Databricks Consumer Goods Global

Mondelēz International

Marketing Personalization · Sales Automation · Supply Chain Optimization

Mondelēz International needed a better way to support enterprise-scale model experimentation, model serving, and AI-driven decision support because siloed data and disconnected model development workflows kept experimentation, supply chain optimization, and revenue growth management from scaling globally.

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20K scaling ai innovation to drive revenue, efficiency and growth

3K models managed in batch and real time using mlflow

Reduced technical debt and improved experimentation on 20K models

Reported impact

Business outcome

Mondelēz International improved enterprise-scale model experimentation, model serving, and AI-driven decision support with measurable results including 20K scaling ai innovation to drive revenue, efficiency and growth, 3K models managed in batch and real time using mlflow, and Reduced technical debt and improved experimentation on 20K models, while giving teams a faster path from governed data to production decisions.

Solution

Mondelēz International uses Databricks Data + AI Platform to support enterprise-scale model experimentation, model serving, and AI-driven decision support. The implementation replaced a legacy state where siloed data and disconnected model development workflows kept experimentation, supply chain optimization, and revenue growth management from scaling globally. Sales execution data and marketing data move through Lakeflow Jobs and land in shared lakehouse datasets, giving teams one trusted data foundation. Unity Catalog and Delta Sharing governs access, lineage, and compliance across sensitive datasets. MLflow, Model Serving, and Lakehouse Federation operationalize models, agents, and analytics that deliver managed experiments and served production models.

Data flow

Sales execution data and marketing data land on a shared platform, pipeline services clean and standardize them, governance controls enforce trusted access, and AI, model-serving, and analytics services deliver managed experiments and served production models.

Solution architecture

8 components · 4 layers

Compute
- Databricks Data + AI Platform Databricks Data + AI Platform unifies engineering, analytics, governance, and AI workloads for enterprise-scale model experimentation, model serving, and AI-driven decision support.
- MLflow MLflow tracks experiments, versions, and release workflows for models that support enterprise-scale model experimentation, model serving, and AI-driven decision support.
Serving
- Model Serving Model Serving exposes real-time predictions and AI outputs to managed experiments and served production models.
- Lakehouse Federation Lakehouse Federation lets teams query external systems tied to enterprise-scale model experimentation, model serving, and AI-driven decision support without copying data into new silos.
Orchestration
- Lakeflow Jobs Lakeflow Jobs schedules ingestion and transformation of sales execution data and marketing data into reusable datasets.
Governance
- Unity Catalog Unity Catalog applies centralized access controls, lineage, and auditability across sensitive datasets.
- Delta Sharing Delta Sharing securely distributes governed data to managed experiments and served production models and partner workflows.
- Lakehouse Monitoring Lakehouse Monitoring tracks data and model quality for enterprise-scale model experimentation, model serving, and AI-driven decision support so regressions surface early.

Architecture clues

20K scaling ai innovation to drive revenue, efficiency and growth
3K models managed in batch and real time using mlflow
Automated ingestion and transformation pipelines keep source data synchronized for downstream analytics and AI.
Centralized governance, lineage, and access controls are enforced through Unity Catalog.
Operational AI workflows are deployed directly beside governed data and analytics assets.

Evidence from the source

The idea is, when the sales rep goes into the market, what is the route they should choose to maximize coverage? That’s where machine learning gives us an advantage,

We want to leverage AI for product improvements, process efficiencies, and to drive responsible AI practices across the organization,

When we wanted to scale an application, that required several worker nodes. We found it challenging to run the entire end-to-end pipeline in one go,

You don’t want to produce more than you can’t sell. Waste is a revenue loss, both in terms of missed sales and the cost to discarded unsold product.

Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth25/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact25/25
3 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence89%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
4 direct source quotes retained.
Date provenance10/10
The publication date comes from the source page or publisher.
Source transparency8/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity10/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

Databricks
Mondelēz International customer story

First-party
https://www.databricks.com/customers/mondelez

Source type
Vendor-published customer story

Published
Apr 22, 2025
Supports
- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date

Date evidence

Published on source page Apr 22, 2025 · Databricks · First-party
Open date source

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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