Industry executives and experts share their predictions for 2024. Read them in this 16th annual VMblog.com series exclusive.
Observability and Where It’s Going
By Jeremy Burton, CEO of Observe
Observability, the fusion of log analytics, monitoring, and APM, will continue to grow into a consolidated and expansive market in 2024.
Funding of observability startups will reach record levels with newer players building on modern architectures to handle the cost associated with massive growth in data volumes and C-Level pressure to drive down incident resolution times and improve customer experience.
Read on for three predictions from Jeremy Burton, CEO of Observe, for the growing observability landscape, the role of AI in observability, and why observability has to be reclassified as a data problem.
Observability is recognized as a Data Problem
Despite pouring $17 billion into observability and monitoring tools each year, enterprises are seeing a negligible impact on mean-time-to-resolution (MTTR) – in fact they are increasing. Why? Modern distributed applications are complex, they change multiple times a day which leads to DevOps teams seeing ‘unknown’ problems in production every day.
When troubleshooting an ‘unknown’ problem, DevOps team must triangulate on data points to determine where the problem may be occurring. That’s where the problems start, some data points are in a logging tool,a monitoring tool, or an APM tool.The best practice is often to screenshot what each tool is showing and post in a Slack channel so the final decision maker can correlate.
This is not sustainable. For Observability to deliver on its promise, the observability data must be in one place – not in several siloes. If the data is in one place it’s easier to navigate, find relevant context for the incident being investigated, and for the DevOps team to collaborate in one consistent interface (that’s not Slack!)
LLMs: The Second Coming Of AI In Observability
In 2024, it will become apparent to almost everyone that LLMs / GPT deliver meaningful productivity improvements in the world of observability. From simple help to writing RegEx’s and queries, LLMs and the friendly GPT interface will enable new users to get up to speed faster and resolve incidents faster than ever before.
At the same time, AIOps (the first generation of AI) will continue to fall out of favor as those that implemented it realize that the promised benefits like root cause detection just aren’t there.
Why AIOps Fall Is LLMs Triumph
In 2024, I expect to see more companies reach a breaking point with AIOps and shift their focus towards the potential of LLMs. While AIOps was a laudable concept when introduced, in practice it has failed to live up to its promise. The idea that you could train a model on data emitted by apps, that change every day, is nothing more than a pipe dream.
Large Language Models (LLMs) appear to be a far more promising alternative because they attack the problem differently and help users make more intelligent decisions. Companies are waking up to this fact but many more will begin to act on it in the new year.
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ABOUT THE AUTHOR
Jeremy Burton is the chief executive officer of Observe, Inc. Prior to Observe, Jeremy was Executive Vice President, Marketing & Corporate Development of Dell Technologies, and served in various leadership roles at EMC prior to Dell. A 20-year veteran of the IT industry, Jeremy joined EMC from Serena Software, where he was President and CEO. Previous to Serena, he led Symantec’s $2 billion Enterprise Security product line as Group President of Security and Data Management. Jeremy also served as Veritas’ Executive Vice President of Data Management Group and Chief Marketing Officer. Earlier in his career, he spent nearly a decade at Oracle as Senior Vice President of Product and Services Marketing. Jeremy is currently a member of the board of directors at Snowflake, a seat he’s held since 2015, and maintains a part-time role on the advisory board at McLaren Group.
