The greater pressure from both customers and regulators to maintain and enhance service reliability, while at the same time controlling costs, has caused many utility distribution businesses to adopt Asset Management (AM) as their framework. Therefore, AM is widely being applied in asset intensive industries around the world. Generally, AM consists of data driven decision-making processes with the goal of deriving the most value from utility assets within the available budget. Asset intensive industries rely on asset data, information and asset knowledge as key enablers in undertaking both strategic AM activities and operational activities. Good asset information (timely, reliable and accurate data) enables better decisions to be made such as determining the optimal asset maintenance or renewal frequency for an asset. Consequently, in the past years utilities have progressively created databases to record asset or business data such as failure, maintenance, operation and cost. However, in many cases, the available data required to track asset population reliability are not sufficiently rich to provide a basis for straightforward decision-making processes. However, the determination of asset population reliability requires collection of data and systematic and scientific evaluation of data on equipment failures. In this MSc thesis project, a systematic approach for analyzing asset life data (data describing equipment lifetime) in presence of incomplete data by means of statistical analytical methods is introduced and applied in a practical case for medium voltage cable joint populations. The analysis in this thesis mainly focus on a statistically based approach which uses data available from the past to predict short term reliability of this specific group of assets.