In the era of algorithms and Artificial Intelligence, it’s easy to forget that the real power of Data Science doesn’t come from the models themselves, but from the quality and trustworthiness of the data behind them. With this in mind, me (Tânia Costa) and Liliana Silva, data engineers, presented the topic “Data Chaos vs. Data Trust: The Invisible Challenge of Data Science” in the course New Perspectives in Data Science at Universidade Portucalense.

The starting point was simple yet striking: according to Gartner, organisations lose an average of $12.9 million per year due to poor data quality. Duplicated, inconsistent, and untraceable information fuels what we call data chaos — a state where data becomes more a source of confusion than of insight. To counter this, organisations must build data trust, ensuring their information is accurate, traceable, and governed transparently.

During the presentation, we highlighted three essential pillars for building data trust: data quality, which measures the accuracy and completeness of information; data lineage, which ensures traceability and visibility of data transformations; and data governance, which establishes the policies, roles, and rules that allow data to be managed as a true enterprise asset. Illustrating how even simple governance and quality practices can reshape how organisations perceive and leverage their data.

More than a technical matter, Data Chaos vs. Data Trust is a reflection on confidence and accountability in the data-driven era. Without quality, there is no truth; without governance, there is no transparency. It is in this balance — between rigor and ethics, control and value — that data science finds its true purpose.

We would like to thank Professor Fernando Moreira and Portucalense University for the opportunity to present our data knowledge and contribute to the discussion on one of the most critical and often overlooked challenges in modern data science — building trust in data.

Tânia Costa