Condé Nast Transforms Publishing Legacy into Data-Driven Digital Media for AI innovation with Databricks and AWS

Before migrating to AWS, Condé Nast’s technical infrastructure operated as a collection of independent brands rather than a unified media company. Each of the 22+ properties—from Allure to Architectural Digest and Ars Technica—maintained separate technology stacks, deployment methods, and data platforms. Different brands used different approaches with no company-wide standardization. "It was the wild west," recalls Vikram Palicherla, Senior Vice President, Software & Data Engineering at Condé Nast. "Everyone had their own IT infrastructure. If you needed insights from more than one brand—comparing Vanity Fair data to GQ data—someone had to manually extract information from two completely different tech stacks. Getting answers for executive-level strategy would take forever and become prohibitively expensive." This fragmentation prevented Condé Nast from understanding audiences across properties, building at scale, or responding to market shifts with the speed required to compete for reader’s attention across algorithmically-optimized social platforms.

Condé Nast's data infrastructure exemplified what Bhakta describes as being "data-rich but insights-poor." It collected massive volumes of audience data across digital properties, but information remained trapped in isolated silos with no unified view of reader preferences or content performance across brands. Different properties maintained varying levels of analytics sophistication—some captured deep engagement metrics while others tracked only basic page views—making cross-brand analysis impossible. This data fragmentation forced critical business decisions to default to subjective opinion rather than objective insights. Unified data would unlock Condé Nast's ability to identify content trends before they peaked, create cross-brand content strategies that connected audiences across properties, and help readers discover relevant content throughout its diverse portfolio. It would also lay the foundation for deploying AI for content recommendations, automated workflows, and predictive analytics. "Your data strategy is your AI strategy," emphasizes Bhakta. "You must organize data before extracting insights." For a creative entertainment company with over a century of content assets, AI presented opportunities to unlock value from historical archives, optimize editorial workflows, and deepen audience relationships—but only if built on a solid data foundation. Condé Nast needed cloud infrastructure that could unify fragmented systems, provide enterprise-grade security and compliance across global markets, and support AI deployment that augmented human creativity rather than replaced it.

To eliminate the operational complexity of maintaining 22+ independent technology stacks, Condé Nast standardized on Amazon Elastic Kubernetes Service (Amazon EKS) as its container orchestration foundation for portability, improved access management, observability, and automated upgrades across all brands. It migrated 800+ media properties from fragmented deployments—where some brands used self-managed Kubernetes clusters, others maintained custom deployment scripts, and regional implementations operated in isolation—to a unified Global Platform Services infrastructure. Amazon Elastic Compute Cloud (Amazon EC2) delivered the underlying compute capacity powering containerized workloads across multiple global regions with multi-region resiliency and failover capabilities. To optimize content delivery, Condé Nast replaced custom CDN rules with Amazon CloudFront, which reduced latency for global audiences and simplified infrastructure management.  It deployed AWS Lambda serverless functions to handle variable workloads efficiently—ensuring compute resources scaled dynamically based on demand without over-provisioning. AWS Control Tower enhanced governance, standardized security policies across accounts, and enforced compliance requirements consistently across all brands and markets. This infrastructure transformation provided the unified foundation necessary for Condé Nast to deploy its consumer-facing application platform Verso globally, migrate its Content Management System to support standardized deployments across all brands, and experiment at scale. "The migration gave us one platform for all brands and systems," explains Palicherla. "Going forward, the Global Platform on Amazon EKS made it possible to migrate to CloudFront and start doing essential things like experimentation with data collection that was previously impossible when each brand operated independently."

Condé Nast chose AWS Partner Databricks as its unified data lake house platform to architect a centralized analytics environment that could handle diverse data types, support real-time processing, and deliver a single source of truth where teams could understand audiences holistically across all the brands—integrating seamlessly with AWS. Condé Nast worked with Databricks to build this data platform on Amazon Simple Storage Service (Amazon S3) as the foundational storage layer for its data lake—storing all data in Databricks Delta Lake Format table format for maximum flexibility and interoperability. To ensure standardized, high-quality behavioral data collection across this global infrastructure, Condé Nast implemented Snowplow as its primary first-party behavioral data platform, deploying it globally across all 22+ brands, and 12 markets. Snowplow serves as the real-time data ingestion and enrichment layer, capturing granular engagement data including content consumption patterns, user navigation flows, click behavior, scroll depth, dwell time, and time-on-page metrics through standardized first-party tracking with schema validation and event modeling. This implementation included a complex global consent-management setup aligned to GDPR and regional regulations, ensuring all data streaming into the Amazon S3-based data lake is validated, enriched, and consent-compliant—establishing the core behavioral signal layer that fuels Condé Nast's analytics and AI workloads. AWS Lake Formation provides fine-grained access controls ensuring appropriate data governance across datasets, AWS PrivateLink delivers private connectivity between services without exposing data to the public internet, and AWS Key Management Service (AWS KMS) encrypts data at rest and in transit to maintain security standards required for handling audience information across 1,236+ global markets with varying regulatory requirements.

Databricks processes, transforms, and catalogs this information using Databricks Serverless for compute workloads supplemented by classic Databricks clusters running on Amazon EC2 for specialized processing requirements, while Databricks Unity Catalog provides centralized data governance, metadata management, and access control across all datasets—eliminating the previous chaos where different teams maintained inconsistent data definitions. The standardized Snowplow data feeds enable reliable modeling and machine learning feature generation across all downstream applications, supporting editorial teams with near real-time analytics for story performance, advertising teams with campaign tracking and inventory optimization during major events like the Met Gala and Oscars After Party, commerce teams with behavioral attribution across subscriptions and e-commerce, and BI teams with unified KPIs across all brands. Data science teams use MLflow on Amazon SageMaker AI for machine learning lifecycle management. With a single tool, teams can track experiments, version models, and deploy predictive analytics into production that power business intelligence dashboards, recommendation engines, and predictive models identifying emerging content trends. The unified data platform follows a medallion architecture—bronze, silver, and gold layers—ensuring data quality improves through each processing stage, with raw data entering the bronze layer, undergoing cleaning and standardization in the silver layer, and reaching the gold layer as analysis-ready datasets suitable for powering business decisions and training machine learning models.

This centralized data foundation eliminated the fragmented systems that prevented cross-brand insights, established consistent measurement standards where engagement metrics and content performance indicators follow standardized definitions across all properties, and shifted Condé Nast to data-driven strategies backed by objective insights. "Condé Nast's transformation exemplifies why we built Databricks on AWS—to help enterprises break free from data silos that prevent innovation," says Will Collins, Global Vice President & General Manager, Databricks. "When we began our partnership, business decisions that should have taken hours were taking weeks because data lived in incompatible systems. Together with AWS, we gave Condé Nast what they needed most: a unified lake house where every brand, every team, and every data scientist could work from a single source of truth. Unity Catalog provided the governance framework to ensure data consistency across brands, while our serverless compute running on AWS gave them the scale to process millions of reader interactions in real-time."

With unified infrastructure and consolidated data in place, Condé Nast deployed AI capabilities using Amazon Bedrock that transformed editorial workflows and audience experiences. Condé Nast uses Amazon Bedrock for access to multiple FMs for various use cases. Bedrock delivers the inference capabilities that power content rights management, enabling Condé Nast to process the entire body of editorial content, analyzing images and assets, integrating contract and licensing data to identify rights availability in minutes rather than weeks. It implemented AI-powered content moderation workflows that automatically flag potential issues and rout content through appropriate review processes. 

Amazon Bedrock's integrated Guardrails enable Condé Nast to deploy AI with appropriate constraints, ensuring model outputs align with editorial standards and brand voice while preventing potential issues before they reach audiences—which is critical for a media company operating across 12 global markets with varying regulatory requirements. Condé Nast also leverages Amazon SageMaker for specialized machine learning applications, including search functionality powered by custom-trained models that understand editorial content context better than generic algorithms. “Content rights management that previously required weeks now completes in minutes, unlocking a century of content assets that were effectively trapped due to prohibitive manual verification costs,” says Bhakta. Content moderation efficiency enables human moderators to focus on nuanced judgment calls that require editorial expertise. "We don't use AI to create content," emphasizes Bhakta. "We use it to do interesting things with content. Always augmenting humans, never replacing them."

The lake house enabled Condé Nast to shift from opinion-driven decisions to data-driven strategies backed by objective insights. This transformation manifests in concrete business results. The AI implementations delivered dramatic operational efficiency improvements. Content rights management processes that previously required weeks of manual legal and licensing review now finish in minutes through Amazon Bedrock-powered analysis. This efficiency doesn't only save time—it unlocks a century of content assets that were once effectively trapped due to the prohibitive cost of manual rights verification, now enabling editors to discover and republish valuable historical content that generates new audience engagement and revenue. The AI-powered content moderation workflows reduced the manual effort required for routine checks, allowing human moderators to focus on nuanced judgment calls that genuinely require editorial expertise. Condé Nast is exploring additional AI agent applications for workflow automation, audience engagement enhancement, and creator empowerment—all while maintaining its commitment to augmenting human creativity rather than replacing editorial judgment.

"Legacy doesn't mean outdated—it means experienced," reflects Bhakta. "We've evolved our 115-year publishing heritage into a competitive advantage by building the data foundation that makes AI possible. There are no shortcuts to innovation; you have to do the work." Condé Nast's transformation illustrates how making data accessible is the essential backbone for scaling AI across any enterprise. "As we looked at AI maturity, it forced us to focus on foundational data issues," says Bhakta. "We learned there are no shortcuts—you must organize data before extracting insights. AWS and partners like Databricks gave us the platform to do that work properly. Now, we're positioned not just to compete in the attention economy, but to shape the future of how audiences engage with quality journalism and storytelling in the digital age."