The process of rapidly collecting data from IT systems, including the integration of data from various monitoring tools in use, has become important. Magic is in algorithms that can create actionable intelligence.
Fremont, CA:Predictive analytics technologies have become vital to competition in manufacturing, e-commerce and horizontal use cases, such as cybersecurity infringement prevention and revenue forecasting. The use of data to predict and prevent IT outages and issues is also a growing practice—especially as advances in software monitoring have made it easier to provide analytics in a timely manner.
The process of rapidly collecting data from IT systems, including the integration of data from various monitoring tools in use, has become important. Magic is in algorithms that can create actionable intelligence. IT predictive analysis, once known as IT operations analytics (ITOA), is still developing in many organizations, but is much more streamlined than it used to be when it comes to exporting data sets to advanced analytical tools such as Tableau or Microsoft PowerBI. Data analysts and data scientists were expected to work with these advanced tools and deliver valuable predictions—a task that could take weeks to complete.
Advances in predictive analytics technology, supported by machine learning and AI, are creating more proactive workflows for IT operations teams. For instance:
• Use of storage: It's not a happy moment for IT to find out that the expensive storage collection is down. It's not enough to note that storage volumes have steadily risen out of the normal pattern. By knowing in advance that the pattern is going out of the acceptable range, IT will investigate the trigger and correct it before any harm is done. With cloud storage, risks include sluggish connectivity where network bandwidth is insufficient or security breaches where the cloud provider's security mechanisms are inadequate for your use. Predictive analytics will alert you to these choices in time to take corrective action.
• Comparison of cloud-to-cloud results: The ability to establish connections and show comparisons between different data points is an important feature of predictive analysis. IT leaders are interested in learning about application efficiency across the IT climate, which may include analyzing historical output across several clouds to anticipate and proactively mitigate vulnerabilities in one service over the other. This use of predictive analysis can also be useful in helping companies choose one cloud provider over another for specific applications and workloads.