How AIOps and DataOps Transform Big Data Operations: Lessons from ABM Platform

Introduction

In the digital era, extracting value from massive and diverse data is a key success factor for enterprises, making big data a critical driver for decision‑making and operations. When data grows rapidly without orderly management, organizations face "data disasters"—the very problem DataOps aims to solve.

Although DataOps provides a structured methodology for data management, manual fault diagnosis and recovery become increasingly difficult in large‑scale environments, especially for observability, anomaly detection, root‑cause analysis, and self‑healing. To address these limitations, AIOps (Artificial Intelligence for IT Operations) combines big data and machine learning to automate data collection, anomaly detection, event correlation, diagnosis, and recovery, reducing MTTR and MTTD. DataOps and AIOps are distinct yet complementary approaches that together enable digital, automated, and intelligent operations.