A remote working world has driven a rapid pace of digital transformation among businesses, moving to the cloud and rapidly adopting data-driven technologies. As a result, the complexity of adapting to this scale of digital transformation has also increased and organizations and their customers are increasingly aware and informed. It is therefore evident that observability in the end is a promising bet and is intended to enable organizations to deliver results. Simply put, observability is essentially the ability of an organization to understand what is going on inside its system from knowledge of its external outputs.
Observability as a key business skill
Every business is now a digital organization and observability is seen as a key skill, not just a cutting edge differentiator. Established leaders with observability best practices build on traditional monitoring and extend it to multi-cloud environments. Research finds these leaders are almost twice as likely to have better visibility into public cloud infrastructure. Even startups, especially cloud-native startups, have had observability practices in mind from day one. For established organizations, on the other hand, this is a big transition: from legacy infrastructures to hybrid multi-cloud environments, from cascading processes to agile processes and into DevOps.
Create a connected infrastructure with observability tools
IT and DevOps teams in all organizations until now have relied on tools that monitor and manage multiple applications and have a seemingly disconnected infrastructure. In addition, the teams operate in silos on two or three different sets of platforms. The need of the hour is to have an integrated infrastructure that provides a unified interface with metrics, traces and logs – all data collected in real time, without sampling and at any scale. The industry offers solutions that give business leaders unprecedented insight and analysis of their data. These solutions have the ability to closely observe workflows and operations on the growing range of machines involved in today’s IT infrastructures. Proper analysis of this data allows companies to draw conclusions and take action quickly, which is essential to ensure customer satisfaction. It is therefore essential for organizations to invest in solutions that help them overcome these complexities and accelerate cloud transformation.
Achieve scale and speed with AI / ML and data analytics
Machine learning (ML) fuels better automation, triggering actions faster than human intervention can handle. On the other hand, artificial intelligence (AI) is one of the main drivers of SaaS companies around the world. Over the past few years, it has been used to generate natural conversations and engagement with customers in order to create and reward loyalty and build advocates, all at the same time. SaaS products are observed to add AI-based models to achieve more personalized experiences. The application of predictive analytics also stands out for data mining purposes, using big historical data to build a focused approach and increase sales. In addition, the use of conversational AI is also on the rise, as it allows companies to create and manage agents themselves with zero-code and drag-and-drop interfaces without the need for additional support.
Consequences of observability gaps
So far, we have established how observability practices drive powerful results enabling organizations to have faster digital transformation and achieve better results with increased customer satisfaction. Now we need to understand the importance of observability gaps and how this can have real consequences such as loss of revenue, reputation and customers. Almost half of business leaders believe that gaps in observability can lead to lower customer satisfaction. The main reason would be the inability of organizations to quickly correlate data from multiple sources. Other challenges include the complexity of the infrastructure, people / culture and management support. Organizations need to understand that success in observability is nurtured over time, not at the end. Data collection and correlation should be a priority. This is no longer optional in a real-time economy where multi-cloud complexity has become the norm.
In conclusion, having a solid observability practice means fewer downtime, better customer experiences, and more successful digital transformations, which directly impact business performance and revenue. The rapid move to the cloud will bring IT and DevOps teams to operational complexities if the gaps in observability are not addressed. The demand for observability solutions is expected to increase as businesses continue to realize the power of data-driven insights and automated actions.
The opinions expressed above are those of the author.
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