LatentView Analytics

The client operated a traditional data warehousing environment that could not support modern analytical workloads or scale to accommodate growing volumes of transactional, ERP, clickstream, IoT, system log, document, social, and external data. The absence of a centralized data foundation limited enterprise-wide collaboration, slowed analytics adoption, and made it difficult for business users to access trusted data across the organization.

LatentView designed and implemented a cloud-based centralized data repository with a self-service analytics platform. The solution integrated transactional, master, and external data sources into a cloud data lake, isolated legacy data marts to create a governed and secure data foundation, enabled multiple enterprise data access mechanisms, and supported real-time indexing, distributed processing, advanced analytics, and machine learning workloads through a self-service analytics layer.

The client operated a fragmented data ecosystem with siloed datasets, poor data integrity, manual deployment processes, and limited governance. The lack of standardized engineering practices made it difficult to scale customer-centric initiatives, maintain a centralized source of truth, and support enterprise-wide analytics.

LatentView implemented a modern Snowflake-based lakehouse architecture together with a DataOps framework using DBT and automated CI/CD pipelines. The solution introduced modular data models, automated testing, embedded data quality validation, role-based and row-level security, metadata discovery, governance controls, and optimized data marts supporting downstream analytics and machine learning workloads.

The client relied on fragmented systems, siloed enterprise data, and manual reporting processes that limited sales, advisor, customer, and compliance analytics. The absence of a centralized enterprise data repository also delayed executive reporting and regulatory reporting while increasing operational effort.

LatentView built a cloud-based enterprise data and analytics ecosystem centered on a centralized enterprise data hub. The implementation consolidated data across multiple internal and external sources, migrated ten years of historical data from seven source systems using nine ingestion patterns, established a configurable ingestion and Change Data Capture (CDC) framework, automated regulatory reporting, and implemented self-service dashboards together with secure enterprise data sharing.