Annual cloud cost savings delivered through FinOps optimization
Reduction in cloud infrastructure costs through workload optimization
Experience across Snowflake, AWS, Databricks, and major data ecosystems
Every intelligence capability that drives publisher revenue – yield optimization, audience activation, content intelligence, measurement unification, subscriber health – depends on a data infrastructure that is reliable, governed, scalable, and connected. Most publisher data platforms were not designed for the operating requirements they are currently running under.
Infocepts designs and deploys data platform modernization for media and entertainment organizations – with deep experience in the specific data engineering, governance, and cloud architecture requirements of broadcast, streaming, and digital publishing environments.
Media and entertainment data platforms are often built for storage, not for commercial activation, leading to fragmented systems, rising costs, and limited visibility into revenue impact. Without a unified architecture, data remains underutilized across content, audience, and advertising workflows.
Most major publishers have invested in a data lake or cloud data warehouse. The commercial ROI of that investment – in terms of intelligence that flows back to revenue-generating teams – is often unclear. The infrastructure is sound; the activation layer is missing.
M&A in media creates data architecture challenges specific to the industry – content rights data from two different rights management systems, ad sales CRM data from two different Salesforce instances, audience data from two different authentication frameworks. Consolidation without a clear architecture plan creates integration debt that compounds for years.
Media organizations running significant streaming and ad delivery workloads on AWS, Azure, or GCP often lack the operational intelligence to understand which workloads are generating cost and which are generating value. Cloud FinOps for M&E requires domain knowledge of media-specific workload patterns – not generic cloud cost management frameworks.
Many broadcasters and publishers maintain legacy infrastructure – on-premise data warehouses, older ETL architectures, proprietary content metadata systems – in parallel with modern cloud infrastructure. The cost of operating both stacks without a clear migration path is significant.
Data governance in M&E must address content rights, subscriber privacy, audience data consent, and advertising data retention – each with different regulatory requirements and operational complexity.
Infocepts designs modern data platforms that connect content, audience, and advertising data into a unified architecture – enabling scalable analytics, cost efficiency, and reliable data foundations for revenue-driving use cases.
We design lakehouse architectures optimized for M&E-specific workloads - combining the storage flexibility of a data lake with the query performance and governance capabilities of a data warehouse, structured around the content, audience, and advertising data domains that drive commercial outcomes.
We deliver cloud cost intelligence specific to M&E workloads - identifying the storage, compute, and data transfer cost patterns that are generating the most spend, and implementing optimization strategies that reduce cost without degrading the data product quality that commercial systems depend on.
We design and implement data governance frameworks for M&E organizations - covering data quality standards, access controls, data lineage, privacy compliance, and content rights data management.
We design and execute post-merger data integration programs for M&E organizations - consolidating CRM, content, audience, and ad delivery data from combined entities into a unified architecture that serves both legacy operations and new commercial requirements.
In annual cloud savings delivered at a single broadcaster through FinOps optimization
Combined savings across storage, application, technology, and infrastructure optimization initiatives
Platform services managed at 100% SLA adherence
Explore how media organizations are modernizing data platforms, optimizing cloud costs, and building scalable data architectures to support advanced analytics and commercial growth.
A data lakehouse is a data architecture that combines the low-cost, scalable storage of a data lake with the performance, reliability, and governance capabilities of a traditional data warehouse. For media and entertainment organizations, a lakehouse architecture is designed to handle the specific data domains of the industry – content metadata, audience behavioral data, ad delivery data, subscriber records, and rights management data – in a unified environment that supports both real-time operational queries and large-scale analytical workloads.
Media companies reduce cloud infrastructure costs through FinOps – the practice of continuously analyzing cloud spend by workload, identifying cost-to-value mismatches, and implementing optimization strategies such as storage tiering, compute scheduling, job orchestration, and infrastructure right-sizing. For broadcasters and streaming platforms, cloud FinOps requires domain knowledge of media-specific workload patterns – particularly the cost dynamics of large-scale video storage, streaming delivery, and programmatic ad serving at peak load.
Data governance for media companies is the set of policies, standards, processes, and technologies that ensure data is accurate, accessible, consistent, and used in compliance with applicable regulatory and contractual requirements. In M&E, data governance must address content rights management (which data is licensed for which uses), subscriber privacy (GDPR/CCPA compliance for audience data), advertising data retention (compliance with ad industry self-regulatory standards), and data quality standards for systems that drive commercial decisions.
