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Databricks Telecommunications Japan

NTT DOCOMO

Customer Service Automation · Data Lake / Data Warehouse Modernization · Risk Management

However, the degree of utilization within the company was uneven, which impacted their ability to visualize the status of utilization and extract insights to enhance functionality and performance. As a result, NTT DOCOMO was able to support decision-making for functional and performance improvements and reduce the time spent manually processing and analyzing data by 90%.

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90% More efficient analysis of LLM usage
As a result, NTT DOCOMO was able to support decision-making for functional and performance improvements and reduce the time spent manually processing and analyzing data by 90%.
As of February 2025, the LLM value-added platform has approximately 10,000 MAUs and 3 million monthly calls, and it is being used in a variety of use cases.
For example, the network quality improvement team used the internal LLM tool to analyze customers’ social media data, processing 20,000 entries daily to detect issues faster.

Reported impact

Business outcome

NTT DOCOMO achieved measurable business impact on Databricks, including 90% More efficient analysis of LLM usage; NTT DOCOMO is a Japanese mobile operator serving approximately 90 million subscribers, connecting individuals through 5G, LTE, broadband and international services.; As a result, NTT DOCOMO was able to support decision-making for functional and performance improvements and reduce the time spent manually processing and analyzing data by 90%..

  • Customer Experience
  • Productivity Improvement
  • Risk Reduction

Solution

NTT DOCOMO is a Japanese mobile operator serving approximately 90 million subscribers, connecting individuals through 5G, LTE, broadband and international services. For businesses, NTT DOCOMO provides communication infrastructure and handsets that drive efficiency and growth. To support this effort, they have developed a large language model (LLM) value-added platform to further improve customer satisfaction through the use of GenAI within the business. GenAI was a key technology enabler for NTT DOCOMO, where its rapid adoption would allow their internal users to quickly find answers and improve productivity. However, the degree of utilization within the company was uneven, which impacted their ability to visualize the status of utilization and extract insights to enhance functionality and performance.

Data flow

Operational, customer, and transactional data lands in Databricks and is transformed through managed pipelines before being served through Databricks SQL, Genie

Solution architecture

4 components · 3 layers
  1. Compute
    • Agent Bricks Supports agentic or GenAI applications that NTT DOCOMO uses to automate or accelerate work.
  2. Serving
    • Databricks SQL Serves dashboards, interactive analysis, and governed SQL workloads for business users at NTT DOCOMO.
    • Genie Lets nontechnical teams at NTT DOCOMO query governed data in natural language.
  3. Orchestration
    • Lakeflow Jobs Orchestrates recurring ingestion and transformation pipelines inside NTT DOCOMO's lakehouse.

Architecture clues

  • Cloud: Azure
  • Key product: Agent Bricks
  • Key product: Databricks SQL
  • Key product: Genie
  • Key product: Lakeflow Jobs

Evidence from the source

90% More efficient analysis of LLM usage
As a result, NTT DOCOMO was able to support decision-making for functional and performance improvements and reduce the time spent manually processing and analyzing data by 90%.
As of February 2025, the LLM value-added platform has approximately 10,000 MAUs and 3 million monthly calls, and it is being used in a variety of use cases.
For example, the network quality improvement team used the internal LLM tool to analyze customers’ social media data, processing 20,000 entries daily to detect issues faster.
NTT DOCOMO is a Japanese mobile operator serving approximately 90 million subscribers, connecting individuals through 5G, LTE, broadband and international services.
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
    4 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

    NTT DOCOMO customer story

    First-party
    https://www.databricks.com/customers/ntt-docomo
    Source type
    Vendor-published customer story
    Published
    Mar 24, 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 Mar 24, 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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