MDR services play a crucial role in cybersecurity by remotely monitoring, detecting, and responding to threats through threat intelligence and human expertise. However, managing large volumes of diverse data poses significant challenges. Establishing a single source of truth, such as a unified data repository like a data lake, is essential to consolidate and analyze this data effectively. This enables MDRs to enhance their threat detection capabilities, provide more accurate responses, and offer faster services to customers. Unlike EDRs, which focus on endpoint-level data, MDRs look for patterns in telemetry data across various security tools to detect anomalies and patterns that indicate ongoing attacks. XDR takes this a step further by integrating data from across an organization's environment, including endpoints, networks, and cloud systems. Proactive threat hunting is critical for maintaining a proactive security posture, but challenges arise when dealing with large-scale data sets without scalable data management strategies. Leveraging tools like ChaosSearch can help MDRs overcome these challenges by creating a unified data model, normalizing data into a standard format, and analyzing it at scale. This approach enables MDRs to offer faster, more accurate threat detection and response services, giving them a competitive advantage in the market.