How AIOps Is Empowering Enterprise Digital Transformation

In the digital economy era, enterprises view digital transformation as a crucial growth driver, yet many struggle due to irregular production relations, management processes, and strategies.

The DevOps International Summit (DOIS) was held in Beijing on October 22‑23, 2021, showcasing case studies and tools from leading internet, overseas, and industry firms, with a focus on DevOps practices in finance and telecommunications.

Yang Lingling, Director of the Governance and Audit Department at the China Academy of Information and Communications Technology (CAICT) Cloud Computing Institute, delivered a keynote titled “Exploring IT Governance Trends in the New Era,” outlining the institute’s efforts in IT governance and supporting enterprise digital transformation.

IT governance, as a core component of corporate governance, relies on three pillars—R&D operations, risk management, and IT value—to drive business through IT, enhancing development efficiency, customer experience, and innovation.

CAICT’s Cloud Computing and Big Data Research Institute has contributed significantly to enterprise digital transformation across AI, blockchain, big data, and cloud computing, launching the “DevOps Capability Maturity Model” in 2018, which has evaluated over 100 projects across banking, securities, insurance, internet, telecom, and mobility sectors.

With the proliferation of enterprise applications and increasingly granular cloud resource management, operational complexity has risen, giving birth to AIOps. AIOps, derived from DevOps, integrates big data, AI/ML, and other technologies to provide proactive, personalized, and dynamic insights supporting all major IT operation functions.

AIOps offers several key advantages:

Real‑time analysis : Applies various algorithms to data in real time, delivering immediate problem diagnosis and operational recommendations.

Noise reduction : Uses big‑data analytics and machine learning to filter out alarm noise, a persistent challenge in operations.

Fault cause analysis and prediction : Analyzes massive datasets to identify root causes and predict future failures based on historical trends.

Operational recommendations : Provides actionable advice based on both real‑time and historical data.

In October 2021, CAICT conducted the first batch assessment of the “AIOps Capability Maturity Model – Part 2: System and Tool Technical Requirements,” with five enterprises and six projects passing the comprehensive evaluation.

The AIOps standards consist of two parts:

Part 1 – General Capability Requirements : Defines a reference framework and grading method for intelligent IT operations applicable to various infrastructures and resources.

Part 2 – System and Tool Technical Requirements : Specifies the framework and technical criteria for intelligent operation systems and tools, guiding both implementers and product providers.

The first batch evaluation of the technical requirements opened four modules: anomaly detection, fault prediction, alarm convergence, and root‑cause analysis.

For further information on AIOps system and tool capability maturity assessments, contact the China Academy of Information and Communications Technology.