Software as a service (SaaS) applications have revolutionized how enterprises approach network agility and cost efficiency. These applications offer developers instant scalability and quicker access to new features and updates, all while leveraging cloud computing infrastructure and economies of scale.
However, SaaS architectures can overwhelm DevOps teams with large amounts of data to analyze. With over 30,000 SaaS developers in 2023 and each enterprise using about 470 SaaS apps, businesses are left with structured and unstructured data that needs parsing.
Today, application analytics platforms rely on artificial intelligence (AI) and machine learning (ML) to sift through big data, provide valuable insights, and enhance data observability.
What are application analytics?
Application analytics involve collecting real-time data from SaaS, mobile, desktop, and web applications to analyze performance and usage. These analytics cover app usage, performance, and cost/revenue.
Using advanced data visualization tools powered by AI, businesses gain a deeper understanding of IT operations, enabling smarter and faster decision-making.
AI in SaaS analytics
AI is a key component in modern SaaS analytics solutions. It helps turn data generated by SaaS apps into actionable insights, allowing for predictive analysis and automated data processing.
SaaS app analytics use cases
Traditional data analysis methods often fall short when handling the vast amounts of data produced by SaaS apps. AI and ML technologies provide more nuanced observability and effective decision automation, enhancing data insights, predictive analytics, personalization, conversion rate optimization, marketing, and pricing optimization.
Maximize the value of SaaS analytics data with IBM Instana Observability
IBM Instana offers a real-time, full-stack observability solution powered by AI. It goes beyond traditional app performance management to provide automated, democratized observability accessible to various teams within an organization. Instana empowers businesses to take intelligent action and maximize the potential of SaaS app analytics.
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