Why Databricks Matters for Impactful Analytics

Sameera
8 hours ago
5 min read

in SAP-Driven Enterprises

From Data Trust Deficit to AI-Ready Enterprises

Across industries, business leaders are facing the same uncomfortable reality: data exists everywhere, but trust exists nowhere. Despite heavy investments in ERP, analytics platforms, data lakes, and AI initiatives, decision-makers still struggle with delayed insights, inconsistent metrics, and AI models that fail to deliver business value.

SAP Business Data Cloud (BDC), powered by native Databricks capabilities, represents a structural shift in how enterprises connect applications, data, and AI, without recreating business logic, duplicating data, or compromising governance. For CIOs, CFOs, COOs, and CEOs, this is not another data platform. It is a business data foundation designed for outcomes.

This blog explains why SAP Business Data Cloud matters, what problems it solves, and how organisations can confidently make the shift, drawing directly from SAP’s architecture, Databricks’ lakehouse capabilities, and real enterprise use cases.

The Real Problem: The Enterprise Data Trust Gap

A 2025 survey of over 1,200 IT and business leaders highlights a consistent barrier to innovation: lack of trust in enterprise data. This trust gap is not theoretical, it shows up daily in boardroom discussions and operational delays.

What causes this trust gap?

Business logic is lost during data extraction and must be rebuilt repeatedly
Complex ETL pipelines increase fragility and cost
Data duplication across platforms creates inconsistent KPIs
Scaling analytics and AI becomes difficult due to fragmented architectures
Time-to-insight increases, reducing business agility

The outcome is predictable: leaders hesitate to act decisively, AI initiatives stall, and data teams spend more time maintaining pipelines than generating value.

Why Traditional Data Architectures Fail Business Leaders

Most enterprises today operate with a combination of:

SAP systems of record (S/4HANA, BW, SuccessFactors, Ariba, etc.)
Cloud data lakes or warehouses
Third-party data sources (market data, ESG, credit scores, logistics)
AI and analytics tools layered on top

While each component may be best-in-class individually, the architecture as a whole is broken.

Key limitations of traditional approaches:

Data must be extracted from SAP, stripping it of semantic meaning
Security and governance weaken as data spreads across platforms
AI models lack business context, reducing accuracy
Costs rise due to duplication, storage, and data movement
SAP analytics tools cannot safely consume non-SAP data without heavy engineering

This is where SAP Business Data Cloud introduces a fundamentally different model.

SAP Business Data Cloud: A Business Data Fabric, Not Just a Platform

SAP Business Data Cloud is built around a simple but powerful idea:

Bring data, business context, and AI together, without breaking trust.

Instead of treating data as raw material to be extracted and reshaped elsewhere, SAP BDC establishes a business data fabric that preserves meaning, governance, and security end to end.

Core principles of SAP BDC:

Semantically rich SAP data products, ready for analytics and AI
Zero-copy data sharing, eliminating duplication
Unified governance and identity management
Open integration with non-SAP data sources
Native support for advanced analytics and AI

This foundation allows organisations to analyse, plan, predict, and innovate using the same trusted data across finance, supply chain, sales, HR, and operations.

Why Databricks Inside SAP Business Data Cloud Changes Everything

SAP’s integration of Databricks directly into Business Data Cloud is a strategic move that addresses one of the biggest CIO challenges: enabling advanced AI and data science without fragmenting the data landscape.

What Databricks brings to SAP BDC:

World-class data engineering, AI/ML, and data science capabilities
Lakehouse architecture for structured and unstructured data
Pro-code environments (SQL, Python, notebooks)
Enterprise-grade governance via Unity Catalog
End-to-end AI lifecycle support—from experimentation to production

What makes this integration unique:

Databricks runs where the SAP data already lives
SAP data products are consumed without copying or remodelling
Non-SAP data can be securely blended with SAP data
Analytics and AI workloads run on trusted, governed data

For business leaders, this means faster innovation with significantly lower risk.

Zero-Copy Data Sharing: Eliminating the Cost and Risk of Duplication

At the heart of SAP BDC and Databricks integration is Delta Sharing—a zero-copy, open data sharing protocol.

Why zero-copy matters to executives:

Eliminates data duplication costs
Prevents KPI inconsistencies across teams
Reduces security exposure
Accelerates time-to-insight
Simplifies compliance and auditing

SAP-managed, partner-built, and custom data products can be shared bi-directionally between SAP BDC and Databricks—securely and instantly.

This enables organisations to move from pipeline-heavy architectures to product-driven data consumption.

Turning SAP BW Investments into Future-Ready Data Products

Many enterprises view SAP BW as a legacy system. SAP Business Data Cloud reframes it as a strategic asset.

How SAP BDC modernises BW:

BW data is exposed as governed data products
No need for full system replacement
Zero-copy sharing to Databricks for AI and ML
Enables modern analytics without re-platforming

This approach protects prior investments while enabling entirely new use cases such as predictive analytics, generative AI, and real-time simulations.

Enabling Business AI: Why Data Quality and Context Are Non-Negotiable

AI success depends less on algorithms and more on data quality and business context.

SAP Business Data Cloud ensures:

SAP’s knowledge graph and semantic models remain intact
Data is AI-ready by design
Governance spans structured and unstructured data
Models are trained on trusted enterprise data

With Databricks Mosaic AI capabilities, organisations can:

Build custom AI/ML models
Fine-tune foundation models
Deploy models as real-time services
Monitor and govern AI assets centrally

This creates an enterprise-ready AI foundation, not isolated experiments.

Real Business Use Cases Across the Enterprise

SAP Business Data Cloud with Databricks enables tangible outcomes across functions:

Finance & Risk

Credit risk assessment of customers and suppliers
Working capital optimisation (5–15% reduction)
Cash flow forecasting with >90% accuracy

Supply Chain

Disruption prediction and scenario modelling
Tariff and trade impact analysis
Inventory optimisation and predictive replenishment

Sales & Marketing

Customer 360 analytics
Campaign optimisation
Dynamic pricing and demand forecasting

HR & People Intelligence

Attrition prediction
Workforce demand forecasting
Scenario-based workforce planning

Each use case combines SAP operational data with third-party signals, securely and without duplication.

How Quantum Digital Helps Organisations Make the Shift with Confidence

We help organisation navigate SAP Business Data Cloud adoption as an operating model change. It is not just a technical upgrade for us.

Our approach focuses on:

Business-first assessment of data trust gaps
Use-case-led roadmap aligned to executive priorities
Modernisation without disruption to existing SAP landscapes
Data product strategy, not pipeline proliferation
AI enablement with governance from day one

Quantum Digital helps organisations move from fragmented data ecosystems to a unified, AI-ready business data foundation, with measurable outcomes.