Product descriptions:
As the world’s first mobile navigation service, TMAP Mobility has established themself as an industry leader over the past 20 years by providing the fastest and safest routes possible. With over 25 million subscribers and adoption in more than 95% of domestic vehicles in Korea, TMAP handles more than 7.3 billion mobility data points annually, delivering personalized travel experiences at scale. However, their existing data environment was poorly suited to efficiently manage such massive data volumes. Teams were hindered by limited data access and rigid security controls, which made it difficult to accelerate AI-driven recommendation efforts. Aiming for data democratization and enhanced governance, TMAP adopted the Databricks Platform and successfully established a new data environment to support their future growth.
Data access and governance roadblocks stalled personalization efforts
Millions of travel decisions are now made on the move: where to go, how to get there, when to leave and which route to take. TMAP Mobility powers these decisions. Their navigation platform boosts business efficiency by reliably processing the massive volumes of traffic data it generates, while also creating new value through trusted AI capabilities.
To improve customer retention, TMAP Mobility set out to deliver more personalized, AI-driven recommendations. This meant delivering distinctive user experiences through their internal Agent Experience (AX) initiatives. These efforts required a secure and scalable infrastructure to effectively harness large amounts of customer data.
Unfortunately, the existing data environment was unable to support these goals. For one, data accessibility was extremely limited. General users struggled to explore or utilize data in the catalog, resulting in a heavy reliance on data specialists for even the most basic tasks. This dependency led to frequent bottlenecks during service expansion and product analysis, ultimately reducing overall productivity. As explained by Chung-Woo Lee, Head of Data Platform and AX at TMAP: “Over half of the existing data analysis team’s workload consisted of simple extraction requests, making it difficult to focus on in-depth analysis or modeling.”
Rigid permission controls and an inflexible governance system added further challenges. With access permissions limited to only basic levels, teams faced obstacles when handling personal data or performing other governance-sensitive tasks.
To address these limitations — and to enable both greater data democratization and stronger governance — TMAP Mobility adopted the Databricks Data Intelligence Platform.
Realizing data democratization with the Databricks Platform
TMAP Mobility successfully promoted self-service data analysis and revolutionized their internal data utilization culture by adopting the Databricks Platform.
With Genie Space, TMAP created an environment where non–data specialists could directly explore and analyze data using natural language commands. This enabled nontechnical departments, such as planning and marketing, to freely utilize data for business applications and gain new insights. By providing an intuitive yet efficient analysis environment, TMAP achieved the data democratization necessary to reduce repetitive tasks for analysts. The data team is now able to focus more on high-value work.
Integrated data analysis and visualization are now possible with Databricks’ capabilities for real-time monitoring and rapid querying. Dashboards on AWS support interactive reporting and performance monitoring. Databricks SQL, a serverless data warehouse, provides fast, governed access to curated data. Databricks Notebooks enable deeper analytical workflows for exploration and modeling. Together, these tools allow teams to validate hypotheses and share insights — all within a single, secure platform.
Additionally, TMAP implemented granular permission management and a data ownership system utilizing Unity Catalog. This allows flexible access rights to be granted to each department — without requiring intervention from the data engineering team. “Unity Catalog enabled highly granular permission management,” Chung-Woo said. “The introduction of column masking policies strengthened our security framework, significantly increasing data utilization."
Maximizing the data team’s operational efficiency
The adoption of Databricks significantly reduced the burden of repetitive tasks on the data team and improved overall operational efficiency. As of September 2025, the analytics team has received simple data extraction requests that are 68% lower than previous levels. Approximately 48% of all employees can now directly explore and utilize data, vastly improving data accessibility. This change enables data analysts to focus on more advanced analytical tasks, strengthening the organization’s overall data utilization capabilities.
To support their goal of improving navigation experiences through more differentiated AI-powered recommendations, TMAP leveraged Databricks to accelerate their internal AX initiatives. By integrating the Recommendation Engines Solution Accelerator, the data team was able to prototype and deploy scalable recommendation models faster and with fewer resources. This helped deliver tailored user experiences based on real-time mobility data, enhancing service relevance and engagement. With foundational efficiencies in place, TMAP is now better positioned to deliver dynamic, data-driven experiences that meet the evolving needs of their users.
Moving forward, TMAP plans to expand Unity Catalog as a central data hub. Chung-Woo’s team also aims to reduce the resources required for data migration by leveraging AWS Glue Federation and External Table integration. Chung-Woo emphasized, “In our journey to build a highly reliable data infrastructure supporting TMAP's AI mobility services, Databricks has made a significant contribution as a data platform offering both scalability and flexibility.”
Building on the success of the Databricks Platform, TMAP will consolidate and migrate all internal data to Databricks, thereby strengthening data governance and enabling more advanced analytics.