Introduction To Data Modeling
Published: 2023-10-16 08:16:57

**What does data modeling have to do with data science?**
Data modeling plays an especially crucial part in data science as it helps represent, organize and structure data in a meaningful way. It helps to structure the data logically and defines the relationship between data elements, so that it is easier or comprehend and manage. It also handles data integration, which becomes more vital the more complex data is.

**What is Data modeling?**
Data modeling is the process of visually designing and representing the data structures, in order to communicate the information system, how each data point affects another, what the data system looks like and what attribute or characteristics each type of data has.

**What kinds of Data modeling are there?**
Depending on the level of abstraction, data modeling can have three different types. Starting from the highest level, we have conceptual models which contain the least amount of detail and specifics. These are also referred to as domain models, provided general information and big picture view. They usually contain data entities, their characteristics and the relationship between them.
Going softer on abstraction, now we have logical models. These utilize a lot more detailed information and mentioned additional info such as attributes e.g. the data types, length of allowed characters or form of numeric values. These models however, do not mention any specific technical system requirements.
And last but not least, we have the most detailed and least abstracts model of them all, i.e. physical data models. These mention how data will be physically within a database, have a finalized design of data including characteristics, relationships, main and foreign keys, and are able to convey information about specifics desired database management systems.

**What components can be observed within a data model?**
Each data model should have some common components. The most basic of these are called “entities”, which describe events, things or concepts that will be described in the database. Entities have to be cohesive and logically discrete, as to avoid collision or confusion. Each of the entities will have properties that help describe it and differentiate it from other entities. These are called “attributes” and can include information such as names, dates, amounts, addresses or etc. We also have rules and conditions which govern over possible values. These “constraints” ensure consistency, integrity and that data will not get out of control. Constraints can apply to entities, attributes, relationships or even business rules.

**Why do we even do data modeling?**
As for the many benefits of data modeling, providing a structured approach to understand and represent complex data characteristics, in order to facilitate communication and alignment between stakeholders can be mentioned. Using data modeling, we can identify key relationships, entities and even attributes. It will also help with data consistency and integrity, reduce possible error at database development and improve performance.

**How should I do data modeling?**
Although it is possible to manually perform data modeling (in fact, data modeling was traditionally done manually without relying on any tools.) there are a myriad of various tools commonly used today that help facilitate and automate the process. Erwin Data Modeler, ER/studio, PowerDesigner, Oracle SQL developer data modeler and etc. are some examples in this regard.

**Conclusion**
In conclusion, data modeling is an important process that is useful in representing and organizing the data in a meaningful way. It provides logical understanding of the data structure and helps with integration and consistency. Utilizing the potential of data modeling, we can extract insights and make informed decisions based on well-structured, well-represented data.