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What is security analytics?

Dynatrace

As a result, organizations are implementing security analytics to manage risk and improve DevSecOps efficiency. Fortunately, CISOs can use security analytics to improve visibility of complex environments and enable proactive protection. What is security analytics? Why is security analytics important? Here’s how.

Analytics 161
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Effective Log Data Analysis With Amazon CloudWatch: Harnessing Machine Learning

DZone

Efficient log management strategies, such as implementing structured logging, using log aggregation tools, and applying machine learning for log analysis, are crucial for handling this data effectively. It offers a faster, more insightful, and automated log data analysis.

Analytics 279
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Five best practices to get the most out of customer experience analytics

Dynatrace

What is customer experience analytics: Fostering data-driven decision making In today’s customer-centric business landscape, understanding customer behavior and preferences is crucial for success. Use advanced analytics techniques Customer experience analytics goes beyond basic reporting. surveys and reviews).

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The state of observability in 2024: Accelerating transformation with AI, analytics, and automation

Dynatrace

The latest Dynatrace report, “ The state of observability 2024: Overcoming complexity through AI-driven analytics and automation ,” explores these challenges and highlights how IT, business, and security teams can overcome them with a mature AI, analytics, and automation strategy.

Analytics 191
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Dynatrace unveils Security Analytics to elevate threat detection, forensics, and incident response

Dynatrace

A traditional log-based SIEM approach to security analytics may have served organizations well in simpler on-premises environments. Security Analytics and automation deal with unknown-unknowns With Security Analytics, analysts can explore the unknown-unknowns, facilitating queries manually in an ad hoc way, or continuously using automation.

Analytics 219
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Dynatrace Perform 2024 Guide: Deriving business value from AI data analysis

Dynatrace

AI data analysis can help development teams release software faster and at higher quality. AI observability and data observability The importance of effective AI data analysis to organizational success places a burden on leaders to better ensure that the data on which algorithms are based is accurate, timely, and unbiased.

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Extract metrics from business events to increase the value of business analytics

Dynatrace

Technology and business leaders express increasing interest in integrating business data into their IT observability strategies, citing the value of effective collaboration between business and IT. Observability fault lines The monitoring of complex and dynamic IT systems includes real-time analysis of baselines, trends, and anomalies.

Analytics 202