Gartner has mentioned Observability is a key trend in the IT market, as it enables businesses to gain real-time visibility into the performance and behaviour of their systems and applications. As the demand for digital services and applications continues to grow, the need for effective observability solutions is becoming increasingly important.
In this article, we will take a look at the key factors driving its growth and the trend we see shaping IT leaders’ agendas for innovation in 2023.
The key driver of the observability market is the increasing complexity of modern systems and applications. With the adoption of cloud computing, microservices, and containerization, systems are becoming more distributed and harder to monitor and troubleshoot. This is leading to a growing demand for solutions that can provide real-time visibility into the performance and behaviour of these systems.
Secondly, the need to improve the customer experience is another driver. As businesses move to digital channels, it is becoming increasingly important to be able to monitor and troubleshoot issues in real time in order to minimize downtime and ensure a smooth customer experience.
The observability market is expected to grow at a CAGR of over 20% during the forecast period from 2020 to 2023. This growth can be attributed to the increasing adoption of DevOps practices, the growing need for monitoring in cloud-based environments, and the increasing demand for real-time visibility in complex systems.
There are several trends that are expected to shape the observability market in 2023: Here’s what we see shaping observability in 2023
- Automation and AI-based solutions: As the volume of data and the number of systems to monitor increases. Businesses face economic challenges and are aiming to do more with less, automation will play a crucial role in 2023. Automation is enabling more companies to redirect skilled resources towards tasks that have the most impact, allowing teams to speed up digital transformation and innovation efforts instead of scaling back. However, the potential for bias in Artificial Intelligence (AI) will pose a barrier to widespread automation across operations, IT, development, and security. Organizations need trustworthy AI to avoid confusion between symptoms and the root cause of problems, or implementing the wrong solutions. Without trust, manual validation of AI-powered solutions will continue, negating efficiency gains and hindering the automation of business, development, security, and operations processes. As a result, trustworthiness will become a crucial requirement for any AI solution, as it must provide precise and explainable answers rather than statistical guesses. companies are looking for solutions that can automate the process of identifying and resolving issues. AI-based solutions that can automatically detect and diagnose performance issues, and provide proactive recommendations for optimization will become more prevalent.
- End-to-end observability: actionable insight: Companies are looking for solutions that can provide a comprehensive view of the entire system, covering all aspects of monitoring, tracing, and logging. With the increasing adoption of cloud-based infrastructure, there will be a growing demand for solutions that can provide observability in cloud-native environments. This includes monitoring and troubleshooting cloud-based services, such as Kubernetes and serverless functions. Most businesses adopt multi-cloud and hybrid-cloud strategies, and solutions that can monitor and troubleshoot across multiple cloud environments will become more important. Also, open-source observability will become more in demand as companies adopt open-source technologies, and there will be an increase in demand for open-source observability solutions that can integrate with existing infrastructure.
- FinOps: cost as a golden signal: As organizations strive to cut costs in the current economic climate, it’s becoming increasingly important to monitor and optimize the cost per transaction. However, many decisions are currently based on traditional metrics such as latency, throughput, and errors, and lack consideration of cost. To address this, FinOps departments are being established to oversee budget consumption, but true cost optimization can only be achieved by involving engineers in the process and sharing cost data with them. By making cost a “golden signal” and considering it alongside other metrics, engineers can make more informed decisions about what to work on and how to deploy code. Additionally, cost data needs to be analyzed in the context of the business function it supports to fully understand its significance. By utilizing data-driven spending decisions and utilizing tools like sensitivity analysis, organizations like Australia Post have been able to maximize business value and gain better visibility into resource usage.
In summary, the observability market is expected to evolve towards automation, full lifecycle end-end observability solutions with integration towards open-source technologies, and multi-cloud and hybrid-cloud monitoring. An organization taking control of its cost(FinOps).