Understanding Data

In the early stages of my career, I approached data tables as many do: akin to a spreadsheet. I organized data into tables that mirrored the format in which I received it. This approach was logical; it was clear, readable, and functional.

As I delved deeper into the study of normal forms and database theory, I grappled with concepts like the transitive property—pondering whether if 'a' equals 'b' and 'b' equals 'c', then 'a' must always equal 'c'. I spent countless hours considering functional dependencies, questioning whether certain values were truly interdependent. I encountered numerous diagrams attempting to elucidate these principles with examples I would never group together in a single table. After years of contemplation, an epiphany struck: I understood the essence of data.

Data resembles an asterisk or a star—not in the sense of a star schema, but as a loosely defined nucleus radiating outwards. It harkens back to my elementary school days, experimenting with linear algebra and discovering that plotting points from x1 to y20, x2 to y19, and so on, resulted in a curve. These insights might seem eccentric, but they were my moment of clarity.

Faced with the decision of whether to add a middle name, initial, or salutation to a 'tbl_person', I realized the answer was a resounding 'no'. Rejecting even first and last names may appear nonsensical or impractical, but it prompts a significant inquiry:

How often do you say 'no'?

Consider the times when a task seemed insurmountable, when adding a column to a database table led to a cascade of errors, or when modifying code resulted in unforeseen complications. We've all encountered these challenges. No external solution or additional technology can rectify the foundational issues that were present from the outset.

Vertical Data Storage

The concept of vertical data storage revolutionizes our approach to data management. By storing data vertically, we can collect diverse data types without the need for constant table modifications. This method allows for the addition of new data points as they emerge, without disrupting the existing structure. It's a dynamic and flexible system that adapts to the evolving nature of data, ensuring that our databases remain robust and scalable.

Structuring Data with Precision

A mere type table is insufficient for the intricate task of managing data within subject domains. To achieve a comprehensive understanding, we introduce a family table to describe types, a class table to describe families, and a realm table to describe classes. This hierarchical structuring enables us to manage sets of data with precision, allowing for a clear delineation of relationships and dependencies within the data.

Fueling Machine Learning and AI

The combination of these tables—type, family, class, and realm—provides the foundation for creating data-driven pivots. This technique facilitates the generation of countless combinations of features, which are essential for machine learning and artificial intelligence processing. By leveraging this adaptable framework, we can achieve peak quality in data analysis, ensuring that our algorithms are informed by the most relevant and comprehensive data sets available.

In conclusion, the journey of understanding data is ongoing. As we continue to explore and innovate, we must remain open to new methodologies that challenge traditional paradigms. The pursuit of knowledge in the realm of data is not just about the numbers; it's about the stories they tell and the future they help us shape.