Teams are locked into a cycle of suffering characterized by the feeling that they are sprinting just to stay still. This morale and productivity-destroying state is caused by an inability to find time to save time. Our new research, The State of Availability Report 2022, discovered that teams know what they want to do—harness cloud and DevOps practices and tools to advance digital transformation—but something’s getting in the way.
Today’s customers see availability as a given. What do they really want? Bigger, better technology with new features and faster platforms. But, according to our recently released Moogsoft State of Availability Report, teams burn their time, money and energy on incident management. In fact, engineers overwhelmingly report that incident management takes up most of their time.
Business transaction insights now on AppDynamics Cloud, delivers visibility with context through AIOps-driven alerts that help identify, prioritize and resolve the most business-critical issues first.
With advances in the field of IT, the amount of data needed to manage IT Operations has grown. In particular with more complex environments, such as the SaaS world, the amounts of data and raw data needed to manage operations have grown exponentially. Managing data manually has become a waste of professionals' skill sets, which could be better used in analyzing and applying the conclusions drawn from the raw data, and not dealing with basic issues that may arise.
Automation is a smart investment in efficiency, productivity, and profitability. According to VentureBeat, companies that invested in automation technologies began to see results almost immediately, including an average 7% increase in revenues. In total, U.S. companies that adopted automation in 2021 generated an extra $195 billion in revenue per month, adding 7.1 million jobs to the economy.
Moogsoft pioneered AIOps, essentially inventing the market 10 years ago. It is worthwhile revisiting why we did that to understand where we are going. My background is as the founder and inventor of Micromuse Netcool, and the RiverSoft’s OpenRiver technology.
Most engineering teams are no strangers to key performance indicators (KPIs), those metrics tracking progress toward critical goals and targets. Ideally, tech leaders design KPIs to focus teams on what matters and prove their contribution to the company’s overall performance. Of course, KPI data should also uncover critical information that guides informed decision-making. For engineering teams tasked with managing the customer experience, KPIs often track availability.
Engineering teams use a lot of single-domain monitoring tools. In fact, the average team manages and maintains 16 monitoring tools — and up to 40 — according to Moogsoft’s State of Availability Report. While IT leaders select and implement these tools to save teams time, our research finds they do quite the opposite. Engineers spend far and away more time on monitoring than they do on any other task — innovative, value-creating tasks included.
IT operations move fast. If you’re an ITOps leader, you need to be moving just as fast to make sure your team has what it needs. Positioning your team for success isn’t easy: complexity in IT is increasing every year and can reach a point where it exceeds a person’s capacity to keep pace. In the face of massive growth, ITOps teams can face major challenges with productivity, burnout and efficiency.
The volume of data that IT systems generate nowadays is overwhelming, and without intelligent monitoring and analysis tools, it can result in missed opportunities, alerts, and expensive downtime. However, with the advent of Machine Learning and Big Data, a new category of IT operations tool has emerged called AIOps. AIOps can be defined as the practical application of Artificial Intelligence to augment, support, and automate IT processes.
If you’re in IT, no doubt you’ve heard the age-old statistic that an average minute of downtime costs $5,600. It turns out that information is a bit outdated and does not reflect the real and nuanced costs of a modern IT outage. BigPanda suspected this and wanted to uncover the true numbers behind outage costs so ITOps can have a better understanding of costs, causes and “cures” of an IT outage.
As the rapid digital transformation has put a lot of pressure on IT organizations to be more proactive and agile, DevOps principles and practices have been an invaluable resource. However, to remain at the top of the game, organizations need an even stronger solution. So, what’s the answer? AIOPs (artificial intelligence for IT operations), of course!
Artificial intelligence for IT operations (AIOps) is an umbrella term for the use of big data analytics, machine learning (ML) and other artificial intelligence (AI) technologies to automate the identification and resolution of common IT issues. The systems, services and applications in a large enterprise produce immense volumes of log and performance data. AIOps uses this data to monitor assets and gain visibility into dependencies within and outside of IT systems.
Business organizations that want to save money and be competitive take into consideration the time costs associated with investments in new technologies. Will the efficiency gains translate to a rapid return on investment? Will users embrace the change and be more productive? Or will those investments be a hassle to employees and result in time-wasting workarounds and a fallback to inefficient, manual processes?
Across all industries, businesses are investing in applications and services powered by artificial intelligence (AI) and machine learning (ML) to boost productivity and gain a competitive advantage.
