Databricks Manufacturing South Korea

Dongwon Group

Data Lake / Data Warehouse Modernization · Risk Management · Supply Chain Optimization

However, their legacy data systems were siloed by business units, limiting their ability to support company-wide AI-based operations.

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96% Raw material price forecasting accuracy

Although Dongwon possessed more than 50 years of accumulated data, their siloed data structure across business units complicated enterprise-wide utilization.

Starting with tuna deep-sea fishing in 1969, Dongwon has expanded into food, packaging, logistics and secondary batteries, generating approximately 10 trillion won in annual revenue.

To address these limitations, Dongwon adopted Databricks to establish a unified data management and AI-driven decision-making framework, which resulted in 96% accuracy in raw material price forecasting.

Reported impact

Business outcome

Dongwon Group achieved measurable business impact on Databricks, including 96% Raw material price forecasting accuracy; Starting with tuna deep-sea fishing in 1969, Dongwon has expanded into food, packaging, logistics and secondary batteries, generating approximately 10 trillion won in annual revenue.; To address these limitations, Dongwon adopted Databricks to establish a unified data management and AI-driven decision-making framework, which resulted in 96% accuracy in raw material price forecasting..

Solution

Additionally, fragmented data pipelines made it difficult to detect errors promptly. The absence of a stable platform for processing large-scale data also limited the use of real-time analytics and advanced predictive modeling. Ultimately, Dongwon’s tech stack shortcomings complicated the team’s need to apply AI at scale, making the need for consistent data, real-time processing and reliable model deployment increasingly more vital. Because data engineers, data analysts and data scientists required different tools, languages and access controls, collaboration was inefficient and even frustrating at times. As Jihyeon Lee, AX Team / DT Division Team Leader at Dongwon Group , explained, “These structural issues delayed data-driven decision-making.

Data flow

Operational, customer, and transactional data lands in Databricks and is transformed into curated datasets stored in Delta Lake under Unity Catalog governance

Solution architecture

2 components · 2 layers

Storage
- Delta Lake Stores Dongwon Group's curated datasets for analytics, machine learning, and operational use cases.
Governance
- Unity Catalog Provides access control, governance, and lineage across Dongwon Group's shared data products and AI workloads.

Architecture clues

Key product: Delta Lake
Key product: Unity Catalog
Real-time or streaming data processing

Evidence from the source

96% Raw material price forecasting accuracy

Although Dongwon possessed more than 50 years of accumulated data, their siloed data structure across business units complicated enterprise-wide utilization.

In today’s highly volatile food industry, the speed of data-driven decision-making directly determines productivity, operational efficiency and market responsiveness.

Starting with tuna deep-sea fishing in 1969, Dongwon has expanded into food, packaging, logistics and secondary batteries, generating approximately 10 trillion won in annual revenue.

To address these limitations, Dongwon adopted Databricks to establish a unified data management and AI-driven decision-making framework, which resulted in 96% accuracy in raw material price forecasting.

Transparency Evidence & methodology 90/100 reference value · 92% confidence · 2 sources

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 value90/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth20/25
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 scale5/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence92%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability23/25
11 of 12 key fields link to a named source.
Direct evidence20/20
5 direct source quotes retained.
Date provenance10/10
The publication date comes from the source page or publisher.
Source transparency9/10
2 source references 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
Dongwon Group customer story

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

Source type
Vendor-published customer story

Published
Jan 22, 2026
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
LinkedIn
Dongwon group | LinkedIn

Independent secondary
https://www.linkedin.com/company/dongwon/
The Organization JSON-LD lists addressLocality 'KR Dongwon F7, Mabangro 68, Seocho-gu, Seoul', and addressCountry 'KR', and numberOfEmployees value 757.

Source type
Structured company profile
Supports
- Company size

Date evidence

Published on source page Jan 22, 2026 · Databricks · First-party
Open date source

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

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