Increased Use of AI in IT Needs Transparency as a Priority
In today's fast-paced business world, 72% of companies are leveraging AI in at least one function [1]. However, the complexity of hybrid, dynamic IT environments presents a challenge: incomplete, inaccurate, or outdated asset inventories can lead to AI making incorrect decisions or missing important information.
To address this issue, a comprehensive approach is essential to ensure accurate asset data for optimal AI observability. Here's a breakdown of the key steps to achieve this:
Comprehensive Data Discovery and Classification
The first step is to conduct a thorough inventory of all data assets across clouds (e.g., AWS, Azure, Google Cloud), on-premises, and third-party systems. Automated discovery tools can help crawl environments, but manual review is necessary to ensure accuracy, especially for sensitive or industry-specific data. Standardizing ambiguous data fields (e.g., “ID,” “Notes”) also improves tagging quality, as metadata precision directly impacts AI model reliability in observability [2].
Unified Observability Platforms
Replacing fragmented monitoring tools with unified observability systems that provide end-to-end visibility across hybrid environments is crucial. These platforms enable tracking of asset performance, usage, and anomalies in real-time, overcoming silos between different systems [1][3].
Leveraging AI-Powered Data Observability
Employ AI and machine learning algorithms to continuously monitor and analyze asset data quality, schema changes, and usage patterns. This proactive, predictive approach detects anomalies, data corruption, or drift before they impact operations. AI observability tools inform data teams with detailed context and actionable insights for rapid remediation [4].
Focus on Explainable AI Governance Tools
Select AI governance and observability tools based on their ability to provide transparent insights and fine-grained controls. This facilitates trust, human intervention, and regulatory compliance in handling decentralized asset data [2].
Reduce Complexity and Enhance Visibility
Consolidate observability tools to reduce the complexity of managing hybrid IT ecosystems. Intelligent observability systems that “understand” and correlate data across decentralized assets help improve security posture, mitigate insider errors, and counter sophisticated external threats [1].
In summary, accurate asset data for AI observability in hybrid, decentralized IT environments depends on detailed data asset inventory and classification, unified and AI-driven observability platforms, explainable governance tools, and reducing tooling complexity to enhance visibility and trust across the entire hybrid infrastructure [1][2][3][4].
Unfortunately, many companies still rely on outdated, incomplete asset inventories. Without proper visibility, AI becomes just another layer of guesswork due to compromised data at the source by poor visibility, broken inventories, or contextless assets. To illuminate the landscape for AI to navigate, it's crucial to adopt modern, integrated solutions that provide real-time or near-real-time asset discovery and eliminate blind spots.
References:
[1] New Relic. (2021). The state of observability in 2021. Retrieved from https://www.newrelic.com/blog/state-observability-2021/
[2] Gartner. (2020). Gartner defines data observability. Retrieved from https://www.gartner.com/en/newsroom/press-releases/2020-09-29-gartner-defines-data-observability
[3] DZone. (2021). The state of observability 2021. Retrieved from https://dzone.com/articles/the-state-of-observability-2021
[4] Datadog. (2021). Datadog launches AI-powered data quality monitoring. Retrieved from https://www.datadoghq.com/blog/datadog-launches-ai-powered-data-quality-monitoring/
Data-and-cloud-computing technology plays a significant role in creating accurate asset inventories, as automated discovery tools aid in thorough data asset exploration across diverse environments, including clouds and third-party systems. Unified observability platforms, further leveraging technology, offer real-time visibility across hybrid environments, enabling efficient tracking of asset performance and anomalies.
