Picture 1: SAP BDC – Cash Flow Dashboard
MOTIVATION
In today’s fast-paced business world, the ability to effectively steer and lead an organisation is fundamental for achieving strategic goals and maintaining a competitive edge. A key success criterion is the capacity to fully harness and derive value from organisational data. Many companies struggle with data problems, such as fragmented information, outdated systems, and the super important task of preparing data for emerging technologies like AI. Recognising these challenges, we are excited to introduce a blog series dedicated to showcasing the substantial business value of SAP Business Data Cloud (BDC) in the context of planning and analytics.
The motivation behind this initiative is to provide a comprehensive exploration of how SAP BDC capabilities as essential instruments, facilitating robust strategic alignment and guiding effective decision-making processes across diverse organisational requirements. We aim to illustrate how SAP BDC, equipped with powerful data management, planning, analytics and innovative AI capabilities, empowers organisations with enhanced business steering agility by providing real-time insights, streamlined data management, and automated decision support – going from insights to action, helps mainly leaders guide their businesses with precision.
Our blog series will provide valuable insights for decision-makers seeking to harness SAP Business Data Cloud (BDC) to enhance operational efficiency, optimise resource allocation, and achieve long-term objectives. By featuring detailed planning & analytics topics, the series will illustrate the transformative impact of SAP BDC in addressing the operational complexities of today’s business environment. This first introductory blog post will spotlight the strategic advantages of SAP BDC, particularly in the context of Business Steering, setting the stage for a deeper exploration of its benefits throughout the blog series.
There are several blog posts in the series that will cover advantages of SAP BDC in the context of business steering, so please stay tuned for the upcoming blogs:
The Value of SAP BDC in the context of business steering Planning & Analytics is an essential part of SAP BDC Key Planning & Analytics components of SAP BDCIs AI going to “retire” a dashboard?Simulations, a game changer in today’s volatile worldExtend existing planning scenarios of SAP BPC/BW-IP in SAP BDC Real-time steering with LIVE planningPaPM – The role of business calculation engine in business steeringSelf – service analytics and real time business steeringBusiness steering powered by AI agentsLeverage data products in the context of planning & forecasting (THIS BLOG POST)Introducing AI/ML capabilities by SAP Databricks
INTRODUCTION
Just a few years ago, only 4 in 10 employees relied heavily on data in their daily work. Fast forward to today, and that number is expected to surpass 70%. That’s nearly double in less than a decade.
At the same time, we’re seeing an explosion in data volumes, driven not just by traditional sources, but by the rise of GenAI, which is generating massive amounts of synthetic data. By 2030, we’re looking at up to 10x growth in data volumes.
And all of this comes at a cost. Quite literally. The average cost of managing data has more than doubled over the past decade.
So while data is becoming a strategic asset, it’s also becoming a growing burden. The question isn’t just how to manage it, but how to turn it into a true competitive advantage without being overwhelmed by its complexity and cost.
If data are organized and harnessed as a product, then it can significantly reduce costs of data management and improve data discoverability and reusability. Specifically, a McKinsey study in a 2022 Harward Business Review suggests Data Products will reduce time for new case implementation for up to 90% and decrease TCO by up to 30%.
In this context data organized as Data Products also offer benefits to the planning and forecasting business processes.
WHAT IS A DATA PRODUCT?
Data products, just like traditional products, are standardized data assets designed to deliver measurable value to its users. They encompass all the essential components needed for their utilization: not just data, but also metadata, including governance policies, data quality rules, data contracts, and, where applicable, a software bill of materials to document their dependencies and components. Adhering to the FAIR principles – findable, accessible, interoperable, and reusable – a data product is designed to be discoverable, scalable, reusable, and aligned with both business and regulatory standards, driving innovation and efficiency in modern data ecosystems.
Picture 2: Data Products are searchable, discoverable and easy to consume logical packages of data
Data Products are also a central component SAP Business Data Cloud. With their delta sharing capability based on open-source Linux Foundation protocol they improve access to third party data and make SAP data more accessible to third party solutions. SAP data products and other key BDC components are described in detail by my colleague @Nektarios_Vasileiou33 in this blog Key Planning & Analytics components of SAP BDC
SAP DATA PRODUCTS
SAP-managed vs customer-managed Data Products:
Starting with SAP S/4HANA Cloud, SAP is providing a comprehensive set of SAP-managed data products across the Lines of Business (LoB). Customer only need to activate these predefined data products and then SAP takes care for extraction, loading and data curation. Here is an updated list and description of the currently available SAP Managed Data Products on our Business Accelerator Hub.Customer also can create their own customer-managed data products to enhance the set of data products with data from any SAP or non-SAP application. Customer-managed data products are curated in SAP Datasphere. The data and ORD file(s) are stored in the customer-managed object store within SAP Datasphere.Both type of data products are technically identical. They are stored in an object store, described via ORD file and published in the SAP BDC data catalogue.
Picture 3: SAP Managed Data Products in Datasphere Catalog
LEVERAGING DATA PRODUCTS IN PLANNING
Data products are valuable for corporate planning because they turn raw data into reusable, discoverable, and trustworthy assets that planners can access directly instead of waiting for bespoke extracts or ad-hoc reports. Easier access removes bottlenecks: planners spend less time requesting data and more time analyzing scenarios, which speeds decision cycles and improves responsiveness. A well-designed data product provides a single source of truth and consistent definitions (e.g., standardized customer, product, or revenue metrics), reducing confusion and disagreement across teams. When data is exposed through APIs, semantic layers, or curated data marts, non-technical users and analytical tools can query it reliably without bespoke engineering work. Built-in metadata, lineage, and documentation increase trust and make it easier to validate results for budgeting, forecasting, and risk assessments.
Data products also enable automation of recurring planning tasks—like rolling forecasts or variance analysis—so companies can run more frequent, higher-fidelity planning cycles with less manual effort. They support scenario modelling because productized datasets are versioned and auditable, letting planners compare alternative assumptions with clear provenance. By centralizing and governing access through SLAs, access controls, and data contracts, organizations reduce duplication of effort, lower data preparation costs, and decrease the chance of using stale or incorrect inputs in strategic decisions. Cross-functional collaboration improves because different stakeholders work from the same curated datasets, speeding alignment and reducing reconciliation work during planning meetings.
Picture 4: Leveraging Data products enables easier data access for planning
In the context of SAP Data products for planning these can be:
SAP managed: for most SAP based cloud applications (S/4HANA PCE and LoB solutions like SuccessFactors or Ariba) orCustom managed: customer defined for on-premise applications, partner based (i.e. PWC, McKinsey, etc.), 3rd party (i.e. weather, benchmark data, etc.) that could be coming from Data marketplace in Datasphere
STEP-BY-STEP EXECUTION BASED ON EXAMPLE
Net Working Capital Planning (based on NWC core data products)
We wish to perform Net Working Capital (NWC) planning based on actual and short term forecast data from S/4HANA. With the help of SAP delivered data products, we will significantly reduce the effort needed for data search, preparation and modelling. As a first step, we will search in BDC for the Cash Flow data product which is deployed and periodically loaded with fresh master and fact data from S/4HANA.
Picture 5: Out-of-the-box Cash Flow Data Product contains the actuals needed for our planning use case
The Cash Flow and several other data products like Journal Entry, Company code, etc. form the view and analytic model for New Working Capital in BDC. This large data set we will in the first step filter and limit the dataset to the dimensions that we need for planning. We keep it simple and decide to plan our NWC on Company Code and Profit Center so we use the Data Boulder in Datasphere to create a limited view. We also filter the facts to a specific year and currency.
Picture 6: Data Builder in Datasphere to help us limit the Data Product content to what we need for planning
Once our view is deployed, we can switch to SAP Analytics Cloud to build our planning data model. We navigate to Modeler and choose to create a new model, start with data and select we want to use a model from Datasphere where we just used several data products to design our NWC planning view. We select our view, and SAC will use its data structure to design the planning model for us.
Picture 7: Instead of building the planning model from zero, we use the Data Product view to auto-create the model
We get a model which includes the live version of NWC facts and the dimensions we defined in the Datasphere view.
Picture 8: Auto-generated planning model based on a data product view
We now must define semantics for our model columns and add master data wherever required. While the latter step currently requires some manual work, this will be dramatically reduced in 2026. SAP plans to deliver within the Seamless Planning Roadmap the possibility to re-use master data in SAC from Datasphere without the need of replication. In addition to master data creation, we also expand the time dimension to allow for the planning time horizon and configure the currency unit for the measure. Finally, we expose our planning model to Datasphere so we can apply the seamless planning approach and potentially make use of further data products if needed for our planning use case.
Picture 9: Finished SAP Analytics Cloud model ready for planning
Based on the created model, we can create a story and start planning our net working capital for 2026. Since our model includes a live version of actuals residing in my data product, we can start our planning based on actuals from 2025. Live Versions (stored externally to our planning model) can in Seamless planning be applied throughout the planning workflow—stories, data actions, predictions, calculations, and more. This feature introduced in Q4 2025 is in detail explained by @MaxGander in an excellent recent blog post.
Picture 10: Planning layout for net working capital planning based on data products
Extending NWC planning with HR data (based on SuccessFactors data products)
We decide we wish to include other data to refine our NWC planning and make it more comprehensive. We can perform another search in the data catalog of BDC and add other relevant data products. While NWC is primarily driven by the operating cycle (receivables, inventory, payables) in some businesses employee headcount and related compensation costs can have a significant indirect impact.
We perform a search in BDC data catalog and amongst multiple SAP-managed data products for SuccessFactors find the one relevant one for employee compensation data.
Picture 11: SAP-delivered Compensation data product
Similarly to the example with NWC data we would create a view on the Actual Compensation data product to focus and filter relevant data for planning.
Picture 12: Use of Data Builder in BDC to limit the Data Product content to what we need for planning
We can now add a new measure in our planning model on SAP Analytics Cloud to store compensation data and make use of External Live Versions to connect the Compensation View we created before.
Picture 13: Connecting another External Data Source from BDC in our model
We need to map the dimensions of our new compensation data source with the dimensions in our planning model.
Picture 14: Mapping of dimensions for the Live Compensation data
In final step we adjust our story that we already used before for NWC planning and copy the actual compensation data into a planning version. Now we can start flexibly using the new compensation driver to model a calculated measure or a data action that will allow us to simulate how plan compensation influences our NWC based on some logic that we define. Note that our case and data are simplified as we are focusing on BDC capabilities and not on business logic in this blog.
Picture 15: Story in SAP Analytics Cloud combining data from both views built on BDC data products
OTHER USE CASES
In addition to the use cases that we described in the above example, data products are also easily sharable due to the delta sharing capability. This allows for easy sharing of actual and plan data to third party tools that could be used to enrich our data. One such example is the potential to use SAP Databricks to apply ML on our plan data. We could use the ML results to automate parts of the planning process (i.e. apply an advanced time series forecasting algorithm in Jupiter Notebooks to actual data to generate a forecast). One of these examples will be described in detail in the blog Introducing AI/ML capabilities by SAP Databricks which is also part of this blog series to be published soon.
SUMMARY
In summary, there is considerable value in data products for planning, and it is measurable:
time-to-insight decreasesthe number of one-off data requests drops declined operational costs tied to manual data wrangling imporved forecast accuracy and plan-to-actual variance
To capture these benefits, companies should identify high-impact planning needs, expose datasets via discoverable interfaces (data products), and monitor usage and quality over time.
USEFUL LINKS
In case you are interested to implement the concept od data products and Data Fabric please refer to this article for implementation tips SAP What is article on Data Products.
Our architecture center includes some interesting architectural information on this topic Data Products in BDC.
In SAP Learning you can learn more on exploring and installing SAP Managed data products Learning.
Finally also our Help content describing the Data Sharing Cockpit in Datasphere is a valuable resource explaining how to create and manage the your data product’s lifecycle.
Picture 1: SAP BDC – Cash Flow Dashboard MOTIVATION In today’s fast-paced business world, the ability to effectively steer and lead an organisation is fundamental for achieving strategic goals and maintaining a competitive edge. A key success criterion is the capacity to fully harness and derive value from organisational data. Many companies struggle with data problems, such as fragmented information, outdated systems, and the super important task of preparing data for emerging technologies like AI. Recognising these challenges, we are excited to introduce a blog series dedicated to showcasing the substantial business value of SAP Business Data Cloud (BDC) in the context of planning and analytics. The motivation behind this initiative is to provide a comprehensive exploration of how SAP BDC capabilities as essential instruments, facilitating robust strategic alignment and guiding effective decision-making processes across diverse organisational requirements. We aim to illustrate how SAP BDC, equipped with powerful data management, planning, analytics and innovative AI capabilities, empowers organisations with enhanced business steering agility by providing real-time insights, streamlined data management, and automated decision support – going from insights to action, helps mainly leaders guide their businesses with precision. Our blog series will provide valuable insights for decision-makers seeking to harness SAP Business Data Cloud (BDC) to enhance operational efficiency, optimise resource allocation, and achieve long-term objectives. By featuring detailed planning & analytics topics, the series will illustrate the transformative impact of SAP BDC in addressing the operational complexities of today’s business environment. This first introductory blog post will spotlight the strategic advantages of SAP BDC, particularly in the context of Business Steering, setting the stage for a deeper exploration of its benefits throughout the blog series. There are several blog posts in the series that will cover advantages of SAP BDC in the context of business steering, so please stay tuned for the upcoming blogs:The Value of SAP BDC in the context of business steering Planning & Analytics is an essential part of SAP BDC Key Planning & Analytics components of SAP BDCIs AI going to “retire” a dashboard?Simulations, a game changer in today’s volatile worldExtend existing planning scenarios of SAP BPC/BW-IP in SAP BDC Real-time steering with LIVE planningPaPM – The role of business calculation engine in business steeringSelf – service analytics and real time business steeringBusiness steering powered by AI agentsLeverage data products in the context of planning & forecasting (THIS BLOG POST)Introducing AI/ML capabilities by SAP Databricks INTRODUCTION Just a few years ago, only 4 in 10 employees relied heavily on data in their daily work. Fast forward to today, and that number is expected to surpass 70%. That’s nearly double in less than a decade.At the same time, we’re seeing an explosion in data volumes, driven not just by traditional sources, but by the rise of GenAI, which is generating massive amounts of synthetic data. By 2030, we’re looking at up to 10x growth in data volumes.And all of this comes at a cost. Quite literally. The average cost of managing data has more than doubled over the past decade.So while data is becoming a strategic asset, it’s also becoming a growing burden. The question isn’t just how to manage it, but how to turn it into a true competitive advantage without being overwhelmed by its complexity and cost.If data are organized and harnessed as a product, then it can significantly reduce costs of data management and improve data discoverability and reusability. Specifically, a McKinsey study in a 2022 Harward Business Review suggests Data Products will reduce time for new case implementation for up to 90% and decrease TCO by up to 30%. In this context data organized as Data Products also offer benefits to the planning and forecasting business processes. WHAT IS A DATA PRODUCT?Data products, just like traditional products, are standardized data assets designed to deliver measurable value to its users. They encompass all the essential components needed for their utilization: not just data, but also metadata, including governance policies, data quality rules, data contracts, and, where applicable, a software bill of materials to document their dependencies and components. Adhering to the FAIR principles – findable, accessible, interoperable, and reusable – a data product is designed to be discoverable, scalable, reusable, and aligned with both business and regulatory standards, driving innovation and efficiency in modern data ecosystems.Picture 2: Data Products are searchable, discoverable and easy to consume logical packages of dataData Products are also a central component SAP Business Data Cloud. With their delta sharing capability based on open-source Linux Foundation protocol they improve access to third party data and make SAP data more accessible to third party solutions. SAP data products and other key BDC components are described in detail by my colleague @Nektarios_Vasileiou33 in this blog Key Planning & Analytics components of SAP BDCSAP DATA PRODUCTSSAP-managed vs customer-managed Data Products:Starting with SAP S/4HANA Cloud, SAP is providing a comprehensive set of SAP-managed data products across the Lines of Business (LoB). Customer only need to activate these predefined data products and then SAP takes care for extraction, loading and data curation. Here is an updated list and description of the currently available SAP Managed Data Products on our Business Accelerator Hub.Customer also can create their own customer-managed data products to enhance the set of data products with data from any SAP or non-SAP application. Customer-managed data products are curated in SAP Datasphere. The data and ORD file(s) are stored in the customer-managed object store within SAP Datasphere.Both type of data products are technically identical. They are stored in an object store, described via ORD file and published in the SAP BDC data catalogue. Picture 3: SAP Managed Data Products in Datasphere Catalog LEVERAGING DATA PRODUCTS IN PLANNINGData products are valuable for corporate planning because they turn raw data into reusable, discoverable, and trustworthy assets that planners can access directly instead of waiting for bespoke extracts or ad-hoc reports. Easier access removes bottlenecks: planners spend less time requesting data and more time analyzing scenarios, which speeds decision cycles and improves responsiveness. A well-designed data product provides a single source of truth and consistent definitions (e.g., standardized customer, product, or revenue metrics), reducing confusion and disagreement across teams. When data is exposed through APIs, semantic layers, or curated data marts, non-technical users and analytical tools can query it reliably without bespoke engineering work. Built-in metadata, lineage, and documentation increase trust and make it easier to validate results for budgeting, forecasting, and risk assessments.Data products also enable automation of recurring planning tasks—like rolling forecasts or variance analysis—so companies can run more frequent, higher-fidelity planning cycles with less manual effort. They support scenario modelling because productized datasets are versioned and auditable, letting planners compare alternative assumptions with clear provenance. By centralizing and governing access through SLAs, access controls, and data contracts, organizations reduce duplication of effort, lower data preparation costs, and decrease the chance of using stale or incorrect inputs in strategic decisions. Cross-functional collaboration improves because different stakeholders work from the same curated datasets, speeding alignment and reducing reconciliation work during planning meetings. Picture 4: Leveraging Data products enables easier data access for planningIn the context of SAP Data products for planning these can be:SAP managed: for most SAP based cloud applications (S/4HANA PCE and LoB solutions like SuccessFactors or Ariba) orCustom managed: customer defined for on-premise applications, partner based (i.e. PWC, McKinsey, etc.), 3rd party (i.e. weather, benchmark data, etc.) that could be coming from Data marketplace in Datasphere STEP-BY-STEP EXECUTION BASED ON EXAMPLENet Working Capital Planning (based on NWC core data products)We wish to perform Net Working Capital (NWC) planning based on actual and short term forecast data from S/4HANA. With the help of SAP delivered data products, we will significantly reduce the effort needed for data search, preparation and modelling. As a first step, we will search in BDC for the Cash Flow data product which is deployed and periodically loaded with fresh master and fact data from S/4HANA. Picture 5: Out-of-the-box Cash Flow Data Product contains the actuals needed for our planning use case The Cash Flow and several other data products like Journal Entry, Company code, etc. form the view and analytic model for New Working Capital in BDC. This large data set we will in the first step filter and limit the dataset to the dimensions that we need for planning. We keep it simple and decide to plan our NWC on Company Code and Profit Center so we use the Data Boulder in Datasphere to create a limited view. We also filter the facts to a specific year and currency. Picture 6: Data Builder in Datasphere to help us limit the Data Product content to what we need for planning Once our view is deployed, we can switch to SAP Analytics Cloud to build our planning data model. We navigate to Modeler and choose to create a new model, start with data and select we want to use a model from Datasphere where we just used several data products to design our NWC planning view. We select our view, and SAC will use its data structure to design the planning model for us.Picture 7: Instead of building the planning model from zero, we use the Data Product view to auto-create the model We get a model which includes the live version of NWC facts and the dimensions we defined in the Datasphere view. Picture 8: Auto-generated planning model based on a data product view We now must define semantics for our model columns and add master data wherever required. While the latter step currently requires some manual work, this will be dramatically reduced in 2026. SAP plans to deliver within the Seamless Planning Roadmap the possibility to re-use master data in SAC from Datasphere without the need of replication. In addition to master data creation, we also expand the time dimension to allow for the planning time horizon and configure the currency unit for the measure. Finally, we expose our planning model to Datasphere so we can apply the seamless planning approach and potentially make use of further data products if needed for our planning use case. Picture 9: Finished SAP Analytics Cloud model ready for planning Based on the created model, we can create a story and start planning our net working capital for 2026. Since our model includes a live version of actuals residing in my data product, we can start our planning based on actuals from 2025. Live Versions (stored externally to our planning model) can in Seamless planning be applied throughout the planning workflow—stories, data actions, predictions, calculations, and more. This feature introduced in Q4 2025 is in detail explained by @MaxGander in an excellent recent blog post. Picture 10: Planning layout for net working capital planning based on data products Extending NWC planning with HR data (based on SuccessFactors data products)We decide we wish to include other data to refine our NWC planning and make it more comprehensive. We can perform another search in the data catalog of BDC and add other relevant data products. While NWC is primarily driven by the operating cycle (receivables, inventory, payables) in some businesses employee headcount and related compensation costs can have a significant indirect impact. We perform a search in BDC data catalog and amongst multiple SAP-managed data products for SuccessFactors find the one relevant one for employee compensation data.Picture 11: SAP-delivered Compensation data product Similarly to the example with NWC data we would create a view on the Actual Compensation data product to focus and filter relevant data for planning. Picture 12: Use of Data Builder in BDC to limit the Data Product content to what we need for planning We can now add a new measure in our planning model on SAP Analytics Cloud to store compensation data and make use of External Live Versions to connect the Compensation View we created before. Picture 13: Connecting another External Data Source from BDC in our model We need to map the dimensions of our new compensation data source with the dimensions in our planning model.Picture 14: Mapping of dimensions for the Live Compensation data In final step we adjust our story that we already used before for NWC planning and copy the actual compensation data into a planning version. Now we can start flexibly using the new compensation driver to model a calculated measure or a data action that will allow us to simulate how plan compensation influences our NWC based on some logic that we define. Note that our case and data are simplified as we are focusing on BDC capabilities and not on business logic in this blog.Picture 15: Story in SAP Analytics Cloud combining data from both views built on BDC data products OTHER USE CASESIn addition to the use cases that we described in the above example, data products are also easily sharable due to the delta sharing capability. This allows for easy sharing of actual and plan data to third party tools that could be used to enrich our data. One such example is the potential to use SAP Databricks to apply ML on our plan data. We could use the ML results to automate parts of the planning process (i.e. apply an advanced time series forecasting algorithm in Jupiter Notebooks to actual data to generate a forecast). One of these examples will be described in detail in the blog Introducing AI/ML capabilities by SAP Databricks which is also part of this blog series to be published soon. SUMMARYIn summary, there is considerable value in data products for planning, and it is measurable:time-to-insight decreasesthe number of one-off data requests drops declined operational costs tied to manual data wrangling imporved forecast accuracy and plan-to-actual variance To capture these benefits, companies should identify high-impact planning needs, expose datasets via discoverable interfaces (data products), and monitor usage and quality over time. USEFUL LINKSIn case you are interested to implement the concept od data products and Data Fabric please refer to this article for implementation tips SAP What is article on Data Products.Our architecture center includes some interesting architectural information on this topic Data Products in BDC.In SAP Learning you can learn more on exploring and installing SAP Managed data products Learning.Finally also our Help content describing the Data Sharing Cockpit in Datasphere is a valuable resource explaining how to create and manage the your data product’s lifecycle. Read More Technology Blog Posts by SAP articles
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