Bally’s operates across digital and retail gaming, where rising player expectations for personalization, speed, and seamless omnichannel experiences are reshaping the industry. However, legacy systems and technology created fragmentation that slowed product launches, limited experimentation, and made it difficult for teams to properly leverage data. By building its Vitruvian platform on Databricks, Bally’s unified its data foundation and created the operating leverage needed to deliver new customer experiences and support growth. As a result, Bally’s improved time-to-market by 60-70% while shifting to a self-service operating model. Business teams can now build and own solutions independently, while technology teams focus on delivering reusable core enterprise capabilities. This shift has helped teams make faster, more informed decisions, deliver more personalized, real-time player experiences, and execute strategic initiatives with greater speed and consistency.
Fragmented systems limited speed, scale and innovation across the business
Bally’s operates a global gaming business across digital platforms and retail casinos, where success depends on delivering seamless, engaging player experiences while meeting strict regulatory requirements across multiple jurisdictions. As the business expands organically and through acquisitions, data has become central to driving customer engagement, enabling compliant operations and accelerating product delivery across the business. Player expectations have also evolved rapidly, with customers increasingly expecting deeply engaging, real-time experiences comparable to leading consumer apps outside the gaming industry.
Across the organization, data powers both customer-facing and internal operational use cases. On the consumer side, Bally’s uses data to deliver engaging player experiences across various digital channels, including recommendations and personalized journeys which drive real-time engagement.
These same capabilities also support critical areas such as responsible gaming and fraud detection, which are essential in a highly regulated industry. On the business side, teams across marketing, product and commercial operations use unified data to better understand player behavior and make faster strategic decisions. Data also underpins regulatory compliance to ensure consistent and governed access to information.
The platform has also become a key enabler of their expansion strategy. Following the recent Bally’s International Interactive acquisition by Intralot, they rapidly integrated data, analytics, marketing, and governance capabilities across the combined organization, significantly accelerating integration timelines and establishing a consistent operating model across the expanded business.
“Our goal is to make data a capability that every part of the business can leverage, rather than something owned exclusively by technology teams,” said Mark Borg, SVP of Data at Bally’s.
Looking back, Bally’s can truly understand how our legacy solutions created real constraints. Data was fragmented across systems, making it difficult to deliver consistent insights or scale analytics globally. Such deployments created operational silos that limited flexibility and slowed innovation. As a result, business teams relied heavily on centralized engineering teams, creating bottlenecks that prevented a true self-service model. Borg explained, “The business was moving faster than our operating model could support, which is what ultimately led us to build a self-service platform that enables teams across the organization to operate with greater autonomy.”
Databricks powers Vitruvian to deliver strategic insights and personalized gaming experiences
Bally’s built the Vitruvian platform on Databricks to unify data, analytics and machine learning into a single foundation supporting both digital and retail gaming operations. This allows them to deliver engaging player experiences, maintain regulatory compliance across jurisdictions, and accelerate business decision making from a shared, governed data layer. This modernization effort, grounded by the migration from their legacy environments to a more scalable lakehouse architecture, enabled a more scalable operating model for analytics and AI.
At the core is a single data plane powered by Delta Lake, enabling teams to process data once and use it across multiple use cases. This eliminated fragmented pipelines and reduced duplication across systems, giving teams faster access to trusted information. “We now operate from a single data foundation with consistent governance across the enterprise,” said Mark Borg. “That gives us governance and auditability at a level that would have been very difficult to achieve with legacy solutions.”
With Unity Catalog, Bally’s implemented fine-grained access controls, data lineage and auditability across its platform. This is especially important in gaming, where regulatory requirements vary across jurisdictions and data must remain tightly controlled and traceable. Instead of rebuilding specific compliance frameworks for specific scenarios, Bally’s can now adapt governed structures across markets, reducing the complexity, cost, and effort associated with expansion into new markets. Compliance is no longer a separate downstream process, but an embedded capability within the data platform, enabling teams to move quickly within a governed framework.
With Databricks AI Runtime (formerly known as Databricks Serverless), Bally’s has reduced infrastructure overhead and simplified how data workloads are managed. By removing the need to maintain complex infrastructure, teams can focus more on building data products and supporting business outcomes. Bally’s has since migrated hundreds of pipelines from Airflow to Lakeflow Jobs, simplifying orchestration and scaling toward hundreds of streaming workloads. Real-time data ingestion is powered by Spark Structured Streaming, enabling reliable, low-latency processing of continuously arriving data at scale. Data ingestion was further streamlined using connectors that allow new data sources to be integrated in days rather than weeks. Lakehouse Federation enables Bally’s to query and govern data across external systems without moving or copying the data, reducing complexity and accelerating time to insight, giving teams a more complete, real-time view of each player across channels.
At the same time, Bally’s shifted to a self-service model. Instead of building every solution centrally, the Vitruvian data team now provides core platform capabilities while business teams build and operate their own use cases, leveraging Vitruvian’s self-service capabilities. “As a technology team, we should focus more on building core re-usable capabilities,” said Borg. “The role of the platform team is to provide the capabilities, governance, and guardrails that allow business teams to build solutions independently.”
Unlocking growth and competitive advantage
Bally’s transformation with Databricks has delivered measurable improvements across speed, operational efficiency and organizational agility. This has strengthened Bally’s ability to execute across a rapidly evolving and highly regulated industry. At a high level, Bally’s achieved:
60–70% Faster delivery of products, capabilities, and new market launches, enabling faster response to market opportunities and player needs.
30–40% Reduction in total cost of ownership through platform consolidation and serverless compute.
10x Increase in model experimentation and delivery velocity, accelerating the rollout of personalized experiences, recommendations, and intelligent decisioning for players.
8x Faster onboarding of new source platforms, enabling a more complete and timely view of the player across channels.
The most significant shift has been time to market. “The biggest gain is the speed of shipping new products,” said Borg. “We are easily 60 to 70% faster than before.” Work that previously took months can now be delivered in days, removing crippling bottlenecks between data and business execution.
The shift has been just as pronounced for machine learning. With a unified data foundation and serverless compute, teams now experiment and ship models roughly 10x faster, accelerating the personalized recommendations and intelligent decisioning that players experience directly. “We can test, learn, and put better experiences in front of players in a fraction of the time it used to take,” said Borg.
Databricks also played a critical role in accelerating Bally’s Intralot integration. With a centralized Lakehouse architecture, they were able to easily unify infrastructure and workflows in roughly three months, significantly faster than traditional integration timelines. This speed has allowed Bally’s to integrate new businesses and markets more efficiently, making data a strategic enabler of expansion.
Realizing that vision required close collaboration between Bally's technology and business teams and the Databricks technical and account management teams. Together, Bally’s challenged established ways of working and built a scalable architecture that enables the business to move faster while maintaining the governance, consistency, and control required in a highly regulated industry.
“We are significantly more productive and efficient than ever before,” said Borg. “Barriers are gone and we have much more room to innovate.” By offloading infrastructure management to Databricks AI Runtime, engineering teams spend less time tuning systems and more time building products that directly support customer engagement, regulatory agility and business growth.
Looking ahead, Bally’s is focused on expanding self-service capabilities so that more teams can build and deploy data-driven products independently. They’re also continuing to invest in embedding intelligence into customer experiences and operational decision making across its platform. The company is actively exploring additional AI and generative AI use cases to further personalize player experiences, streamline operations and scale proven capabilities across the enterprise. Databricks remains the foundation for this strategy. As Borg summarized, “Databricks gives us the foundation to scale data and AI consistently across the enterprise while maintaining the governance, agility, and efficiency required to support our growth strategy.”