That was the starting point for Inetum’s webinar SAP Business Data Cloud & Databricks: Unlock AI & Data potential, which brought together practitioners from Inetum and special guests from SAP and Rolls-Royce. The session featured Jan Tretina (Ecosystem Development Manager, SAP), Sebastian Stefanowski (Databricks Practice Leader, Inetum), Raul Muñoz-Gutierrez (SAP Analytics Business Director, Inetum), and Andrew Lager (Program Manager and Digital Delivery Manager, Civil Digital and IT, Rolls-Royce), hosted by Oleh Hudym (SAP Growth Manager Manager, Inetum).

Less than 40% of leaders trust their own data – and that gap stalls AI

“Less than 40% of leaders report high confidence in their data,” Jan Tretina said at the opening of the session. “That gap directly impacts both decision speed and innovation.”

Three issues sit behind this figure. First, data quality: over half of organizations struggle to keep data accurate and consistent across systems. Second, misalignment between IT and business – finance teams need insights quickly, but IT landscapes can’t always deliver at that pace. Third, fragmentation: data spread across multiple systems, bringing it together in real time remains a major pain point.

The business consequence is concrete. Data-oriented organizations – those that have solved the trust problem – are, according to SAP’s analysis, four times more likely to succeed.

What SAP Business Data Cloud is – and how it closes the trust gap

Jan Tretina described SAP Business Data Cloud through a flywheel: AI is only as strong as the data behind it, data is only valuable when it is trusted and accessible, and both require a resilient platform underneath.

“SAP Business Data Cloud is our unified data foundation,” he said. “It brings clean, connected, and trusted business data together – unifying data across SAP and non-SAP systems and making that data immediately usable for AI, analytics, and planning.”

BDC consolidates SAP data services – SAP BW, SAP Datasphere, SAP Analytics Cloud, and extension partners including Databricks and Snowflake – under a single platform. The operational impact Tretina highlighted: instead of maintaining thousands of pipelines, custom integrations, and shadow data, BDC provides one consistent data foundation across all use cases. It embraces non-SAP data, open standards, and a broad partner ecosystem, letting organizations build multi-vendor landscapes without losing control of their data. This architecture makes BDC relevant across sectors – from retailers to manufacturing to financial services and banking.

Migration: BDC works with what organizations already have

A recurring concern when evaluating any new platform is the cost of transition – years of investment in SAP BW, Datasphere, or Analytics Cloud environments that can’t simply be discarded.

Raul Muñoz-Gutierrez addressed this directly. BDC includes all SAP data services within a single platform and is designed to absorb existing environments, not replace them. For SAP BW, BDC offers a “lift and shift” migration path – the existing environment moves in with data and connections preserved.

For SAP Datasphere or SAP Analytics Cloud, a “rewiring” process brings those solutions under the BDC umbrella without rebuilding. “All these tasks don’t require any work from the customers,” Muñoz-Gutierrez confirmed. “They can be carried out directly by SAP.”

Data products eliminate the 80% of a project that adds no value

“Do you know the effort and time required in a data project to perform data extraction, loading and transformation, as well as reconciling all the information?” Raul Muñoz-Gutierrez asked during the session. “All that work, that normally is the 80% of the project, is partially eliminated through a SAP data product.”

SAP data products contain the main data from key functional areas – financial, HR, supply chain – and they inherit all the data semantics from SAP. This makes them usable not just for reporting, but for AI and machine learning development from day one.

For CFOs specifically, the Financial Intelligence Package activates intelligent applications on SAP Analytics Cloud while simultaneously triggering data loading from SAP S/4HANA into BDC data products. “Regularly every 30 minutes, the data is going to travel from our SAP S/4 system to our BDC,” Muñoz-Gutierrez explained. Treasury management, financial planning, and forecasting become available and AI-ready from the moment of activation – with the option to build custom AI and machine learning on top.

SAP Databricks and standalone Databricks: the same engine, different purpose

If Databricks is already in the organization’s stack – or under evaluation – a natural question arises: how does SAP Databricks within BDC relate to the standard Databricks platform?

Sebastian Stefanowski, whose team works with both, explained the distinction. SAP Databricks is a modified release of the Databricks platform, tightly integrated with BDC – authentication, authorization, and billing are all managed through SAP, using SAP compute units. The most important aspect of the integration, in Stefanowski’s words, is a dedicated connector enabling zero-copy data sharing between SAP data products and the Databricks catalog: SAP financial data becomes directly queryable in Databricks without replication or transformation overhead.

“If SAP data is at the heart of your business, and financial data is really central to what you do, then SAP Databricks is definitely worth serious consideration,” Stefanowski said. “It would enable most of the useful features of Databricks with, generally speaking, no technological barrier.”

The trade-off is feature breadth. Organizations with IoT streaming requirements, multi-stage declarative pipeline orchestration (DLT), or a need to manage their own compute clusters will find standalone Databricks richer – with dedicated connectors for external cloud services and platforms like Salesforce. In SAP Databricks, those broader integrations run through SAP. Stefanowski’s summary was clear: for general data integration scenarios, standalone Databricks; for organizations where SAP financial data is the core, SAP Databricks removes the barriers.

“What seemed to be impossible a few years back in terms of AI capabilities is now possible with this amazing partnership between SAP and Databricks,” Oleh Hudym noted during the session.

Six years with Databricks at Rolls-Royce: 50% cost reduction and AI for every analyst

Andrew Lager has been on the Databricks journey at Rolls-Royce for over six years, including as an early adopter of many of its latest technologies – and he came to the webinar with numbers.

“We’ve reduced costs upwards of 50% compared to our previous solution,” he said, referring to the migration from a legacy warehouse to Databricks Lakehouse for engine health monitoring data. Three factors drove that reduction: cheaper compute via Spark, schema evolution that prevents jobs from breaking when table structures change during migrations, and Unity Catalog – which centralizes data access across sources so Lager can produce management information for internal stakeholders without involving additional teams.

On the AI side, his example was equally direct. “Those activities that would take me days can take me five minutes now,” he said, describing Databricks AI Genie – a feature that lets non-technical users query datasets in natural language instead of SQL.

“The biggest benefit, not just for me but for Rolls-Royce in general, has been opening up technical solutions to less technical people.”

What AI on SAP data actually looks like

Sebastian Stefanowski described Databricks as a complete environment for AI and machine learning, where SAP BDC data products are exposed as tables that plug directly into data pipelines. He grouped the platform’s AI and ML capabilities into several practical areas:

• AI Playground – run any off-the-shelf or externally sourced model on a serverless runtime; create custom prompts and test hypotheses before committing to a build
• Model serving endpoints – host and serve custom models at scale
• AI agent framework – build agentic solutions quickly, with a built-in vector search database for RAG implementations
• MLflow – manage and monitor the full training lifecycle; compare experiments and select the best-performing model
• AutoML – Databricks runs parallel experiments across multiple model architectures on your data automatically, then selects the model that best fits your test results
• AI functions in SQL – call AI models directly from a SQL SELECT statement and store predictions as part of the query output
• AI Gateway – govern model usage centrally: block unauthorized access, filter sensitive queries, and maintain full oversight of what models run and on what data

He then connected those capabilities to the most common use cases for SAP data: cash flow forecasting, prices forecasting, and stock level optimization – blending SAP financial and operational data with seasonality data, interest rate history, and interest rate predictions to anticipate future costs, prices, and inventory needs. Databricks AutoML fits naturally here, running statistical algorithms in parallel and surfacing the model that best fits historical test data.

Beyond forecasting, LLM integration opens a second category: automatically generating product descriptions from structured SAP product data; running sentiment analysis on customer feedback to understand the reasoning behind customer behavior; and identifying the most promising customers to approach based on that analysis.

“All these features can be really, really nicely integrated with SAP and blended with SAP data,” Stefanowski said. “I think that’s something which will bring SAP customers to another level.”

Flexible consumption: licensing that moves with the organization

Raul Muñoz-Gutierrez described the BDC commercial model as one of its differentiators. “SAP Business Data Cloud offers a unit subscription model that provides customers with flexible subscription pricing,” he said. “This allows them to subscribe to the services they currently need and easily modify them in the future.”

The example he gave: an organization starts with SAP BW integrated into BDC. Once that environment has been migrated to SAP Datasphere, the BW capacity is removed and reallocated – to SAP Databricks or SAP Snowflake, for instance. A separate data services licence layer complements the platform subscriptions, enabling activation of analytical applications with near real-time data, development of custom data products, and external data sharing via BDC Connect – the service that exposes SAP data to Databricks, Snowflake, and Microsoft Fabric through Delta Sharing.

What is coming in SAP Business Data Cloud in 2026

Jan Tretina outlined SAP’s roadmap across three areas.

First, deeper openness and connectivity: BDC Connect is expanding to Databricks, Snowflake, Google Cloud, Microsoft Fabric, and AWS. Bi-directional data sharing with SAP HANA Cloud is also in development – data flowing both ways, removing silos at the source rather than managing them downstream.

Second, data products: SAP is expanding coverage across more lines of business and releasing Data Product Studio, which will make modeling SAP and non-SAP data into governed, shareable data products “much easier than ever before” – turning data into “a true asset others can consume and trust,” in Tretina’s words.

Third, AI-native capabilities built directly into the data layer: a new AI Hub with new models, enhancements to the SAP HANA Cloud Knowledge Graph, and a fully agentic multi-modal database – including memory for agents. “This is the database AI was really looking for,” Tretina said.

“We are building the data foundation every enterprise will need to power next-generation AI. 2026 will be a breakthrough year for BDC.”

How CFOs should measure ROI on SAP Business Data Cloud

In the Q&A, Jan Tretina identified four areas where BDC generates measurable returns for finance leaders.

Efficiency and proactivity. Fewer manual reconciliations, reduced data preparation effort, faster close-to-forecast cycles.

Data trust. Higher data quality and fewer errors – directly addressing the confidence gap he described at the start of the session.

Agility. Faster time to insight and quicker scenario iterations.

Business impact. Improved forecast accuracy and tangible cost savings from AI-driven recommendations.

“CFOs should look at gains in proactivity, trust, and measurable financial outcomes,” Tretina said. “With SAP BDC, the ROI is visible both in operational efficiency and in the quality of decisions which can power the business.”

How to start – without committing to a licence first

For organizations that want to evaluate BDC before committing to a full rollout, Raul Muñoz-Gutierrez described a concrete entry point. Inetum has three years of experience with SAP Datasphere and related environments, and operates its own SAP Business Data Cloud environment – available to run proof-of-concept scenarios with customers in a live setting, testing whether a target architecture fits their real needs before any licence investment.

“You don’t have to invest in a licence right now,” Muñoz-Gutierrez said. “You have to know what you want, and we are the best company to accompany you to this final scenario – because we have SAP Data Specialists and also global data expertise from non-SAP solutions.”

On the Databricks side, Sebastian Stefanowski was unambiguous: “Databricks is for everyone.” The platform runs on a pay-per-use model – if workloads run once a day for an hour, the cost reflects that hour. If the platform is idle, the cost goes to zero. “The openness and scalability of this platform – which actually scales down to zero – that is what should encourage any company who wants to start cloud data processing,” he said.

The message across the webinar was consistent: before AI delivers value at scale, organizations need a data foundation that is clean, connected, and trusted. SAP Business Data Cloud is built to be that foundation. Databricks extends what can be done on top of it. And when the two are combined with the right approach, the gap between fragmented enterprise data and working AI closes faster than most organizations expect.

The full session is available on demand.

SAP, Databricks, Rolls-Royce, and Inetum practitioners covered migration strategy, data product architecture, and AI implementation – including the unedited live Q&A. Watch the webinar on demand →

To run a proof of concept in Inetum’s own SAP Business Data Cloud environment – without upfront licence commitment – talk to our team →

Artificial Intelligence in a Magic Quadrant

Microsoft, Qlik and Tableu have been listed among the leading providers of Business Intelligence systems by Gartner for years now. Solutions based on Artificial Intelligence are already offered by each of these leading companies.

• The Power BI tool, for example, uses the potential of Microsoft AI, enabling business users to prepare data easily, create machine learning models and draw conclusions.
• Tableau, which occupied second place in Gartner’s Magic Quadrant in 2020, uses Ask Data and Explain Data functionalities. They use natural language processing, Artificial Intelligence mechanisms and statistics to ensure better data analysis – both for business users and those with knowledge of the complexities of widely understood Data Science.
• Qlik rounds out the list in turn, introducing a functionality called Cognitive Engine in its April 2018 version, which uses Machine Learning mechanisms to suggest the most optimal visualizations, thus making it easier for users to analyze data.

SAP – Business Intelligence visionary

Gartner’s Magic Quadrant is a guide for business representatives when it comes to choosing a tool. It is worth observing the contest between so-called visionaries. Here Gartner indicates providers who know the market and have the potential to change it. SAP is one such visionary – and today we will take a closer look at what this well-known player has to offer. Since it is widely known that the cloud is a development direction for Business Intelligence tools, SAP has also decided to create something that will be a cloud alternative to the on-premises SAP BusinessObjects. The SAP Analytics Cloud tool (SAC), which has been available on the market since 2018, is currently the main direction of development for SAP in the field of data analysis and reporting. Let’s look at how SAP redefines work with analytical tools.

SAC functionalities

Data democracy has recently been a hot topic, promoting widespread access of users without specialized IT knowledge to unassisted data analysis, among others. Until now, such users were dependent on Business Intelligence specialists. Introducing the benefits of Artificial Intelligence to its analytical tool, SAP tries to meet the needs of users who do not have high-level analytical or IT qualifications. Let’s look at some interesting functionalities supporting data analysis that we can find in the SAP Analytics Cloud.

Search to Insight

Let’s imagine for a moment that we are analyzing sales data for beverages and we want to start with some basic information on sales results in recent years. According to the traditional approach, we would try to search for the report on our platform, hoping that it was titled in a way that is intuitive enough for us… But what about the possibility of using a search box in a different way, and – instead of looking for a specific report title – trying to ask a question which we want to find out the answer to?

Here are some examples of how Search to Insight uses Natural Language Processing to build reports based on search engine queries:

Query: “Gross margin time chart by location”

Answer:

Query: “Show me the top 5 products in Location Reno in 2016 Q1”

Answer:

The text query processing function works quite well, provided that we use the names of the dimensions available in the analytical model prepared beforehand by someone else in the queries. Fortunately, the user can display an available list of objects which they can use in their queries. Search to Insight works well for basic queries related to the aggregation of available metrics. However, if our queries exceed the capabilities of this tool, we can still count on AI support in the form of another module called Smart Discovery.  

Machine Learning at SAC

The tool for report building in SAC, based on the existing data model, gives us the opportunity to use Machine Learning mechanisms. Machine Learning facilitates the discovery of patterns and statistically important relationships for our data source. When running such an analysis for a previously used sales model, the user only needs to indicate what element of the model will be the subject of the study (whether it will be a dimension or a measure), and then indicate which other elements are to be assessed in terms of the impact on the analyzed variable as part of the model.

For example, by indicating the gross margin measure as the main object of our analysis, we can get the following result:

Summary overview:

• summary presenting the trend of values in past periods together with forecast values for future months, calculated based on historical data
• distribution of values of a given measure presenting the most “typical” values for our measure
• analysis of the aggregates with the use of the dimensions available in the model

Key influencers:

• Indication of model objects that statistically have the greatest impact on the examined variable. In the analyzed model, the “Product” dimension was indicated as the factor with the biggest impact on the analyzed variable. In other words, the margin on selected products is very diverse.

Unexpected values:

• Identification of transactions in which the value of the examined variable differs significantly from the value forecasted by using the model. These types of values may be subject to further analysis in order to clarify or verify the correctness of transactions carried out.

Simple mechanism for “what-if” analysis. It allows you to assess the impact of changing particular model parameters on the variable you are studying, such as changing the discount value for selected products. It is a highly desirable functionality for business users, although in my view it still requires some refinement so that the presented results are more understandable to users.

The presented Smart Discovery tool is not the last functionality that SAC has to offer in the field of Augmented Analytics. After verifying the correctness of historical data, SAC can help us look to the future thanks to its Smart Predict functionality.

SAC predictive models

As part of predictive services, SAC offers three types of predictive models:

• Classification – used when the examined variable can have binary values. For example, we can try to predict if a customer will make a purchase (true / false) based on the available customer features.
• Regression – this model may apply a numerical value based on the diagnosed correlation between descriptive variables and the examined variable. This type of model can help us identify the descriptive variables that have the biggest impact on the result variable we are examining, and thus assess the potential numerical value of the examined variable for a hypothetical combination of descriptive variables.
• Time series – simply a time series forecast based on historical data.

The operating principle is similar for each type of model.

User:

• Preparation of historical data (file or database connection).
• Data import to the platform.

User:

• Indication of the column with the examined variable.
• Defining a filter for explanatory variables.

SAC:

• Division of data provided into a training set and verification set.
• Generating several predictive models and choosing the best by comparing the results with the verification set.

User:

• Division of data provided into a training set and verification set.
• Generating several predictive models and choosing the best by comparing the results with the verification set.

When assessing SAC functionalities in the field of data prediction, we can claim that their main advantage is simplicity of use, which will allow you to undertake complex analysis with no need for advanced statistical knowledge. This is why this tool should not be compared with other Machine Learning tools available on the market, which have much more developed capabilities, but therefore require a more in-depth knowledge of statistics.

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Do I still need Business Intelligence specialists?

After finding out more about the capabilities of Business Intelligence tools which use Machine Learning and the possibilities they open up for users, the following question may arise: “Do we still need Business Intelligence specialists?” My answer is: definitely! The competences of Business Intelligence specialists will still be necessary to create analytical models in such a way that will allow for their proper use in the above-mentioned Self-Service Reporting tools in modern SAP Analytics Cloud systems. Specialist BI knowledge will also be priceless in terms of integrating data from many different sources and building more complex analytical applications.

Summary

Self-Service Reporting tools equipped with the power of Artificial Intelligence uncover new possibilities for users who are looking for Business Intelligence tools. And while we’re still not using the full potential of Machine Learning, one thing is certain: AI is the future of BI! Business Intelligence specialists equipped with new tools will be able to provide consistently better business solutions. If your organization is facing new analytical challenges, take advantage of the extensive experience of Business Intelligence specialists to help make full use of the potential of your tools and achieve your business goals.