As time progresses and competition grows, being “good enough” means that you may be falling behind. Engineers will discover new ways to solve problems, which will enable rapid increases in availability and scalability. With these increases comes more complexity and the generation of more data. Rather than just monitoring the new data and letting the old data sit there collecting dust, you should consider using it to gain maximum insights into your environment.

Observability has gained a lot of momentum in the past year, be it full stack observability or data observability. Modern complex IT systems using clouds, microservices and serverless are easy to develop and deploy but extremely difficult to observe. These systems generate tremendous amounts of data and need an automated way of handling the volume. The next era of delivering customer experience is underpinned by the full stack observability capability.

The following first appeared on VentureBeat. Despite the economic uncertainty due to the global pandemic, worldwide technology spending in 2021 increased by nearly 9% to $4.2 trillion. The U.S. economy grew sharply across the past three quarters of 2021, leading to stronger consumer spending, higher price inflation, and persistent employee shortages amid the great resignation.

You’ve probably seen the term AIOps appear as the subject of an article or talk recently, and there’s a reason. AIOps is merging DevOps principles with Artificial Intelligence, Big Data, and Machine Learning. It provides visibility into performance and system data on a massive scale, automating IT operations through multi-layered platforms while delivering real-time analytics.

Unlike traditional IT Ops, the role of the SRE isn’t simply focused on finding and solving technical problems. The big win for today’s SREs is supporting the organization’s strategic innovation initiatives. With the appropriate observability capabilities, it’s possible to quantify the value that software infrastructure contributes to this innovation effort.

By now, you’ve likely heard of AIOps, a technique that promises to inject new levels of efficiency into IT operations with the help of AI and machine learning. But what, exactly, does AIOps mean in practice? Which specific use cases can IT organizations enable or improve with the help of AIOps? Those may be more difficult questions to answer if you have yet to see AIOps at work in your organization.

When the U.S. Congress passed the Modernizing Government Technology Act (MGT) of 2017 as part of the 2018 National Defense Authorization Act, it established both funding and a process intended to help bring aging federal IT systems and infrastructure up-to-date with state-of-the-art technologies common in the private sector. According to the legislation, the goals of MGT are to.

As today’s IT infrastructure becomes more complex, a continued reliance on legacy systems can become costly as IT operations get bogged down in inefficiencies and hampered by unreliability.