One of the main language objectives is to decipher the information that is transmitted in order to be understood, interpreted, and react to the message. On the other hand, when talking about data analysis, algorithms are applied that manage to decipher the collected information making it possible to address almost all potential problems or situations that may have arisen in the world, either in the present or in the past. The main objective when deciphering this information is to create new opportunities to prevent or propose solutions to different types of situations. However, it can be argued that one’s perspective on a problem and its possible solution may be influenced by previous experiences or limited by a lack thereof. Consequently, data analysis is challenged to identify and recognize patterns and correlations (decipher) within data through algorithms like statistical/machine learning, among others. In this way, the idea that data analysis is also the language of data is introduced. The data has meaning and, depending on the context, can help to know the status of structures, patients, or processes with the primary objective to make predictions and/or forecasts. Two seemingly dissimilar fields are presented, but they converge on a common point in the pursuit of a solution. They are structural health monitoring (SHM) and decision support for medical staff in the intensive care unit (ICU). In SHM field, damage detection problems will be addressed utilizing different types of signals collected from an unmanned aerial vehicle (UAV). In the context of the ICU, the main objective is to know if patients with Coronavirus Disease 2019 (COVID-19) should be intubated or to know the neurological prognosis of patients with subarachnoid hemorrhage pathology. In both fields, data are stored for further analysis, where concepts such as descriptive statistic, statistics, hypothesis testing, correlation analysis, resampling, and models based on statistical or machine learning methods, among others are employed. Many times, a simple solution using basic statistical concepts has enabled the development of data analysis techniques. Finally, the methods developed in each field have facilitated the creation of predictive models for decision-making processes. Along with these models, quality indices such as accuracy, precision, recall, specificity, among others allow the results to be objectively evaluated and compared, adjusting them according to the required result. Data language deciphering in the field of SHM enables the early detection of damage. In the ICU context, it allows developing powerful decision support tools that permit experts to operate with greater ranges of reliability and efficacy and with less uncertainty to identify and develop solutions. In conclusion, the objective of this work is to disseminate the fundamental aspects of these research and outline the nuances of decision-making associated with each specific case. In each of these fields, issues related to interpretability, the emergence of neologisms, and dialectal variations, along with basic statistical concepts and the help of ML and SL methods, played a prominent role in solving real-world situations.