Case Studies

A leading global biopharmaceutical company needed consistent data quality for drug candidate selection, drug development, and commercialization, including manufacturing and supply chain workflows. Fragmented data sources and independent context layers slowed clinical candidate selection, validation, and delivery, leaving limited visibility into recurring issues.

Acceldata established one unified observability layer across data sources for SAP modernization by standardizing data quality, reconciliation, freshness, anomaly detection, and schema drift controls. Migration toolkits, automated reporting, and bulk updates streamlined work across environments. For the digital team creating divisional agents, an enterprise intelligence layer was established with Acceldata ADM for common context.

Reduced time to clinical candidate selection from ~36 months to ~12 months. Delivered priority use cases in 60 days across three production programs. Increased right-first-time validation rate to ~85%. Reduced manual effort by approximately 95% across repeatable workflows. Accelerated policy and asset promotion from 2-3 weeks to hours. Automated daily trend and KPI reporting across more than 1,200 checks.

Inability to scale their data engineering and operations efforts as the transaction volumes increased by many orders of magnitude.

Observability to eliminate data pipeline scaling challenges across Streaming, OLAP and OLTP. Automatic reconciliation between 70+ Live and DR Hadoop Clusters.

Stable architecture with resilient data operations that accelerated migration to cloud while maintaining performance of the existing Hadoop environment.

Monitoring a newly integrated Snowflake environment for cost, usage, and user metrics presented a significant challenge. Detecting anomalies in cost trends, query spillages, and warehouse timeouts could take weeks, leading to substantial Snowflake costs.

ADOC Snowflake Compute effectively reduced costs due to spillages and optimized warehouse processes. ADOC Compute also provided out-of-the-box graphs illustrating Snowflake usage and trends, facilitating a clear understanding of Snowflake spend.

A centralized monitoring platform enables a thorough understanding of all Snowflake expenditures and provides timely alerts regarding critical cost spillages for each department within the organization.

The existing cloud platform experienced a significant increase in monthly expenses, which impacted overall cost management. The team also encountered challenges scaling infrastructure to meet growing business activity and user base without incurring additional costs.

With Cost Observability, cost savings opportunities were identified and implemented. This included right-sizing warehouse recommendations, analyzing queries, grouping queries with estimated costs, and providing intuitive graphical representations of cost metrics.

Significant monthly cost reduction of $10,000 achieved without compromising performance or functionality. Optimized and identified spillage queries, improving 3-4 daily processes. Despite a drastic increase in cloud activity and users, the rate of cost increase has decreased. Two new data sources have been onboarded following effective cost management for the first.

Visibility challenges across cloud and on-premises data made migrations extremely hard to achieve without loss or errors.

Data observability across HDP, CDP, ODP and cloud data environments, with reconciliation and drift checks across all their complex pipelines.

Stable architecture with resilient data operations that accelerated migration to cloud while maintaining performance of the existing Hadoop environment.