What does modern information technology have in common with professional basketball superstar Kevin Durant? Both are achieving new levels of performance and efficiency through the use of predictive analytics.
Predictive analytics is an area of statistics that uses artificial intelligence (AI), machine learning, data mining and predictive modeling to gather and assess historical data in order to predict future events. Businesses are increasingly using predictive analytics tools to evaluate large amounts of qualitative data in search of patterns and insights that can be used to guide corporate decisions and policies.
Early in his career, Durant began working with math whiz Justin Zormelo, who had devised a statistical model for evaluating player efficiency based on film analysis. Over a two-year period, Durant tailored his game based on Zormelo’s analysis and became a league scoring champion and most valuable player.
In a similar way, Hewlett Packard Enterprise (HPE) is upping its game with predictive analytics.
HPE’s InfoSight predictive analytics platform analyzes and correlates data from thousands of data center sensors and analyzes that data to predict and prevent infrastructure problems before they happen. Industry experts say the InfoSight platform is paving the way for a fully autonomous data center.
Giving the increasingly important role of software in today’s business operations, organizations cannot afford any disruptions or delays to their applications. But the complexity of infrastructure causes an “App-Data Gap” that delays delivery of data to applications, impacts business processes and wastes time.
By predicting and preventing problems before they can affect your business, HPE InfoSight minimizes disruptions and dramatically reduces the need for human intervention. HPE says InfoSight reduces the time spent troubleshooting issues by up to 85 percent while delivering greater than 99.9999 percent of guaranteed availability.
InfoSight has actually been running for almost 10 years, accumulating billions of data points from more than 10,000 deployed Nimble storage arrays. Although the sensors are in the storage devices themselves, they also collect network, compute and hypervisor data. The data has been transferred to a cloud platform for analysis and aggregation, with machine learning techniques used to identify anomalies that require corrective action.
Now, HPE has added an AI-based recommendation engine to the InfoSight platform for 3PAR flash storage as part of its strategy to extend machine learning across the HPE storage and server portfolio. HPE says this will give IT the ability to resolve performance problems and pinpoint the root cause of issues between the storage and host virtual machines (VMs). It also provides visibility to locate so-called “noisy neighbor” VMs that monopolize bandwidth, disk I/O, CPU and other resources and can negatively affect the performance of other VMs and applications.
Data centers have customarily been built on a box-by-box basis, with devices added as needed, configured independently and managed manually. Years of continually adding servers, storage devices and networking gear to meet evolving business needs has resulted in IT infrastructures so large and complex as to be nearly unmanageable.
With HPE InfoSight, organizations now can realistically move toward an autonomous data center that virtually runs itself — automatically adjusting compute, networking and storage resources to address changing demands of essential applications and services. For the modern CIO, that sounds like a slam dunk.