Data Epistemology

Epistemology, a term rooted in the Greek word episteme, meaning “knowledge,” and logos, meaning “discourse” or “communication,” explores the concept of “justified knowledge.” It addresses questions like “What do we know?”, “How can we claim to know something?”, “What validates our beliefs?”, and “How do we ascertain the certainty of our knowledge?”

In the realm of data, these questions take on a new dimension, especially in the Big Data era, where we store vast amounts of raw data, structured, semi-structured, and unstructured. Data science teams apply machine learning or deep learning models to extract insights, turning raw data into knowledge. However, to ensure that these insights align with their origins, a “justification of knowledge” is essential, ensuring that what we extract truly reflects what was expected. This process emphasizes evidence-based validation, where qualitative research relies on the quality of design and data collected.

A key challenge in Big Data is in the generation and validation of knowledge. It’s crucial to analyse the complex relationships between the knowledge produced, the entities that produce it, and the methods used to generate it.

Conventionally, knowledge is considered to build on information, yet I believe it can be derived directly from data. For further exploration, please refer to the topic "Data vs. Information vs. Knowledge vs. Wisdom," along with my interpretation of this progression.

My interpretation of Data vs Information vs Knowledge vs Wisdom