Leveraging Telemetry Insights to Improve the Customer Experience

Learn how Dell Technologies is using AI to deliver the best experience for customers.

To ensure smooth operations and maximum productivity, businesses need to get most out their IT infrastructure. Dell’s Optimize for Infrastructure service helps customers achieve this with a designated expert—a technical account manager (TAM)—who is responsible for ensuring customers’ infrastructure is performing optimally.

To help TAMs to monitor and analyze the vast amounts of telemetry data generated by modern complex systems, Dell Technologies developed Optimize Telemetry Insights (OTI). Using OTI, an AI-powered tool, TAMs can monitor performance metrics and other critical telemetry data across all systems and customers under their purview.

The fundamental feature of OTI is an AI model that can detect performance bottlenecks and assist TAMs with root cause analysis. The AI model in OTI uses an STL-based (seasonal-trend decomposition using Loess) anomaly detection algorithm to detect anomalies in performance data. The STL model is well known in time-series analysis that is particularly useful for detecting seasonal trends, long-term trends and other variations in data. Anomalies detected in performance data indicate a potential performance bottleneck for the system, which can lead to downtime and lost productivity.

The AI model not only identifies the anomaly but also identifies the individual systems or storage objects responsible for the performance bottleneck in the cluster. With this, TAMs can both alert the customer when there is a performance bottleneck and pinpoint the exact root cause of the issue to help the customer mitigate it. The AI model is constantly learning from the usage pattern of customers’ infrastructure, which enables it to adapt to the changing needs of their environment and self-calibrate accordingly.

The UI/UX of OTI is designed with the customer in mind. It provides alerts to TAMs when there is an issue with the customer’s system so the TAMs can alert the customer without delay. It also identifies the severity of the anomalies so the TAM and customer can focus on the issue requiring immediate attention first.

“The Optimize Telemetry Insights tool leverages AI/ML capabilities that truly provides a predictive approach within a customer’s environment. This amazing tool allows for preventative care when it comes to performance, alerts, capacity and much more.”  -Testimonial by Technical Account Manager

OTI is also designed to perform anomaly detection over thousands of systems in a matter of minutes. This enables OTI TAMs to cater to all customers and provide fast and in-depth analysis regardless of the size of their operations. OTI delivers two key benefits: reduced downtime for customers and increased customer engagement. By detecting and resolving performance bottlenecks quickly, Dell customers can avoid costly downtime and ensure their systems are performing optimally. This, in turn, leads to increased productivity and profitability. And automating performance bottleneck detection and root cause analysis allows TAMs more time to focus on other critical aspects of their job, such as customer engagement, planning and leveraging other sources of technical data to guide and support customers’ objectives.

At Dell, we are committed to developing new and innovative ways to use AI to improve the customer experience. With Optimize Telemetry Insights, we are taking a big step forward in our mission to provide the best customer experience possible.

You can learn more about Dell Technologies Optimize for Infrastructure offer here.

About the Author: Vadiraj Kulkarni

Vadiraj is senior advisor at Applied Data Science & Engineering group in Dell CSG Services. He develops Artificial Intelligence and Machine Learning solutions with a focus on helping Technical Account Managers, Service Account Managers and Technical Support better serve our customers. At Dell, he has proven track record delivering successful projects using Generative AI technologies such as Large Language Models (LLMs) to create powerful question-answering bots, AI drafts for tech support and sales teams. He has deep understanding in natural language processing, machine learning and deep learning algorithms and is constantly exploring new ways to apply these technologies to real-world business problems. Vadiraj began his career as software engineer at Qualcomm, developing drivers for Qualcomm SOCs. He later worked as Data Scientist at Hitachi India, building AI models for fraud detection. Vadiraj holds a master’s degree in Data Science from Indian Institute of Science, Bangalore and a bachelor’s degree in Electronics and Communication from National Institute of Technology-Karnataka, India.