Learning from the Past: The Key to Making Better Data-Driven Decisions

With the advent of the Internet of Things (IoT) and connected devices, the amount of data agencies collect continues to grow, as do the challenges associated with managing that data. Handling these big data challenges will require federal IT pros to use new data mining methodologies that are ideal for hybrid cloud environments. These methodologies can improve network efficiency through automated and intelligent decision-making that’s driven by predictive analytics.

Today’s environments require a break from the data analysis methods of the past, which were time-consuming and required an enormous amount of manual labor. Traditionally, data analysis has required massive investments in computational power and teams of data scientists working around the clock to parse the meaning behind the information. That was difficult before the IoT, connected devices, and hybrid cloud environments became commonplace; today, it’s nearly impossible.

Data lives across numerous departmental silos—and not to mention multiple IT environments—making it hard for IT departments to keep track of it all. It’s difficult to achieve clear insights into these types of environments using traditional data mining approaches, and even more difficult to take those insights and use them to ensure consistent and flawless network performance.

Agencies need tools that make it easier for federal IT pros to monitor and analyze data that lives both on-premises and across several clouds. Having this cross-stack view of IT data can help agencies compare disparate metrics and events across hybrid infrastructure, identify patterns and the root cause of problems, and analyze historical data to help pinpoint the cause of system behavior.

Predicting the Future

Automated data mining paired with predictive analytics addresses both the need to identify useful data patterns and use that analysis to predict—and prevent—possible network issues. By using predictive analytics, administrators can automatically analyze and act on historical trends in order to predict future states of systems. Past performance issues can be evaluated in conjunction with current environments, enabling networks to “learn” from previous incidents and avert future issues.

With predictive analysis, administrators can be quickly alerted about potential problems so they can address issues before they occur. An administrator might receive an alert regarding their disk space running out, or that a patch will fail upon installation. The system derives this intelligence based on past experiences and known performance issues, and can apply that knowledge to the administrator’s present situation so that network slowdowns or downtime can be proactively prevented. By comparing both historical and recent data, predictive analytics can help IT pros make informed predictions about the future.

Learning from the Past

Administrators can take things a step further and incorporate prescriptive analytics and machine learning into their data analysis mix. While predictive analytics is essential for providing insights into opportunities and highlighting potential risks, prescriptive analytics and machine learning actually provide recommendations to prevent problems, like potential viruses or malware, before they occur across the IT environment. Prescriptive analytics can help agencies overcome threats and react to suspicious behavior by establishing what “normal” network activity looks like.

Using new, modern approaches to data analysis can help agencies make sense of their data and keep their networks running at the utmost efficiency. Predictive and prescriptive analysis, along with machine learning, can help keep networks running smoothly and prevent potential issues before they occur. Each of these approaches will prove invaluable as agencies’ data needs continue to grow.

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