Predictive storage analytics tools are becoming standard equipment in the enterprise. Get to know the features you’ll need, how they work and the benefits they provide.

5 predictive analytics features you'll want to watch out for

Predictive Analytics – We’re all familiar with the power, convenience and borderline creepy accuracy of predictive data analytics. Whether it’s looking for something new to watch on Netflix, browsing items related to an item bought last week on Amazon or cursing our phone’s autocomplete misfire, predictive analytics is increasingly used to automate routine tasks, filter information, make better decisions and improve customer support.

These same features have come to the data center under the rubric of AIOps, a new genre of infrastructure management features with significant implications for storage.

Most early uses of predictive analytics centered on consumer applications where massive amounts of data could be collected from millions of users, making it easier to justify the large investment in data acquisition, processing and model development. The predictive analytics ROI hurdle is high because it’s a sophisticated undertaking. It goes far beyond traditional statistical and probabilistic techniques, using machine learning and, in some cases, neural network-based deep learning to train models and make predictions based on massive data sets. The data-driven approach is perfect for automating IT systems management given the massive size of event logs, systems telemetry and performance metrics today’s infrastructure spews out.

This article originally appeared on techtarget.com To read the full article, click here.

Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards

If you would like to learn more, click here.