Vessels during their calls on ports is possible to have an inspection from the local Port State Control (PSC) authorities regarding their implementation of International Maritime Organization guidelines for safety and security. This qualitative study focuses on how shipping professionals understand and use Big Data in the PSC inspection databases, what characteristics they recognize these data should have, what value they attach to those big data, and how they use them to support the decision-making process within their organizations. This study conducted interviews with shipping professionals, collected their perspectives, and analyzed their sayings with Thematic Analysis to reach the study's outcome. Many researchers have been discussed Big Data characteristics and the value an organization or a researcher could have from Big Data and Analytics. However, there is no universally accepted theory regarding Big Data characteristics and the value for the database users. The research concluded that Big Data from the PSC inspections procedures provides valid and helpful information that broadens professionals' understanding of inspection control and safety need, through this, it is possible to upscale their internal operations and their decision-making procedures as long as these data are characterized by volume, velocity, veracity, and complexity.

The term Big Data has been gaining importance both at the academic and at the business level. Information technology plays a critical role in shipping since there is a high demand for fast transfer and communication between the parts of a shipping contract. The development of Automatic Identification System (AIS) is intended to improve maritime safety by tracking the vessels and exchange inter-ship information.  This master’s thesis purpose was to a) investigate in which business decisions the Automatic Identification System helps the shipowners and operators (i.e., users), b) find the benefits and perils arisen from its use, and c) investigate the possible improvements based on the users’ perceptions. This master’s thesis is a qualitative study using the interpretivism paradigm. Data were collected through semi-structured interviews. A total of 6 people participated with the following criteria: a) position on technical department or DPA or shipowner, b) participating on business decisions, c) shipping company owns a fleet, and d) deals with AIS data. The Thematic Analysis led to twenty-six codes, twelve categories and five concepts. Empirical findings showed that AIS data mostly contributes to make strategic business decisions. Participants are interested in using AIS data to measure the efficiency of their fleet and ports, to estimate the fuel consumption, to reduce their costs, to protect the environment and people’s health, to analyze the trade market, to predict the time of arrival, the optimal route and speed, to maintain highest security levels and to reduce the inaccuracies due to manual input of some AIS attributes. Finally, participants mentioned some AIS challenges including technological improvement (e.g., transponders, antennas) as well as the operation of autonomous vessels.  Finally, this master’s thesis contributes using the prescriptive and descriptive theories and help stakeholders to find new decisions while researchers and developers to advance their products.