Post by account_disabled on Feb 17, 2024 6:25:34 GMT -5
Data observability will become mandatory as organizations look to drive smarter automation and faster decision-making. The volume of data continues to double every two years, and organizations are looking to accelerate its ingestion and analysis on a larger scale. However, the cost and risk of low-quality data is greater than ever. In a recent survey, 57% of DevOps professionals stated that the lack of data observability makes it difficult to automate in a compliant manner . As a result, organizations will increasingly demand solutions that provide data observability, enabling faster and more secure ingestion of high-quality, reliable data ready for on-demand analysis.
Organizations will extend observability to more business use cases as senior management looks to support sustainability and FinOps goals. The combined pressure to adopt environmentally sustainable business practices and address rising cloud costs will drive observability from an IT priority to a business requirement. The increase in the use of AI by Cell Phone Number List organizations will be a key driver of this trend, as it increases the consumption of cloud resources, resulting in expanded carbon footprints. However, AI-powered observability data analytics can help organizations address these challenges and mature their FinOps and sustainability practices by delivering actionable insights and fueling intelligent automation to address critical points of inefficiency in cloud environments. The increased use of AI-powered observability will allow organizations to automatically orchestrate their systems for optimal resource utilization, reducing emissions and the cost of operating their cloud environments. As a result, we will see growing interest in observability use cases beyond the IT department as the broader enterprise begins to pay attention.
Organizations will recognize that a smoothly and securely functioning software delivery pipeline is equally vital to business continuity as the quality and security of digital services that end users and customers rely on. Therefore, we will see a shift toward productization (turning a company's services into products) of the tools used to drive DevOps, security, and site reliability engineering (SRE) best practices . This will bring platform engineering to the forefront as organizations codify the knowledge and capabilities necessary to automate secure software delivery pipelines. As this trend takes hold, software delivery, security, and operations processes will be unleashed through application programming interfaces (APIs) that automate those tasks based on real-time insights from observability data.
Organizations will extend observability to more business use cases as senior management looks to support sustainability and FinOps goals. The combined pressure to adopt environmentally sustainable business practices and address rising cloud costs will drive observability from an IT priority to a business requirement. The increase in the use of AI by Cell Phone Number List organizations will be a key driver of this trend, as it increases the consumption of cloud resources, resulting in expanded carbon footprints. However, AI-powered observability data analytics can help organizations address these challenges and mature their FinOps and sustainability practices by delivering actionable insights and fueling intelligent automation to address critical points of inefficiency in cloud environments. The increased use of AI-powered observability will allow organizations to automatically orchestrate their systems for optimal resource utilization, reducing emissions and the cost of operating their cloud environments. As a result, we will see growing interest in observability use cases beyond the IT department as the broader enterprise begins to pay attention.
Organizations will recognize that a smoothly and securely functioning software delivery pipeline is equally vital to business continuity as the quality and security of digital services that end users and customers rely on. Therefore, we will see a shift toward productization (turning a company's services into products) of the tools used to drive DevOps, security, and site reliability engineering (SRE) best practices . This will bring platform engineering to the forefront as organizations codify the knowledge and capabilities necessary to automate secure software delivery pipelines. As this trend takes hold, software delivery, security, and operations processes will be unleashed through application programming interfaces (APIs) that automate those tasks based on real-time insights from observability data.