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Highlights

A leading manufacturer of flavors, antioxidants, nutraceuticals, animal nutrition products, natural colors, and fragrances—based on plant-derived ingredients—sought to advance its digital transformation initiatives.

The organization aimed to implement SAP Datasphere to modernize its reporting and analytics landscape and additionally required SAP data to be integrated with the Anaplan system to support demand and forecasting planning.

Problem Statement: Current state and pain areas

• Data Quality Risks: Processed data was fed manually into Anaplan planning system during the month and thus increased the likelihood of errors, inconsistencies, and incomplete data, impacting planning accuracy in Anaplan.

• Inefficiencies and Dependencies: The same data were housed in complicated layers of models that were constructed. This hampered data accuracy and introduced needless complexity.

• Limited Scalability: As the business grows, the manual process becomes increasingly difficult to manage, creating bottlenecks and operational strain.

• Audit & Compliance Challenges: Manual interventions leading to reduced traceability and making it difficult to maintain audit trails, impacting compliance and governance requirements.

Areas of Value Add: End-to-end design and implementation of the SAP Datasphere solution

indigoChart’s services delivered significant value by designing and implementing SAP Datasphere to effectively address the organization’s data movement challenges.

SAP CDS views enabled with Change Data Capture (CDC) were used to track real-time incremental data changes in SAP S/4HANA. Both SAP-delivered standard CDS views and custom CDS views created for domain-specific enhancements were leveraged to replicate data into SAP Datasphere through replication flows.

Once the data was ingested into SAP Datasphere, it was stored in local tables at the Ingestion Layer, which also maintained the respective delta/change-log tables. This ensured that delete operations and incremental changes were automatically captured without the need for separate handling.

In the Quality Layer, necessary data modelling techniques were applied to build Fact, Dimension, Hierarchies, and Relational datasets. The associations between these models enabled the creation of analytical models for SAP SAC in the Reporting Layer.

Furthermore, a dedicated Propagation Layer was established for outward data movement and staging. This enabled seamless data export to Azure Blob Storage, supporting downstream processing and integration requirements for Anaplan.

Systems and Technologies used in the Integration Architecture:

• SAP S/4HANA (on-premise): SAP S/4HANA (on-premise) provides a robust, highly configurable ERP platform hosted within the organization’s own infrastructure, enabling full control over system customization, security, and integration for core business processes.

• SAP Cloud Connector: SAP Cloud Connector establishes a secure, real-time link between on-premise SAP systems and cloud applications, enabling controlled data access and seamless integration without exposing internal systems directly to the internet.

• SAP Datasphere: SAP Datasphere provides a unified, scalable data foundation that integrates, models, and governs enterprise data in real time, enabling advanced analytics, AI use cases, and seamless consumption across SAP and non-SAP systems.

• Anaplan: Anaplan is a cloud-based planning and forecasting platform that enables connected business planning across finance, supply chain, and operations, allowing organizations to model scenarios, align decisions, and drive data-driven performance.

• Azure Blob Storage: Azure Blob Storage is a scalable, secure cloud storage service designed for storing large volumes of unstructured data, enabling cost-efficient data archiving, analytics, and integration with enterprise applications.

Impact:

Partnering with indigoChart helped bring about several positive impacts for our client’s analytics and reporting practices via implementation of the SAP Datasphere solution.

• System Integration: By integrating the systems through SAP Datasphere, the entire data movement process was fully automated, eliminating the need for any manual data loads. This significantly reduced operational efforts, minimized errors, and ensured timely and consistent availability of data for Anaplan.

• Efficiency & Accuracy: Automation through SAP Datasphere completely removed manual interventions, resulting in faster, more accurate, and reliable data transfers.

• Improved Timeliness: With automated data integration, Anaplan receives updated SAP data without delays, enabling more timely planning, forecasting, and decision-making.

• Reduced Dependency: Eliminating manual loads reduces dependency on specific individuals and mitigates the risk of delays during month-end processes.

• Better Scalability: The automated pipeline supports future growth and higher data volumes without additional operational effort.

• Enhanced Data Quality: Automated integration ensures clean, consistent, and validated data reaches Anaplan, improving planning accuracy.