BookMyShow, India’s entertainment technology platform for movie and live event ticketing, has scaled self-service analytics across its business by adopting the Databricks Data + AI Platform, Unity Catalog and Genie. The company sells around 22 million tickets every month to more than 100 million monthly active users across movies, live experiences, sports and events. As its operations expanded, the ability to access timely data became increasingly important for teams making decisions across marketing, finance, live entertainment and cinema operations.
The company’s earlier analytics model relied heavily on a central data engineering and analytics team. Business users had to submit requests for reports and analysis, with the team writing SQL queries and delivering results based on its available capacity. This created a bottleneck, limiting how quickly teams could investigate trends, assess performance or respond to business requirements. With Genie, BookMyShow has moved towards a self-service model in which employees can ask questions of business data in natural language. The shift has reduced routine analytics requests to the central team and allowed it to focus more on data infrastructure, governance and pipelines.
Moving beyond a centralised analytics model
When Noel Curtis joined BookMyShow as Chief Technology Officer, the company’s data environment was split between a warehouse on Amazon Redshift and a data lake built on Amazon S3 and Athena, with Glue handling ETL. The setup lacked a unified data platform and a common catalog and governance mechanism. As a result, most analytics requests from marketing, executives and business units had to pass through the central data team.
Self-service access was largely limited to users familiar with SQL. Even for those users, the data was not catalogued in a way that made independent analysis practical for most teams. “There was practically no self-serve capability,” Curtis said, noting that SQL knowledge was a limiting factor for users seeking to analyse data independently. BookMyShow subsequently moved its warehouse and lakehouse workloads to Databricks and invested in Unity Catalog to support data access and governance across the organisation. The introduction of Genie built on that foundation, combining business context with natural-language interaction to make analytics more accessible.
Domain-specific Genie Agents support business teams
Rather than relying only on a single general-purpose assistant, BookMyShow established domain-specific Genie Agents for teams including marketing, customer lifecycle management, digital marketing, live events, cinemas and movie intelligence. Each agent is grounded in the relevant business domain, while Genie One remains available for broader questions across the company.
The approach allows employees to ask questions related to their own business areas, from ticket sales and unique transactors for a movie title to gross merchandise value by city or event category, offer performance and revenue trends across events. Teams use the tools to investigate anomalies, analyse performance and identify emerging patterns.
The marketing and customer lifecycle management agent also supports the tracking of more than 3,500 offers running at any given time, including payment promotions, promo codes, third-party partnerships and loyalty programmes. As users become more familiar with the tools, some progress to Genie Code for deeper analysis and dashboard creation, working alongside the data engineering team when required.
BookMyShow reports that the broader adoption of Genie has resulted in 90% fewer data engineering tickets, as teams increasingly handle routine analytics requirements independently. Curtis said the platform has changed data access from a capability available to a small group of engineers to something employees across the company can do themselves in seconds.
Governance remains central to self-service access
As access to analytics expands, BookMyShow relies on Unity Catalog for fine-grained access control, data classification and lineage, particularly for personally identifiable information. The company is also preparing for requirements under India’s Digital Personal Data Protection Act. The governance controls extend to the data accessed through Genie, helping ensure that users’ questions and resulting insights remain within their authorised access boundaries.
Publisher integrations move from months to about a day
BookMyShow has also applied its data platform to partner-facing workflows. Previously, each publisher integration required custom engineering work. After standardising publisher data access through OpenSharing, integrations largely involve providing an API contract and configuring the relevant filters.
The change has reduced integration timelines from months to approximately one day, while simplifying how publisher partners access data for movie and event listings, lead generation and aggregation. The company has also replaced manually compiled post-game reports for Indian Premier League ticketing partners with a self-service analytics application built using Databricks Apps. The application provides access to ticketing and occupancy data, allowing franchise teams to review performance without waiting for custom reports.
A single data analyst built and deployed the application’s front end and back end in a couple of hours. BookMyShow then expanded access from one franchise to four within a matter of days. During a Mumbai Indians game, the ticketing head accessed live occupancy data on a phone and used it to inform decisions ahead of the next match.
Data teams shift focus to platform and governance
With business teams handling more day-to-day questions through Genie, BookMyShow’s central data team can concentrate on maintaining Unity Catalog, data pipelines and its medallion architecture. The company is also exploring further uses of Databricks across real-time personalisation, fraud and bot defence during high-demand ticket sales, natural-language customer support, and sponsorship and advertising attribution. For BookMyShow, the move to self-service analytics has extended beyond faster reporting. It has changed how business teams access information, how partner data is shared and how the central data function allocates its time, while keeping governance as a core part of the operating model.
