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Gaurav Mohan , VP Sales , SAARC and Middle East ,
NETSCOUT
Data management necessary for successful AIOps
In today ’ s dynamic network operations landscape , optimising application and service management is essential for enhancing customer experiences . Key questions arise : how can we fine-tune applications to better serve customers ? How does improved visibility into performance enhance satisfaction ?
Can anomaly detection and automated pattern recognition elevate the quality of experience ? And how can software-driven networks support new services and revenue opportunities ?
AIOps is emerging as a vital tool in addressing these challenges . It holds transformative potential across various sectors such as IT , enterprise , cable , security , and wireless . As service providers increasingly adopt automation technologies to reduce costs and boost productivity , AIOps offers significant advantages by tackling operational challenges and uncovering new data utilisation opportunities .
AIOps is not just a trend and includes a range of applications that enhance network operations . These include improving efficiency across network management layers , enhancing customer experience , gaining insights into user behaviour , and optimising network resources for various applications . Additionally , AIOps aids in identifying traffic patterns and anomalies , supporting data-driven decision-making .
Effectiveness of AIOps depends heavily on the quality of the data it processes . Indeed , the true value of AIOps lies in its ability to utilise high-quality data . In the context of 5G networks , it is crucial to refine and contextualise data to make it useful .
A common misconception is that AIOps can compensate for poor data quality . It is essential to focus on relevant data and understand its application to specific use cases . This approach prevents data overload and ensures that AIOps systems remain efficient , focusing on the critical information needed to resolve specific issues .
Implementing AIOps should be a gradual process . Starting with basic steps and progressing to more advanced stages that allow organisations to tailor AI algorithms to their needs and network conditions over time .
Effective data management is crucial for the success of AIOps . It is important to focus on curating and refining data to ensure that AIOps systems operate efficiently . This strategy helps avoid data bloat and ensures that only relevant data is used for timely decision-making .
While AIOps represents a significant advancement in network management , its effectiveness is linked to the quality of data . By prioritising relevant data and adopting a phased implementation approach , organisations can enhance operational efficiency and discover new opportunities .
Deep observability , network-derived intelligence delivered to cloud , security , and observability tools , addresses this challenge by offering a comprehensive view of data as it moves through different systems . Traditional security tools often provide limited visibility , capturing isolated performance metrics .
In contrast , deep observability goes beyond the limitations of metrics , events , logs , and traces , which is the basis for traditional cloud tools . It ensures end-toend monitoring , allowing IT teams to track the movement of data across hybrid cloud environments . This level of visibility is essential to securing the entire infrastructure and detecting threats before they cause harm .
Deep observability is not a siloed solution . In fact , it complements an organisation ’ s existing log-based security and observability tools with actionable network-derived intelligence and insights .
That is why it provides organisations with the complete picture , enabling them to detect previously unseen threats , accelerate root-cause analysis of performance bottlenecks , and lower the operational overhead associated with securing and managing hybrid and multi-cloud IT infrastructures .
Here are three critical ways it is transforming cybersecurity :
Proactive threat mitigation
Deep observability can help organisations reduce incident response times by identifying and neutralising
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