Datasheet: Nastel AutoPilot for Analytics

Accelerate Decisions, Satisfy Customers, Innovate Continuously

About Nastel AutoPilot for Analytics

Every IT organization and line of business needs to use data to learn and improve the operation of the company by identifying signals that provide new insights and doing this faster is now a business fundamental. Being able to sense problem conditions before they actually affect users is the formula for increasing the user experience and providing a critical differentiator to the corporation. But continually growing volumes of business data and the volume of new streams of event data can overwhelm traditional analytic methods, that can’t scale to modern volumes and frequency’s. Nastel takes a fresh approach to this challenge, combining predictive anomaly detection with machine learning, which allows for improvement over time and addresses the broadest range of “novel” situations.

Features and Benefits:

Feature Benefit Description
Smart Analytics Machine learning improves performance over time. AutoPilot rapidly improves its ability to predict, sense, and evaluate performance problems as they emerge. Leverage AutoPilot’s ready-to-use analytics without the burden of constantly writing processing rules to make sense of data.
Intuitive Insights Faster time to decision. Business analysts are armed with advanced tools to better understand the behavior of users and immediately understand what is normal or expected behavior, and what is not.
360° Situational Awareness Deeper understanding. Real-time Complex Event Processing collects and correlates metrics and events from all infrastructure systems and external sources for a complete informational picture.




Predictive Anomaly Detection and Machine Learning

The task of detecting problems and combining emerging trends and subtle behavior patterns into a clear picture of how IT operations are affecting business outcomes is made much easier and faster with AutoPilot’s real-time “smart” anomaly detection analytics. IT professionals and business stakeholders can now make more informed and rapid decisions based on business insights formerly hidden in a multitude of high-volume data sources. This major advance in anomaly detection and machine learning is based on extensive enhancements and extensions to the open source code contribution by Netflix in their Robust Anomaly Detection (RAD) project. Utilizing machine learning algorithms, AutoPilot rapidly improves over time at predicting, sensing, and evaluating the exact nature of performance issues. Potential use cases include almost any industry vertical where subtle data outliers demand fast reactions and decisions. Some examples include actual or potential financial system security breaches, non-compliance, supply chain issues that potentially cascade rapidly into major delivery problems affecting customers… the list is almost endless.

To answer business-centric questions and provide guidance for decision-makers, Nastel combines:

Advanced predictive anomaly detection and machine learning algorithms, raw information handling and analytics speed, end-to-end business transaction tracking that spans technologies, tiers, and organizations and Intuitive, easy-to-use data visualizations and dashboards fuse seamlessly across dynamic IT environments, from mobile to mainframe. providing the broad array of analytic and decision-support capabilities needed by developers, IT admins, and business analysts to satisfy real-time operations intelligence and APM needs.


A Fresh Approach to Analytics

Although many IT organizations field basic analytic tools sufficient to keep MTTR to an acceptable level, they need more sophisticated capabilities to answer questions like: “How does the performance of IT activities and operations impact our business?” And, “Is there a way to understand these dynamic interplays in real-time to optimize intelligent day-to-day management of the business?”

Platform Description:

Cloud and On-premise

Supported Platforms:

Windows, Linux, i-Series and Mainframe

Deep examination of transaction message payloads enables linkage of IT activities and behavior to expected and actual business outcomes.