Delivering on Mead Johnson’s Promise of Science-based Pediatric Nutrition Through SAP Data Modernization & AI

Executive Summary

Mead Johnson Nutrition (MJN) is a leading global provider of pediatric nutrition products, with a legacy of over a century in developing science-based formulas for infants and children.

When MJN transitioned its ERP core from SAP ECC to SAP S/4HANA, it triggered an immediate need to rethink how critical SAP data fed into the company’s planning and reporting ecosystem. With over 140 business-critical tables at stake and a tight two-month deployment window, MJN turned to Tiger Analytics to architect a new data pipeline that would ensure business continuity while laying the groundwork for long-term modernization.

Tiger Analytics delivered a metadata-driven solution using SAP DataSphere, Azure Data Factory, and Databricks that ensured near real-time data replication, eliminated bottlenecks in existing SQL Server-based consumption, and introduced scalable data governance through Unity Catalog. This architecture not only stabilized MJN’s planning and analytics processes post-ERP upgrade but also introduced measurable performance gains and set the stage for strategic initiatives across data governance, automation, and platform efficiency.

By going beyond the brief and proactively building staging and history layers in Databricks, Tiger enabled teams to reduce reporting latency and simplify data access. The successful implementation has positioned MJN to scale its analytics landscape more effectively, backed by robust pipelines, automated delivery mechanisms, and a foundation built for direct, governed consumption from the cloud.

Client Overview

MJN is a leading global provider of pediatric nutrition products, with a legacy of over a century in developing science-based formulas for infants and children. MJN operates in highly regulated markets across North America, Asia, and Latin America, serving millions of families through trusted brands like Enfamil.

MJN’s operations rely on complex, data-driven processes for supply chain planning, R&D, compliance reporting, and financial forecasting. With a strong emphasis on innovation and operational efficiency, the company has made digital transformation a strategic priority, modernizing its data infrastructure to support faster, more accurate decision-making across the business.

The Ask: Solving a mission-critical modernization that disrupted a delicate balance

As part of a broader digital transformation, MJN initiated a major ERP modernization effort: migrating from SAP ECC to SAP S/4HANA. The goal was to enable real-time insights, streamline operations, and future-proof the business with an intelligent, modern ERP foundation. But modernization came with risk.

The switch to S/4HANA disrupted the existing data replication layer that fed analytics and reporting systems across the enterprise. To maintain business continuity, MJN needed to rapidly rebuild this layer and enhance it to support three strategic goals:

• Enable real-time replication from S/4HANA into the DnA Delta Lakehouse for advanced analytics, AI/ML use cases, and enterprise-wide reporting.
• Ensure clean and secure Nutrition data delivery from SAP S/4 to the Global EDAP team, which manages centralized analytics and data processing across business units.
• Meet a compressed two-month timeline, ensuring business teams could transition without losing access to the 140+ critical SAP tables they depended on.

That’s where Tiger Analytics came in. With deep expertise in enterprise data architecture, Tiger was brought in to design, build, and operationalize the new replication layer, enabling seamless data flow from the upgraded S/4HANA system to MJN’s analytics platform.

The Challenges: The ask was urgent. The room for error, zero.

MJN’s pivotal enterprise move to modernize their core ERP stack impacted a crucial piece of their analytics architecture: the data pipelines feeding the BI and planning systems.

Previously, MJN relied on SAP SLT to replicate data from SAP ECC to a SQL Server–based EDAP system. But this architecture posed growing challenges, including performance overhead on ECC, limited scalability, and an inability to support near real-time data needs. The setup was also rigid, required heavy maintenance, and lacked modern capabilities for data governance, lineage, and extensibility.

As MJN’s analytical and AI-driven needs evolved, it became clear that the existing architecture needed to be upgraded. To future-proof their data foundation, MJN committed to migrating to SAP S/4HANA and DataSphere, with Tiger Analytics providing architecture and technology support throughout the transition. Adding complexity, our team was brought into the engagement after key architectural decisions, like source/target systems, were already finalized. What was needed wasn’t just a connector, but a smart workaround built within tight guardrails.

Key Constraints:

• 8-week end-to-end timeline for development, UAT, and production deployment.
• Pre-fixed architecture: Source (SAP S/4HANA via DataSphere) and target (SQL Server on VM) were already decided.
• 140+ high-priority tables across business domains to be replicated, tested, and deployed.
• Need for near real-time replication with minimal downtime and low cost.
• Multiple downstream systems and teams, many of which had tight dependencies on legacy SQL Server pipelines.
• No room for delays with dependencies across various key functions, including R&D analytics, supply chain planning, and enterprise reporting, etc.
• The existing SLT pipelines were obsolete post-upgrade.

While none of this implied that MJN’s systems were failing, the stakes were high. Our team needed to build an entirely new pipeline, orchestrate processing logic, ensure high data fidelity, and create a foundation that wouldn’t buckle under scale or scrutiny.

The Solution Journey: Engineering a reliable and scalable backbone for critical SAP data

Even with tight timelines and a fixed go-live date, the Tiger team didn’t limit its role to executing predefined tasks. We quickly assessed the existing landscape and identified both short-term gaps and long-term improvement opportunities. Our recommendations were focused on performance, scalability, and governance.

Key strategic recommendations included:

• Proposing Databricks as the final layer of consumption to optimize performance and reduce cost.
• Building and maintaining staging and history layers in Databricks proactively, despite the layers not being part of the original scope.
• Advocating for Unity Catalog to modernize data governance, improve lineage, and eliminate redundant data copies across teams.
• Suggesting CI/CD pipelines to eliminate manual dependencies and enforce engineering rigor across deployments.

These interventions weren’t always immediately adopted due to legacy preferences and tight timelines, but they paved the way for a shift in mindset that is now visibly underway.

Tiger’s Consultative Approach: Thinking beyond the brief to architect for the long-term

Tiger Analytics set out to design a scalable, metadata-driven, and auditable data pipeline architecture built to meet MJN’s immediate needs today and its innovation roadmap tomorrow.

With years of experience working with MJN on data engineering initiatives, we were able to quickly understand the landscape, align with technical and business stakeholders, and move from planning to execution in record time.

Restoring critical data flows in just 8 weeks

• Ingestion via SAP DataSphere and ADLS Gen2
  • SAP DataSphere was configured to generate delta parquet files via CDS views, which were dropped into Azure Data Lake Storage (Gen2).
  • These delta files captured changes in near real-time and served as the foundation for the replication logic.
• Reliable Delivery to SQL Server
  • The transformed data was loaded into SQL Server, which was the consumption layer for downstream analytics and planning teams.
  • An auditing layer tracked the status of each job, file ingestion, and failure steps with built-in validation.
• End-to-End Orchestration in ADF
  • We built a metadata-driven ADF pipeline to orchestrate ingestion, transformation, and load processes across all 140+ tables.
  • A single parameterized pipeline replaced the need for 140 individual pipelines, pulling metadata from SQL Server to drive logic dynamically.
• Notifications and Failure Management
  • Azure Logic Apps were implemented for failure notification via email, ensuring proactive issue resolution.
• Pre-processing and Staging in Databricks
  • Databricks was used to process the delta files and apply minimal transformations (timestamp conversion, null handling), adhering to the “as-is” requirement.
  • Pre-processed data was staged and prepared for downstream loading.

A key addition was the development of Databricks history tables, a strategic enhancement introduced by our team to reduce reliance on SQL Server and pave the way for direct Databricks consumption. Several teams have already begun shifting their reporting logic accordingly, with measurable improvements in query performance.

Building the foundation for the future

Our work didn’t stop at “go-live.” After stabilizing the replication pipeline, the team made strategic enhancements to future-proof the architecture.

Databricks History Layer

• Proactively created a history table and staging table in Databricks, laying the foundation for long-term data availability and analytical reuse.
• This decision wasn’t part of the initial plan but was implemented immediately after go-live.

Business Impact of Databricks Consumption

• A planning team generating shipment reports switched from SQL Server to Databricks for data access and experienced significantly improved performance and report efficiency.
• This success story became a catalyst for broader Databricks adoption across MJN.

CI/CD Rollout for Scalability

• Our team began implementing CI/CD pipelines to reduce manual intervention and standardize deployment.
• This includes automation of ADF pipelines, notebooks, metadata tables, and SQL artifacts.

Unity Catalog for Governance

• Our team proposed and is currently implementing Unity Catalog, enabling cross-team data sharing with strong access controls.
• Once complete, business users will be able to query data directly from Databricks, eliminating redundant copies and reducing storage/maintenance costs.

Impact Delivered: Tangible results that changed how data serves the business

• 140+ tables deployed: Delivered development, UAT, and production deployment in 2 months.
• Near real-time replication: Framework designed to support real-time processing. Near real-time replication (30-minute interval) chosen to optimize cost and to align with the downstream team’s needs.
• Reduced SQL Server dependency: Databricks history layer enabled direct consumption by business users.
• Improved report performance: Shipment report consumers experienced higher speed and stability.
• Governance transformation: Unity Catalog in rollout to simplify access and reduce data silos.
• Automation in motion: CI/CD pipelines underway to bring repeatability, traceability, and speed.
• Cost optimization: Avoided unnecessary investment in real-time infrastructure like Delta Live Tables.

Voices from the Partnership Leaders

Rama Donepudi

SVP & Global CIO, MJN

“The real value of SunRise² lies in what it enables. With a modernized landscape, we’re better positioned to respond to business needs with speed and precision to serve our consumers better and faster.”

Ganesh Sivakumar

Global CDA&AI Officer, MJN

“With a harmonized SAP data foundation, we can now operationalize insights across functions, from supply chain and finance to marketing and product innovation. SunRise² is unlocking new levels of agility and intelligence in how we run the business.”

Asif Ghatala

VP & BU Head, Tiger Analytics

“At Tiger, we aim to deliver more than just successful implementations—we strive to drive business value. SunRise² enables MJN to take a strategic leap forward, setting a strong foundation for advanced analytics, AI-powered decisioning, and faster go-to-market.”

Tiger Solves: From service provider to transformation partner

Tiger’s role has grown from solution provider to integration partner – trusted to architect, operate, and extend MJN’s data infrastructure across business units.

As MJN explores new analytics use cases, Tiger has stepped into a pivotal role: proposing strategic extensions, shaping governance models, and simplifying access for decision-makers.

This is more than a data replication project. It’s a foundation for enterprise agility.