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IBM Public Sector Spain

Royal Spanish Athletics Federation (RFEA)

Real-time Analytics

RFEA had athlete records, sensor streams, biomechanics video and physiological metrics spread across disconnected systems, limiting its ability to combine insights quickly and use data as a coaching advantage.

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1,000 coaches by 2028

Reported impact

Business outcome

RFEA created a national-scale evidence-based coaching platform that improves how data is used across disciplines today and establishes a strong foundation for broader AI-driven performance and injury-prevention use cases.

  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

RFEA partnered with IBM and Habber Tec to build IA-THLETICS on IBM watsonx.data as a modern lakehouse for performance analytics. Historical records, live wearable telemetry, high-speed video and physiological measurements are standardized into queryable tables inside a governed environment so the federation can work from one AI-ready data foundation instead of siloed systems. Coaches and analysts then access unified dashboards showing cadence, recovery and health indicators in real time, while machine-learning models use the consolidated data to detect biomechanical patterns, predict injury risk and recommend more personalized training plans. The platform is designed to support daily coaching and competition decisions rather than replace expert judgment, and it gives RFEA a scalable base for multilingual dashboards, predictive modeling and personalized training at national-team scale. In effect, watsonx.data becomes the operational backbone that turns raw athletic telemetry into evidence-based coaching.

Data flow

Historical athlete records, live wearable streams, high-speed video and physiological metrics are normalized in watsonx.data, surfaced through unified dashboards and consumed by AI models that generate coaching and injury-risk insights.

Solution architecture

1 components · 1 layer
  1. Storage
    • IBM watsonx.data Provides the lakehouse backbone that unifies athlete records, sensor data, video and physiological measurements into a governed analytics platform.

Architecture clues

  • Habber Tec helped avoid custom integrations by designing the platform around open and scalable data architecture patterns.
  • IA-THLETICS already supports multiple disciplines including race walking, discus, hurdles, sprint and relay events.
  • The architecture is explicitly described as a modern data lakehouse built on watsonx.data.

Evidence from the source

RFEA centralizes athlete data with IBM watsonx.data
a modern lakehouse architecture that connects historical records with live sensor streams, high-speed video, and physiological data
millions of data points from shoes, wearables, and medical tests feed AI models
plans to enable 1,000 coaches by 2028
Transparency Evidence & methodology 75/100 reference value · 87% 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 value75/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 specificity8/10
    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. IBM

    Royal Spanish Athletics Federation (RFEA) customer story

    First-party
    https://www.ibm.com/case-studies/rfea
    Source type
    Vendor-published customer story
    Published
    Apr 2, 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

Date evidence

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

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