In today’s digital landscape, IT operations face unique challenges and pressures unlike those of the past. Currently, the cost of a service failure for medium and large enterprises is estimated to exceed $100,000 per hour. At present high incident management costs, coupled with the impact on customer satisfaction, present significant challenges for enterprises. To resolve this challenge AI and ML assists in enhancing the overall management of incidents and reducing response times.

Telco service providers face a unique set of challenges when it comes to service delivery. Their networks are complex and heterogeneous, and they need to be able to correlate events from multiple protocols and layers in order to quickly identify and resolve problems.

In today’s digital landscape, delivering a flawless customer experience is the ultimate competitive advantage. However, traditional methods of ensuring service resilience during operation can often be both expensive and cumbersome to maintain. This is where left-shift operations come into play—a powerful strategy aimed at instilling quality and resiliency in the early stages of building and delivering high-quality products and services..

As per the latest forecast by Gartner, worldwide IT spending is projected to amount to $4.6 trillion in 2023, up by 5.1% from 2022. The demand for IT will be strong in 2023 as enterprises launch digital business initiatives to respond to global economic challenges. In a downward economy, conventional wisdom warrants reducing costs.

Cisco’s one-of-a-kind conference, Cisco Live EMEA 2024 in Amsterdam, marked a pivotal milestone in illuminating the path forward with a spotlight on AI, operational simplicity, and security. Bringing key network telemetry and correlating it with the business outcomes has become quite essential for Modern Enterprises and CloudFabrix addressed this by launching three new modules for the Cisco Observability Platform.

Data-centric AI is the new frontier in AI, where the models themselves now remain stationary while tools, techniques and engineering practices improve data quality. As Andrew Ng puts it, “Data-centric AI is the discipline of systematically engineering data to build an AI system.”

CloudFabrix, the Robotic Data Automation Fabric inventor, announced “Data Observability Pipelines” for dynamic Data Ingestion and automation for any data source and destination. The solution acts as a data management and integration service that uses robotic processes to automate data tasks, such as data integration, data ingestion, cleansing, transformation, and enrichment. Automated data management saves time, improves data quality, and streamlines data workflows.