From AI troubleshooting assistants significantly improving fault location efficiency, to intelligent log parsing breaking through the bottleneck of unstructured data processing, and to vertical domain large models achieving deep alignment between O&M language and business language... AI technology is visibly driving the O&M system to gradually evolve from a passive response model of alert triggering and manual intervention, to an intelligent O&M system with proactive perception and auxiliary repair capabilities.

Especially in the global expansion marketing sector, where business spans multiple countries and regions worldwide, the growing popularity of concepts such as AIOps (Artificial Intelligence for IT Operations) and Agents has made the introduction of AI O&M tools a consensus choice for an increasing number of global enterprises. However, in the actual implementation process, a new problem has gradually emerged: why do many global enterprises that have introduced AI O&M tools still struggle to break free from the challenges of alert noise overload, excessively long fault location cycles, and uncontrollable system stability at critical nodes such as peak sales events?

In this issue of MarTech Hub, combined with eclicktech's AIOps best practices, we will focus on how AI can rebuild the O&M foundation for global expansion marketing with "observability" as the core capability in the context of multi-cloud and globalization, to lay a solid technical cornerstone for the sustained growth and stable operation of business.

As a martech company that pioneered the AIGC strategy in the global expansion marketing field, in recent years, eclicktech has not only continuously launched an AI marketing product matrix covering the end-to-end process of intelligent insight, creative generation, automatic delivery and data attribution at the application product level; but also built an intelligent O&M system adapted to global business by integrating AI into the middle platform, with "observability" as the core breakthrough.

It is also in this process that eclicktech has identified that the key reason why global enterprises still cannot get rid of the traditional O&M dilemma even after introducing AI O&M tools lies not only in the advancement of the AI technology itself, but more importantly in whether the O&M system has the basic conditions to support the full play of AI capabilities.

Highlight 01 Unified Observability: The Prerequisite for AI Intelligent O&M to Truly Take Effect

In global expansion marketing scenarios where multi-cloud architecture, cross-regional deployment and high-concurrency traffic coexist, system complexity often grows exponentially. At this point, without a unified and reliable observable data foundation, even with the introduction of AI O&M tools, it is difficult for the model to form a stable and reusable judgment basis, let alone truly participate in the closed loop of O&M decision-making.

In practice, eclicktech has gradually realized that observability is not simply "stacking monitoring tools", but building a credible and understandable data foundation for AI O&M. In the context of global marketing business, this foundation must meet at least three core requirements simultaneously:

• Cross-Environment Uniformity: Data from different cloud vendors, different regional nodes, and different business systems must be integrated through unified collection and standardized data definitions to avoid data silos.

• Data Correlation: Multi-dimensional Metrics, Logs and Traces should not exist in isolation. They should be correlated with each other in one system, supporting root cause problem solving through multi-dimensional analysis.

• Business Interpretability: Abnormal signals at the technical level must ultimately be mapped to specific business behaviors, helping the O&M and business teams form a shared understanding.

Based on this understanding, in the initial stage of building the intelligent O&M system, eclicktech prioritized starting with "unified observability", gradually integrated monitoring data scattered across different cloud platforms and business systems, and built a unified observation view covering infrastructure, application services and key business links.

At this stage, the monitoring platform mainly assumes the role of the "fact layer": ensuring the integrity, consistency and traceability of data, rather than prematurely introducing complex automated decision-making logic. Centered on this goal, eclicktech combined the capabilities of the Tencent Cloud Observability Platform, and gradually formed a monitoring system of "unified access, intelligent collection, optimized processing" with Prometheus monitoring service as the core. While ensuring monitoring coverage and reliability, it balances system operating costs and O&M efficiency. It is precisely on this observable foundation that subsequent anomaly analysis, root cause location and intelligent auxiliary judgment have the possibility of continuous evolution, laying the necessary prerequisite for the further implementation of AIOps capabilities.

Highlight 02 Intelligent Strategy Adaptation: Enabling the Observable Foundation to Support Efficient AI Operation

After the unified observable data foundation is built, how to avoid "system overload caused by data overload and AI judgment interference from invalid data" has become a new challenge for global enterprises implementing AIOps. Especially in a business environment with tens of billions of monitoring data per day, massive data brings not only a surge in storage and computing costs, but also traps AI models in the dilemma of "data noise" during training and inference, which in turn reduces the decision-making efficiency and accuracy of AI O&M.

In practice, eclicktech has gradually found that observability construction is not "the more data the better", but requires strategic governance to make data lean, accurate, stable and clear. Based on this judgment, eclicktech, together with the Tencent Cloud Observability Platform, built a set of intelligent data governance strategies centered on "core metrics first, differentiated frequency collection", achieving a balance between cost and O&M efficiency while ensuring the accuracy of AI analysis. Its core experience is mainly reflected in three aspects:

• Prioritizing Core Metrics to Reduce Data Noise at the Source: In the data collection phase, relying on the metric pre-aggregation capability provided by the Tencent Cloud Observability Platform, it systematically sorts out business metrics, focuses on core indicators, eliminates redundant and valueless indicators, reduces data reporting volume at the source, and cuts costs.

• Differentiated Collection Frequency to Balance Real-Time Performance and Efficiency: A layered collection mechanism is established according to the business attributes of different indicators. For core scenarios with high timeliness requirements such as ad delivery status and real-time data table updates, quasi-real-time high-frequency collection is adopted to ensure that AI can capture business fluctuations in a timely manner; for non-critical system status indicators, low-frequency collection is used to reduce resource consumption without affecting the monitoring effect.

• Centralized Strategy Management to Ensure Consistency of Data Caliber: All collection strategies (targets, frequency, filtering rules, etc.) can be centrally configured through the Tencent Cloud Observability Platform console, which greatly improves O&M efficiency by more than 30%, and also ensures the uniformity of collection rules.

The implementation of AI O&M is never a single-point breakthrough of technology, but the collaborative effort of data foundation and intelligent strategies. Unified observability solves the availability of data, and intelligent governance ensures data quality, both of which together clear the obstacles for AI to empower O&M. For global enterprises with business all over the world, the O&M challenges under the multi-cloud architecture are inherently complex, but the cooperation between eclicktech and Tencent Cloud provides a clear answer: take observability as the root, and improve efficiency with intelligent strategies, so that AI can truly become the "intelligent brain" of O&M. In the future, with the deepening of AIOps technology, only by consolidating the data foundation and accurately adapting to business strategies can global enterprises achieve the leap from passive fire-fighting to proactive defense in O&M, and escort the growth of global business.