Predictive analytics for networks are helping businesses optimize their network performance, anticipate network capacity related requirements, and eventually forecast future much more efficiently than ever.
Gartner has estimated the value of network monitoring software market at roughly $2.1 Billion, this figure is presently increasing at a growth rate of 15.9% annually. The demand for predictive analytics in network monitoring software has grown beyond enterprises and datacentres. This technology can help users predict the future network behaviours with surprisingly 95% accuracy! This accuracy makes such network monitoring software’s increasingly viable option thinking from a financial standpoint.
Those businesses who can understand the unique advantages of predictive analytics are advancing with an edge over competition. These businesses are using this technology to revamp the way they’re monitoring the network performance, therefore bringing increased accuracy to their forecasts of network reliability, predicting network issues, and improving the capacity of IT operations and services as user’s demands evolve. A unique combination of network monitoring software and Artificial Intelligence stands to provide a substantial leap ahead.
What is Predictive analytics?
Predictive analytics can be defined as a fundamental paradigm shift for network and IT infrastructure analysis. It makes future predictions which significantly improves decision making process with the use of Machine Learning (ML) and Natural Language Processing (NLP). This technique uses the current available data to make predictions about the future. It involves study of current and historical events. Typically, this data can be used for network capacity planning purposes. This kind of network intelligence plays a key role in IT infrastructure Operations Management.
Benefits of Predictive Analytics
Predictive analytics goes beyond the typical network monitoring paradigm with the use of historical machine data to prepare an actionable dashboard of future predictable network operations. This may help IT departments from all business verticals detect anomalies and prevent future bottlenecks & network congestion. Traditional method is to set threshold alerts to get a timely update. Machine learning can improve network performance & lower down human intervention in a number of ways, including:
- Foresee network capacity requirements: Allow IT administrators to anticipate network capacity requirements and accordingly advance the hardware procurement process.
- Optimizing performance & quality of IT operations: Scan historical IT data and pin-point recurring error rates or any component network failures accordingly take preventive action.
- Improving IT security: Mitigate any cyber security threat by recognizing anomalies in IT data originating from applications, systems, or IP addresses.
- Forecasting network budgetary needs: Map out trends in overall network usage and plot predictions based on the performance to forecast budgetary needs. With capacity planning comes budgetary planning.
Key Use Cases
As mentioned in the above section, the use of predictive analytics for networks offers enterprises a smart way to proactively mitigate network bottlenecks, prevent outages, and other major issues before they happen. Let us go through some of the key use cases of predictive analytics:
- Resolution of delivery delays and troubleshooting storage redundancies in case of datacentres’.
- Introducing continuous learning capabilities also known as cognitive learning to network monitoring software.
- Save time by being independent of human inputs as machine does most of the work.
- Determine and optimize network performance by predicting network capacity related problems accurately
- Study trends in network traffic patterns, considering usage type and also get alarms and warnings on time
- Get clues on any cyber security threat which generally escape human observers but can’t escape machine driven intelligence.
Motadata goes Beyond: from Proactive to Predictive
As 2019 comes to an end, we look back at the range of features we introduced in Motadata based on customer & partner feedback. New Year calls for new developments that’s why we’re going to introduce the most desirable feature, predictive analytics in 2020. Yes! You read it right! We’re coming up with the first ever Network Monitoring Software with Predictive Analytics powered by Artificial Intelligence and Machine Learning. This module also has base lining, APM and a lot more! Watch out for this space for the latest updates. If you’d like to pre-book a demo with us then drop us an email on firstname.lastname@example.org.