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  3. SAP Business Data Cloud
Logo of SAP Business Data Cloud

SAP Business Data Cloud

bySAP
in
4.4
Market Presence: Metadata Management Solutions, Data Integration Tools

Overview

Product Information on SAP Business Data Cloud

Updated 12th January 2026

What is SAP Business Data Cloud?

SAP Business Data Cloud is a software designed to enable organizations to unify, manage, and analyze data from various sources across on-premise and cloud environments. The software provides capabilities for data integration, governance, and sharing, aiming to break down data silos and enhance data-driven decision-making. It supports data modeling, cataloging, and secure collaboration, while offering built-in tools to ensure compliance and data quality standards are met. By streamlining access to trusted business data, the software helps address challenges related to fragmented data landscapes and supports better analytics and business outcomes.

SAP Business Data Cloud Pricing

SAP Business Data Cloud software uses a subscription-based pricing model, typically structured by user, data storage, and usage requirements. Pricing may vary based on the level of features, data capacity, and integration options selected. Additional costs can apply for advanced modules, add-ons, or support services, with detailed quotes often provided upon request or following a needs assessment.

Overall experience with SAP Business Data Cloud

Data and Analytics Manager
10B - 30B USD, Manufacturing
FAVORABLE

“Unified data and AI platform that delivers where the SAP fit is right”

5.0
May 6, 2026
This text serves as a placeholder and does not reflect the user’s review responses or opinions. This text serves as a placeholder and does not reflect the user’s review responses or opinions. This text serves as a placeholder and does not reflect the user’s review responses or opinions.
Data Analyst
500M - 1B USD, Manufacturing
CRITICAL

“Technical Challenges and Performance Issues Complicate SAP Metadata Integration Efforts”

3.0
Mar 11, 2026
This text serves as a placeholder and does not reflect the user’s review responses or opinions. This text serves as a placeholder and does not reflect the user’s review responses or opinions. This text serves as a placeholder and does not reflect the user’s review responses or opinions.

About Company

Company Description

Updated 13th August 2025

SAP was founded in 1972 and is headquartered in Walldorf, Germany. The company employs over 105,000 people globally and develops software solutions for enterprise resource planning (ERP) and related business functions. SAP’s early products, SAP R/2 and SAP R/3, were widely adopted for managing core business processes. Its current ERP platform, SAP S/4HANA, uses in-memory computing to support data-intensive operations and integrates capabilities such as artificial intelligence and machine learning. SAP offers a portfolio of software applications that support various business functions across industries. These applications are designed to operate on a unified digital platform. As of 2025, SAP reports over 230 million cloud users and provides more than 100 solutions. The company’s offerings are used by organizations to manage finance, human resources, procurement, supply chain, and other operational areas.

Company Details

Updated 13th August 2025
Company type
Public
Year Founded
1972
Head office location
Walldorf, Germany
Number of employees
10001+
Annual Revenue
30B+ USD
Website
http://www.sap.com

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A Snapshot of What Matters - Based on Validated User Reviews

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Peer Discussions

SAP Business Data Cloud Reviews and Ratings

4.4

(31 Ratings)

Rating Distribution

5 Star
48%
4 Star
45%
3 Star
6%
2 Star
0%
1 Star
0%
Why ratings and reviews count differ?
  • Data and Analytics Manager
    10B+ USD
    Manufacturing
    Review Source

    Unified data and AI platform that delivers where the SAP fit is right

    5.0
    May 6, 2026
    Our experience with SAP Business Data Cloud has been encouraging, though we're still in the earlier stages of getting full value out of it. We came in with a fragmented data landscape across SAP and non-SAP systems, and the promise of a unified, semantically consistent layer was the main reason for going down this path. The biggest win so far is that business context travels with the data. Earlier, when finance and operations pulled the same KPI, we'd often end up debating definitions before we could even discuss the numbers. With data products carrying the SAP semantics intact, that argument has largely gone away. The integration with SAP Analytics Cloud means dashboards and planning sit on top of the same governed layer, which removes a class of reconciliation work we used to do manually. The Databricks integration has been useful for the AI and ML side of things. Being able to share data products with Databricks for processing without copying data around is a meaningful change from how we used to handle similar workloads. That said, the platform is still maturing. Some of the prebuilt data products and intelligent applications we expected to use are arriving in waves rather than being fully available on day one, so our roadmap has had to flex around SAP's release cadence. Documentation is reasonable for the core flows but thinner once you step off the well-trodden path. And anyone coming in without an SAP background, particularly on the Datasphere side, will need real onboarding time before they're productive. Overall, it's been a worthwhile investment for an SAP-heavy organisation, but it's not a plug-and-play story.
  • Data and Analytics Manager
    10B+ USD
    Manufacturing
    Review Source

    Unified data and AI platform that delivers where the SAP fit is right

    5.0
    May 6, 2026
    Our experience with SAP Business Data Cloud has been encouraging, though we're still in the earlier stages of getting full value out of it. We came in with a fragmented data landscape across SAP and non-SAP systems, and the promise of a unified, semantically consistent layer was the main reason for going down this path. The biggest win so far is that business context travels with the data. Earlier, when finance and operations pulled the same KPI, we'd often end up debating definitions before we could even discuss the numbers. With data products carrying the SAP semantics intact, that argument has largely gone away. The integration with SAP Analytics Cloud means dashboards and planning sit on top of the same governed layer, which removes a class of reconciliation work we used to do manually. The Databricks integration has been useful for the AI and ML side of things. Being able to share data products with Databricks for processing without copying data around is a meaningful change from how we used to handle similar workloads. That said, the platform is still maturing. Some of the prebuilt data products and intelligent applications we expected to use are arriving in waves rather than being fully available on day one, so our roadmap has had to flex around SAP's release cadence. Documentation is reasonable for the core flows but thinner once you step off the well-trodden path. And anyone coming in without an SAP background, particularly on the Datasphere side, will need real onboarding time before they're productive. Overall, it's been a worthwhile investment for an SAP-heavy organisation, but it's not a plug-and-play story.
  • Read All 36 Reviews

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User Sentiment About SAP Business Data Cloud
Reviewer Insights for: SAP Business Data Cloud
Performance of SAP Business Data Cloud Across Market Features

SAP Business Data Cloud Likes & Dislikes

Like

The single biggest thing for us is that data keeps its business meaning as it moves around. With our earlier setup, every time data left the source system it lost context, and analysts spent more time reconciling definitions than actually analysing anything. SAP's data products carry the semantics with them, so a sales figure means the same thing whether you're looking at it in a dashboard, a planning model, or feeding it into an ML pipeline. That alone has cut down a lot of unproductive back-and-forth between teams. The unified setup across Datasphere, Analytics Cloud, and Databricks is the other strong point. Earlier we were paying for and operating these as separate pieces, with effort going into keeping them in sync. Having them under one managed roof, with the federation working in real time rather than us copying data around, has simplified the architecture considerably and reduced the storage footprint we were carrying. I'd also call out the BW modernisation path. We have years of investment sitting in our existing Business Warehouse, and the ability to bring those objects in as data products without a rip-and-replace exercise made the business case much easier to defend internally. It let us move forward without writing off what we'd already built.

Like

The single biggest thing for us is that data keeps its business meaning as it moves around. With our earlier setup, every time data left the source system it lost context, and analysts spent more time reconciling definitions than actually analysing anything. SAP's data products carry the semantics with them, so a sales figure means the same thing whether you're looking at it in a dashboard, a planning model, or feeding it into an ML pipeline. That alone has cut down a lot of unproductive back-and-forth between teams. The unified setup across Datasphere, Analytics Cloud, and Databricks is the other strong point. Earlier we were paying for and operating these as separate pieces, with effort going into keeping them in sync. Having them under one managed roof, with the federation working in real time rather than us copying data around, has simplified the architecture considerably and reduced the storage footprint we were carrying. I'd also call out the BW modernisation path. We have years of investment sitting in our existing Business Warehouse, and the ability to bring those objects in as data products without a rip-and-replace exercise made the business case much easier to defend internally. It let us move forward without writing off what we'd already built.

Like

The single biggest thing for us is that data keeps its business meaning as it moves around. With our earlier setup, every time data left the source system it lost context, and analysts spent more time reconciling definitions than actually analysing anything. SAP's data products carry the semantics with them, so a sales figure means the same thing whether you're looking at it in a dashboard, a planning model, or feeding it into an ML pipeline. That alone has cut down a lot of unproductive back-and-forth between teams. The unified setup across Datasphere, Analytics Cloud, and Databricks is the other strong point. Earlier we were paying for and operating these as separate pieces, with effort going into keeping them in sync. Having them under one managed roof, with the federation working in real time rather than us copying data around, has simplified the architecture considerably and reduced the storage footprint we were carrying. I'd also call out the BW modernisation path. We have years of investment sitting in our existing Business Warehouse, and the ability to bring those objects in as data products without a rip-and-replace exercise made the business case much easier to defend internally. It let us move forward without writing off what we'd already built.

Dislike

The implementation time is very long due to technical challenges like memory consumption, performance issues and product bugs.

Dislike

The implementation time is very long due to technical challenges like memory consumption, performance issues and product bugs.

Dislike

The implementation time is very long due to technical challenges like memory consumption, performance issues and product bugs.