Case Study
A Global Semiconductor Enterprise Transforms Storage Operations with Autonomous, AIOps reducing detection & resolution time by 40%
Download Case StudyIndustry: Semiconductor design and manufacturing
Key Technologies and Platforms: NetApp AIOps (Active IQ, Digital Advisor), Jade OpsWatcher, Agentic AI, Hybrid IT Infrastructure
About the Client
A global semiconductor enterprise operating at the forefront of chip design and manufacturing runs a highly data-intensive infrastructure. Its environment supports mission-critical workloads, including electronic design automation (EDA) simulations, chip verification, tape-out, test data processing, and long-term data retention for analytics.
Why was this change needed?
As semiconductor design complexity increased, the customer faced mounting operational challenges across its storage ecosystem. Several converging factors made transformation unavoidable.
- Explosive data growth EDA simulations and design workloads generated massive volumes of data, placing continuous strain on storage performance and capacity planning. Managing AIOps for high-performance workloads became critical as design cycles accelerated.
- Stringent performance and availability SLAs Storage performance issues, even small ones, affected ongoing design work. Delays at this stage had a direct impact on tape-out schedules. In this environment, predictive storage performance management became part of normal operations, not an enhancement.
- Reactive operations model Monitoring was largely alert-driven. Troubleshooting was manual. Issues were typically addressed after impact, which led to avoidable disruptions and longer recovery times. This limited operational efficiency with AIOps, as teams remained focused on response rather than prevention.
Business Challenge
With tight product timelines and revenue directly linked to infrastructure performance, storage availability, predictability, and scalability are non-negotiable. The enterprise operates across a hybrid IT landscape combining high-performance on-premises storage with cloud environments, making operational visibility and control essential. This environment demanded an AIOps grounded in real operational complexity rather than isolated tooling.
- Limited hybrid visibility The lack of unified insight across on-premises and cloud storage environments made it difficult to correlate issues, understand dependencies, and proactively manage risks within a hybrid infrastructure.
- Capacity and cost management challenges Inaccurate forecasting often resulted in emergency capacity procurement, inefficient tiering, and limited cost predictability, preventing a scalable storage operations model.
- Manual processes that did not scale Operational effort increased disproportionately with data growth, placing heavy dependency on storage administrators and increasing operational overhead.
The semiconductor enterprise recognized the need to move beyond conventional monitoring toward an autonomous IT operations strategy, while maintaining governance, accountability, and control.
Business requirements
To address these challenges, the semiconductor enterprise required:
- Visibility across storage, compute, and network layers operating within a hybrid environment, without relying on separate tools or views.
- Early identification of performance issues, failures, and capacity risks, so issues are addressed before they affect active workloads.
- Guidance that can be acted on directly, rather than alerts that still require investigation or interpretation.
- A move toward autonomous operations, introduced carefully and within clearly defined limits.
- Storage decisions reflecting how different workloads are actually used, especially design and simulation workloads.
- Less day-to-day manual effort, without loosening compliance controls or blurring ownership.
Business benefits achieved
- Availability: 28% fewer unplanned storage incidents.
- Performance: EDA and simulation workloads showed reduced variation during operation.
- Operations: Support tickets reduced by 54%. Less manual handling by storage administrators.
- Cost: Capacity usage was more controlled. Growth followed planned thresholds.
- Agility: Faster response as workload demand and data volume increased.
The Solution
Jade Global implemented a storage operations framework that combined observability, AIOps, and agent-based automation. The intent was to improve how operational data was interpreted and acted upon within defined controls.
AIOps was used to support consistent decision-making, not as an end in itself, with emphasis on actions that could be governed and sustained in daily operations.
Key use cases delivered
- Proactive incident prevention Performance anomalies affecting EDA workloads were detected early, with automated or guided remediation executed before user impact.
- Mean Time to Detect reduced by 42%.
- Mean Time to Resolve reduced by 40%
- Capacity and cost optimization Forecasting was used as part of storage growth planning. Tiering for cold and inactive data was automated. As a result, emergency capacity additions were no longer required, and cost tracking became more predictable.
- Intent-driven automation Storage behavior reflected how different workloads were used in practice.
- Design and simulation workloads were handled with performance as the primary consideration.
- Archival and long-term data followed tiering approaches focused on cost.
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