Product descriptions:
SRG SSR is Switzerland’s public service media organization, providing independent information, education and entertainment, operating through five regional business units (SRF, RTS, RSI, RTR, and SWI). It produces and distributes content in four national languages (German, French, Italian and Romansh) across 17 radio stations, seven television channels and various digital platforms to a population of 9 million people. With the ongoing shift of user behavior from scheduled broadcasts to on-demand digital platforms, the German-speaking business unit SRF faced a growing need to understand user behavior, personalize content and align editorial strategy with audience insights. Without a unified data infrastructure, cross-team collaboration was challenging, custom KPIs were difficult to implement and daily ingestion of 200GB from 1.3 million unique users proved unwieldy. With Databricks, SRF built a scalable, cloud-native platform that now powers everything from machine learning–driven content recommendations to executive dashboards. The broadcaster has boosted developer productivity and data transparency by making insights accessible to nontechnical users across the organization. What once took hours for some components can now run 70x faster — enabling smarter, faster decisions that keep public media relevant in the digital age.
Growing gap between legacy broadcast infrastructure and digital demand
As Switzerland’s national public broadcaster, SRG SSR carries a weighty mission: to provide trusted news, foster national culture across four language regions and support education through well-researched, objective journalism. But in a world shifting away from scheduled TV and radio to always-on digital consumption, SRG SSR needed to evolve how they reach their population of roughly 9 million listeners. “Less and less people are watching TV or radio, and more and more are switching to our digital apps and videos on demand,” Corinne Ruckstuhl, Product Manager for Data and AI at SRF, said. “For that, we recognized that we need actionable data. We need to deeply understand how our customers are consuming our content.”
To meet this shift, SRF invested in a modern data strategy that would enable personalized, data-informed experiences across their growing digital footprint. This includes news articles, TV segments and original series, podcasts and educational videos available on demand.
SRF’s core data use cases span the entire content lifecycle. Customer analytics help the organization understand how audiences engage with digital content across platforms, while business intelligence dashboards and reports deliver strategic insights to editorial and business teams. Product development efforts are shaped by data as well, from measuring adoption of features like push notifications to assessing how users navigate the mobile app. Machine learning–driven personalization models recommend videos tailored to user behavior. Meanwhile, trending content aggregation features surface what’s most watched or read in real time, providing users with interesting content they might have missed.
These use cases are deeply embedded into SRF’s editorial and operational workflows. According to Corinne, “We’re able to better understand how users are behaving on our platforms, and from that we’re building data products. That includes dashboards and reports for management, but also more advanced analytics, like algorithms for personalization and recommendations — similar to how Netflix works — to provide what is relevant for each user.”
By centralizing and analyzing this data, SRF can align content performance with strategic goals, improve engagement and ensure their public service mandate is fulfilled in the digital age. But SRF’s transformation wasn’t simply a matter of buying a tool. Before the Databricks Platform, the organization lacked a unified data infrastructure. “There wasn’t another option before,” Corinne noted. “We were building up the platform from scratch.”
This blank-slate approach came with real challenges. SRF’s data was siloed across departments and business units, making collaboration and shared metrics difficult to achieve. At the same time, the organization faced increasing pressure to standardize data across their regional operations and unify their approach under a national strategy. Compounding the issue, SRF handles a massive volume of data daily — 200GB of ingestion from over 1.3 million unique visitors.
Without a shared data foundation, different teams lacked visibility into how content was performing or how user behavior varied across platforms. That meant SRF needed a solution that could scale, offer transparency and support both technical and nontechnical users alike. The next step in their journey was clear: a modern data platform built to unite — and activate — their data.
Powering editorial intelligence with the unified Databricks Platform
Databricks Data Intelligence Platform quickly emerged as the backbone of SRF’s modern data and AI platform — an end-to-end solution flexible enough to support the broadcaster’s evolving needs while offering the governance and scalability required for a national public institution. With Databricks, SRF established a cloud-native infrastructure to ingest, process and analyze large volumes of diverse data types — from user interaction logs and streaming content data to structured editorial performance metrics.
At the core of their platform lies Delta Lake, which SRF uses to power reliable data pipelines for downstream batch analytics at scale. This enabled the team to implement a medallion architecture, progressively structuring data into Bronze, Silver and Gold layers for improved data quality, lineage and performance. This architecture helps streamline data workflows and ensures a high level of consistency and reliability across use cases — from executive dashboards to content personalization.
“Databricks gave us the technical flexibility we needed to build a platform tailored to our business goals,” Davide Heller, Technical Lead Data Team at SRF, said. “For example, our audience department built a unique performance metric called impact points. With Databricks, we were able to model and integrate this custom KPI directly into our analytics pipeline — something we couldn’t have done with traditional out-of-the-box tools.”
To further support collaboration and transparency, SRF adopted Unity Catalog, providing centralized access control and data governance. This was a game changer for teams across departments that needed visibility into where data originated, how it was processed and who was using it. Instead of working in silos, teams now operate from a single source of truth, reducing duplication and improving trust in data. According to Corinne, “Our stakeholders can now go into the catalog and see exactly how the data flows. That level of transparency is something we never had before. Other tools we use are more like a black box.”
Operationally, Databricks Workflows features enabled SRF to automate and secure their extract, transform, load (ETL) processes, replacing previous reliance on Azure Data Factory. This transition led to faster deployment cycles and simplified infrastructure management. The combination of Workflows and the Databricks environment significantly improved developer productivity and team morale, deploying changes faster and with fewer dependencies. “Now, we can do everything through Asset Bundles. It streamlined how we deploy and manage changes,” Davide said. Asset Bundles allowed SRF to centralize configurations in a single, version-controlled repository, removing the need for external orchestration tools and improving overall agility. This reduced deployment complexity while increasing consistency.
SRF implemented DLT within the Bronze layer of their medallion architecture. While it didn’t suit the more refined Silver or Gold layers, DLT has played a key role in simplifying ingestion logic and standardizing data transformation at the most granular level. As the team looks ahead to near real-time processing and better data quality monitoring, DLT is already laying the groundwork.
SRF is also exploring AI/BI Genie, the platform’s AI-powered assistant for querying and dashboarding. This is particularly valuable for nontechnical users like product managers and editors, who can now ask natural language questions of their data without needing to write SQL or navigate complex BI tools. “We see Genie as a key enabler of data literacy across the company,” Corinne added. “It opens the door for more people to engage directly with data and uncover insights that matter to their work.” In one recent example, Corinne’s team used Genie to quickly determine how many users had upgraded to a new app release — an analysis that previously required joining multiple tables.
At the organizational level, the Data and AI department at SRF is spearheading efforts to coordinate governance, develop data literacy programs and embed data ambassadors within various teams. The Databricks Data Intelligence Platform has made it easier to align technical teams with business users, ensuring that data is both accessible and actionable. The platform’s scalability also enables collaboration with other business units at SRG SSR, as SRF builds a shared data foundation to support company-wide initiatives.
By centralizing infrastructure while empowering decentralized teams, SRF is creating a data ecosystem where innovation and transparency go hand in hand, laying the groundwork for more intelligent, responsive public media.
Improving content strategy and engagement with faster insights at a nationwide scale
The transition to Databricks’ newest features, such as Asset Bundles and DLT, has yielded measurable workflow gains for SRF. Data processing times have accelerated dramatically — up to 70x faster in some workflows — enabling quicker time to insight for both operational teams and executive decision-makers. The ability to deliver insights faster has increased stakeholder confidence and helped inform key decisions around content strategy, customer engagement and product development. “One stakeholder told us that the analysis they used to do now runs 70 times faster,” Ruckstuhl said. “That saves time across the board and gets insights into the hands of the right people much quicker.”
The speed benefits extend beyond just analytics. Thanks to the adoption of Databricks, deployment processes have also improved. “The new architecture improved performance speed and just made developers happier,” Davide shared.
Looking forward, SRF plans to scale Databricks usage across the entire organization, enabling more stakeholders to build data products, personalize experiences and analyze performance at scale. Tools like Genie and Unity Catalog will play a critical role in making data accessible to both technical and nontechnical users, further embedding data into the organization’s culture. “Databricks has helped democratize data and dissolve silos,” Corinne summarized. “It puts data at the heart of how we operate and how we make decisions.” With this foundation in place, SRF is well positioned to continue delivering content that’s not only timely but also meaningfully informed by the audiences it serves.