Happy New Year everyone! Don’t worry, I’m well aware it’s only August and we still have another four months or so before the “official” new year arrives. But with the winding down of summer and the commencement of the school year, I tend to view September as a type of New Year as well. September has always been my favorite month of the year.
You can’t work in tech and project management without hearing the following buzzwords: Agile, Scrum, Lean, Kanban, and SAFe. Depending on your working experience then reading those words may have given you a warm fuzzy feeling, or you might have the cold fingers of existential dread trying to creep across your brain.
An application running in production is a difficult beast to tame. Most experienced developers–ones who spent enough late nights or Saturday mornings trying to break apart a nasty production bug–will try and create the clearest possible picture for their later selves while writing their code, so that they could understand what’s actually going on in the system during an incident.
If the last year has taught those of us at ScienceLogic anything, it is that we underestimated how much our customers and partners relied on us. It’s understandable, really, since no one could have anticipated the pandemic-driven chaos, and how it would push IT to its limits—and beyond.
In today's increasingly complex work environment, facility managers play an ever-growing role in ensuring that organizations have access to the tools and services that they need to function at their best, especially when it comes to the return to the office. If you've met or spoken to the facility manager at your organization, you may have noticed they're one of the hardest people to get a hold of.
Efficient root cause analysis is vital to incident management. How quickly an issue can be understood determines the mean time to resolve (MTTR), which directly impacts the digital experience. When there is a sudden outage or a performance degradation, root cause analysis can become laborious given the complexity of all the components involved and the potentially huge amount of observability data generated from different sources.