Maximizing SaaS Application Analytics Value With AI
IBM News, Wednesday, June 5th, 2024
Software as a service (SaaS) applications have become a boon for enterprises looking to maximize network agility while minimizing costs. They offer app developers on-demand scalability and faster time-to-benefit for new features and software updates.
SaaS takes advantage of cloud computing infrastructure and economies of scale to provide clients a more streamlined approach to adopting, using and paying for software.
However, SaaS architectures can easily overwhelm DevOps teams with data aggregation, sorting and analysis tasks. Given the volume of SaaS apps on the market (more than 30,000 SaaS developers were operating in 2023) and the volume of data a single app can generate (with each enterprise businesses using roughly 470 SaaS apps), SaaS leaves businesses with loads of structured and unstructured data to parse.
That's why today's application analytics platforms rely on artificial intelligence (AI) and machine learning (ML) technology to sift through big data, provide valuable business insights and deliver superior data observability.