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Databricks Commercial and Professional Services South Korea

SK Shieldus

IT Operations Automation · Risk Management

SK Shieldus needed a more scalable analytics foundation because its legacy environment could not support advanced machine learning for proactive security services, dispatch optimization, and customer retention.

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6 months
90%+
Accuracy in machine learning models, a 20% improvement vs. legacy models
Launching end-to-end security in 6 months to cut client churn

Reported impact

Business outcome

SK Shieldus reported Launching end-to-end security in 6 months to cut client churn, SK Shieldus predicted client threats faster to reduce dispatches with Databricks 90%+, and Accuracy in machine learning models, a 20% improvement vs. legacy models, improving predictive security operations and churn prevention with measurable gains in risk reduction, productivity improvement, and scalability.

  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

SK Shieldus uses Databricks Data + AI Platform, Delta Lake, Unity Catalog, and Lakeflow to support predictive security operations and churn prevention. SK Shieldus needed a more scalable analytics foundation because its legacy environment could not support advanced machine learning for proactive security services, dispatch optimization, and customer retention. In practice, the implementation stores or organizes core datasets in Delta Lake, governs access and lineage with Unity Catalog, coordinates repeatable workflows with Lakeflow, and runs transformation or AI logic on Databricks Data + AI Platform. Transaction, claims, risk, and event data are consolidated into Delta Lake; pipeline execution is coordinated with Lakeflow; Databricks Data + AI Platform applies analytics or model logic. The case study reports Launching end-to-end security in 6 months to cut client churn, SK Shieldus predicted client threats faster to reduce dispatches with Databricks 90%+, and Accuracy in machine...

Data flow

Transaction, claims, risk, and event data are consolidated into Delta Lake; pipeline execution is coordinated with Lakeflow; Databricks Data + AI Platform applies analytics or model logic.

Solution architecture

5 components · 4 layers
  1. Compute
    • Databricks Data + AI Platform Unifies engineering, analytics, and ai processing on one governed platform
    • MLflow Tracks experiments and model lifecycle management for the deployed ai workloads
  2. Storage
    • Delta Lake Stores curated lakehouse tables used for analytics and machine learning
  3. Orchestration
    • Lakeflow Orchestrates batch and streaming pipelines that keep the solution current
  4. Governance
    • Unity Catalog Enforces access controls, lineage, and governance across the data and models used in this workflow

Architecture clues

  • Centralized governance and lineage are enforced through Unity Catalog.
  • The platform is designed for near-real-time or streaming analysis rather than batch-only reporting.

Evidence from the source

6 months
Accuracy in machine learning models, a 20% improvement vs. legacy models
Launching end-to-end security in 6 months to cut client churn
SK Shieldus predicted client threats faster to reduce dispatches with Databricks 90%+
To build an integrated data analytics platform 2%
Transparency Evidence & methodology 95/100 reference value · 89% confidence · 1 source

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

01

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 value95/100
  • 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
    6 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
    5 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.
02

Traceability

Sources & provenance

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

  1. Databricks

    SK Shieldus customer story

    First-party
    https://www.databricks.com/customers/sk-shieldus
    Source type
    Vendor-published customer story
    Published
    Jul 29, 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 Jul 29, 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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