Oil and gas pipelines, which transport and distribute oil products and natural gas to industrial and residential users, are subject to catastrophic accidents or failures due to leakages and ruptures. Pipeline wall thickness loss due to corrosion is a significant reason for pipeline failure. In-line inspection (ILI) is carried out periodically to detect and quantify the metal loss and assess the integrity of pipeline. Multiple sensors are often employed to perform the non-destructive in-line inspection to reduce the uncertainty and enhance the reliability. In this research, we aim to conduct an exploratory analysis of multiple ILI data, e.g. magnetic flux leakage (MFL) and axial flaw detection (AFD) data, to understand the statistical relationship between the heterogeneous data sources. The outcomes from this study will serve as a basis for further investigations on data alignment and fusion for improved data interpretation.