data Product

1. Identify the Source Extractors in S/4HANA

Start in S/4. Identify the standard SAP content that gives you what you need—typically in the form of CDS Views (I_*), ABAP-based extractors, or ODP extractors.

For our example, we see these types of extractors available in our system:

• 2LIS_11_VAITM (Sales Order Item Data)
• 2LIS_13_VDITM (Billing Document Items)
• 2LIS_11_VAKON (Conditions/Discounts)
• Or CDS Views like I_SalesOrderItemCube if your S/4 system supports it (I much prefer this these days)

*For datasphere, you can no longer use 2LIS extractors, so you either have to convert your extractors to an ABAP based CDS view or use your BW PCE environment to generate a data product. 

2. Set Up the Connection in Datasphere (BDC)

In SAP BDC’s Datasphere workspace:

• Use the ODP connector to connect to your S/4HANA system
• Configure replication or real-time access depending on latency/performance/modeling needs
• Choose to load data via data flows or set up remote tables for federated access

Success!  You now have access to raw SAP extractor data in your Datasphere space

3. Create a Data Flow to Model and Transform

In Datasphere:

• Build a data flow object
• Bring in the replicated/exposed tables
• Apply your joins, filters, calculated columns (e.g., net sales = quantity x price – discounts)
• Aggregate or stage the data if needed
• Write to an analytical model or output table

This is your technical modeling layer—turning raw tables into business logic.

4. Build a Business Analytical Model

Now make it semantic:

• Create an Analytical Dataset
• Add dimensions (e.g., region, customer, product)
• Tag measures like net sales, returns, open orders
• Define hierarchies, units, currencies, and expose filters

Think of this as your API-ready data product—clear, reusable, and trusted.

5. Expose to SAP Analytics Cloud (SAC)

Head into SAC:

• Connect to your BDC Datasphere tenant via live connection
• Consume the Analytical Dataset directly in a Story, Dashboard, or Analytics Application
• Build the visualization layer—KPIs, drilldowns, filters, charts

Bonus: Use SAC’s Smart Insights or Joule integration to let business users explore the “why” behind the numbers.  (This has licensing implications and availability implications still currently.  Check to see what you have and if you can enable it/use it).

What Makes This a “Data Product”?

✔ Reusable logic
✔ Governed, trusted source
✔ Clearly named, semantic context
✔ Available across tools (SAC, Python, Databricks, APIs)
✔ Aligned to business outcomes

This is the shift. From “1 report” to data asset.

Alternate Route: From BW PCE to Datasphere

On BW PCE? You’re not left behind.

This works for you too:

1. Use BW Bridge in Datasphere to connect to your BW PCE tenant
2. Expose CompositeProviders or Queries as remote tables
3. Use those objects in data flows inside Datasphere
4. Apply any further enrichment (transformations, joins, filters)
5. Publish as Analytical Datasets just like above
6. Feed into SAC, AI, or Planning

BW PCE still holds valuable logic. Don’t waste it—repurpose it.

How Mindset Helps with Data Products

We’ve done this before. And we know where the dragons live.

Here’s what we offer:

Extractor Mapping & Rationalization – We help you match SAP technical objects to actual business needs
Semantic Layer Design – Standardize naming, definitions, and ownership of key business measures
BDC Architecture – From S/4 to Datasphere to SAC, we design the whole stack
Reusable Data Product Templates – We create blueprint-style artifacts so you can scale fast
Enablement & Governance – We teach your team how to fish—and how not to blow up the kitchen

TL;DR

SAP Business Data Cloud isn’t just about centralizing data. It’s about activating it.

By building structured, reusable data products—from S/4HANA or BW PCE—you unlock real business value: faster decisions, fewer reporting fires, and a foundation for AI and planning that doesn’t require a crystal ball.

So whether you’re starting greenfield in BDC, or bringing BW along for the ride, the future of data is semantic, governed, and “product-driven”.

Let’s help you build your first one. Or your fiftieth.

This blog is part of a 6 part series on SAP BDC written by Jonathan Bragg.
See
– part 1 HERE
– part 2 HERE
– part 3 HERE
– part 4 HERE
– part 5 HERE
– part 6 HERE