Harnessing the Power of Real-Time Data Analytics
Information is power and for many organizations, that power lies in the insights that data can offer about customer behaviors. Without context, however, that power isn’t worth much. Real-time Network Performance Analytics can provide the context that organizations need regarding the data from their monitoring solution, but many organizations don’t know where to begin. There are 4 types of Network Performance Management Analytics that help determine the cause and effect of data outcomes. What are they?
- Descriptive: Answers the question, “What happened?”
- Diagnostic: Answers the question, “Why did this happen?”
- Predictive: Answers the question, “What will happen?”
- Prescriptive: Answers the question, “What do we do?”
Data never lies, but making sense of that data is the challenge. Using these real-time analysis methods to provide context for collected data can help your organization make better decisions. Pairing monitoring solutions with data analytics is essential, as there is simply too much data and too little time to manually drill down. This combination helps identify key areas that need attention, and has a positive effect on business outcomes. When Engineering and DevOps teams are able to use real-time data analytics to see exactly what went wrong and why, they can become more proactive and less reactive.
The ultimate goal for organizations employing data analytics is to gain enough confidence in the analyses to automate tasks. In order to gain confidence in the quality of the data analysis, organizations will need to make use of each of the 4 types of Network Performance Management Analytics.
For example, an organization may want to analyze the capacity of a certain application. Monitoring the capacity data in real-time using a performance management platform will describe what is happening. If there is a sudden change in capacity, the organization will usually receive a descriptive alert. Pulling a report on the capacity of the system will help organizations diagnose why there was a sudden change in capacity, and predict what, if anything will happen because of it. If they see a large spike in the number of users at a certain time, they can investigate if there was any targeted outreach that may have driven users to the application at that time. Once the questions of what happened, why it happened, and what will happen as a result are answered, organizations can prescribe automated systems to address this type of capacity spike in the future.
The value of real-time Network Performance Management Analytics will only increase over time as it provides meaningful insights about customer behaviors and customer affecting events. According to Gartner, more than half of large organizations globally will compete using advanced analytics by 2018. Organizations can use data from their monitoring solutions and real-time data analytics to describe, diagnose, predict, and prescribe customer behaviors to stay ahead of the trends and improve customer loyalty by adapting to customer needs.
Is your organization prepared for the prominent role real-time data analytics will play in digital transformation?