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Databricks Media and Entertainment Switzerland

SRF

Marketing Personalization · Real-time Analytics

SRF needed a better way to support audience intelligence, recommendation, and editorial analytics for public media because digital consumption was rising fast while siloed metrics and custom KPI pipelines kept editorial teams from understanding audience behavior in time.

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1.3M of data ingested and processed each day
200GB data points analyzed for insights
90 billion

Reported impact

Business outcome

SRF improved audience intelligence, recommendation, and editorial analytics for public media with measurable results including 90 billion, 200GB data points analyzed for insights, and 1.3M of data ingested and processed each day, while giving teams a faster path from governed data to production decisions.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

SRF uses Databricks Data + AI Platform to support audience intelligence, recommendation, and editorial analytics for public media. The implementation replaced a legacy state where digital consumption was rising fast while siloed metrics and custom KPI pipelines kept editorial teams from understanding audience behavior in time. Streaming and broadcast audience events and digital app activity move through Lakeflow Jobs and Spark Declarative Pipelines and land in Delta Lake, giving teams one trusted data foundation. Unity Catalog governs access, lineage, and compliance across sensitive datasets. Genie operationalizes models, agents, and analytics that deliver content recommendations and editorial dashboards. SRF improved audience intelligence, recommendation, and editorial analytics for public media with measurable results including 90 billion, 200GB data points analyzed for insights, and 1.

Data flow

Streaming and broadcast audience events and digital app activity land on a shared platform, pipeline services clean and standardize them, governance controls enforce trusted access, and AI, model-serving, and analytics services deliver content recommendations and editorial dashboards.

Solution architecture

6 components · 6 layers
  1. Ingest
    • Spark Declarative Pipelines Spark Declarative Pipelines standardizes streaming and broadcast audience events and digital app activity into production-grade tables and features.
  2. Compute
    • Databricks Data + AI Platform Databricks Data + AI Platform unifies engineering, analytics, governance, and AI workloads for audience intelligence, recommendation, and editorial analytics for public media.
  3. Storage
    • Delta Lake Delta Lake stores curated streaming and broadcast audience events and digital app activity so every team works from one trusted data foundation.
  4. Serving
    • Genie Genie gives business users natural-language access to governed data for faster decisions around audience intelligence, recommendation, and editorial analytics for public media.
  5. Orchestration
    • Lakeflow Jobs Lakeflow Jobs schedules ingestion and transformation of streaming and broadcast audience events and digital app activity into reusable datasets.
  6. Governance
    • Unity Catalog Unity Catalog applies centralized access controls, lineage, and auditability across sensitive datasets.

Architecture clues

  • 90 billion
  • 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.
  • Delta Lake provides the shared storage layer for curated lakehouse datasets.
  • Operational AI workflows are deployed directly beside governed data and analytics assets.

Evidence from the source

For that, we recognized that we need actionable data. We need to deeply understand how our customers are consuming our content.
Less and less people are watching TV or radio, and more and more are switching to our digital apps and videos on demand,
There wasn’t another option before,
We were building up the platform from scratch.
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.

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 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.
02

Traceability

Sources & provenance

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

  1. Databricks

    SRF customer story

    First-party
    https://www.databricks.com/customers/srf
    Source type
    Vendor-published customer story
    Published
    Jul 10, 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 10, 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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