22 November 2024, Fri |
10:08 AM
By Mena Migally, Regional Vice President, META – Riverbed
While digital channels have simplified engagements for customers and employees, they have saddled IT teams with new levels of complexity. Despite their best efforts, IT teams find they have an insufficient understanding of how the network and applications are performing. Without the ability to derive valuable insight from the deluge of data, IT teams find their effectiveness inhibited when asked to innovate, or address issues that arise.
IT teams that actively contribute to driving business results need greater insight from the data they receive so they can do their job well. The solution? Observability, which represents the next phase in the evolution of monitoring and visibility.
Observability gives IT the flexibility to dig into “unknown unknowns” on the fly. It enables access to actionable insights by correlating information across disparate tools and providing appropriate context around why things are happening.
It is important to note that observability is not a replacement for monitoring which has been key to keeping environments running. Rather ‘completes the story’ by augmenting this established practice to provide actionable insight that aids troubleshooting and resolution.
While observability should bring together the benefits of monitoring, visibility, and automation, most observability tools available today have limitations. This is why it is not just observability, but unified observability that organisations need.
Full-fidelity data is captured across the entire IT ecosystem, from client devices, networks, and servers, to applications, cloud-native environments, and the users themselves. This complete picture enables IT to understand what is happening and what has happened while avoiding missing key events due to sampling.
This, coupled with the analysis of actual user experiences, not just sample data, offers organizations a deeper level of insight that augments quantitative measures of user experience with qualitative measures of employee sentiment.
Applying Artificial Intelligent (AI), Machine Learning (ML), and proprietary data science techniques across disparate data streams, including third-party data, can help organisations better detect anomalies and changes. By doing so, it can surface the most important issues faster and with precision.
This is a significant difference from existing observability tools available today because organizations can better understand the impact and severity of issues from the start. This enables better prioritization so they can focus their time and effort on the areas that matter the most.
By leveraging the powerful combination of AI and ML enabled automation, organisations gain context-rich, filtered, and fix-first insights, ready for IT action. These insights enable effective cross-domain collaboration because it offers a single source of truth, allowing for more efficient decision-making to accelerate mean time to resolution.
In fact, this approach also reduces time spent in war rooms, finger-pointing, and excessive escalations. Through open APIs, these actionable insights can be imported from or exported to a broader ecosystem of third-party systems, including ITSM and security tools, to continuously improve digital experiences and IT service quality.
As new paradigms, workforce models, and customer preferences will continue to add to the demands placed on IT teams, complexity can become an ever more significant challenge.
Unifying data, insights, and actions across IT will help organisations eliminate data silos and alert fatigue, improve decision-making, apply expert knowledge broadly, and continuously improve digital service quality. Organisations that invest in this ability will find flawless digital experiences within reach once more.
