Learning from the Past for Better Data-Driven Decisions

By Joe Kim, SolarWinds EVP, Engineering and Global CTO

 

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. Things were difficult before the IoT, connected devices, and hybrid cloud environments became commonplace; today, it’s nearly impossible.

 

Data lives across numerous departmental silos, 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 have a cross-stack view of their IT data so they can compare disparate metrics and events across hybrid cloud 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. The system derives this intelligence based on past experiences and known performance issues, and can apply that knowledge to the present situation so that network slowdowns or downtime can be proactively prevented.

 

Learning from the Past

 

Administrators can take things a step further and incorporate prescriptive analytics and machine learning into their data analysis mix. Prescriptive analytics and machine learning actually provide recommendations to prevent problems, like potential viruses or malware. This 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.

 

Find the full article on Government Computer News.

Source: solarwinds GEEK SPEAK

0

Like This