What's the best way to design a normalized data model?

In my experience, data normalization is overrated. The degree to which it is normalized (1NF, 2NF, 3NF) should depend not only on data quality, but the way data is read and/or written. The classic mantra is that you break 3NF to gain efficiencies. If you're going to have an invoice generating function, it may get resource-intensive if you have to do a join to get the product name and another join to get the city of the delivery address. You may want to duplicate to save on the joins. You have to understand how your data *should* be organized in the logical model, then use that understanding to build the physical model.

Normalization is really important in this age as well...... Reduced Data duplicacy: Normalization minimizes data duplication, saving storage space and reducing the risk of inconsistencies. This is crucial for smaller datasets and data warehouses where efficiency is critical. Builds Trusted Data: Normalized tables enforce data relationships, reducing the chances of errors and improving data quality. This is essential for maintaining accurate reporting and analysis. Simplify: Normalized structures allow for efficient querying of specific data points. Updates and modifications are also easier to manage when data is not scattered across multiple tables.

I'll flip this question upside down. Why would we even bother creating normalized data models in this day and age? The industry is moving in the opposite direction (de-normalization). AI/ML cannot work with normalized data. NoSQL databases and data lakes are the norm. Data Warehouses do not use traditional normalization techniques. Data duplication is a fact of life these days. Don't bother normalizing. I'm sure this will make some folks roll their eyes. Happy to discuss this.

Normalization is a technique of organizing the data in the database. It is a systematic approach of decomposing tables to eliminate data redundancy, ensuring data dependencies make sense, thereby preventing anomalies that can arise from data manipulation. It is a multi-step process that puts data into tabular form by removing duplicated data from the relation tables.

In my opinion, understanding both the logical organization of data and how it will be used is key to designing an effective physical data model. The adoption of a balanced approach, valuing both theoretical principles and real-world application is essential to determine what level of normalization is required.

A normalized data model is crucial for businesses, especially given the risks of data mismanagement. Without it, inconsistencies can lead to flawed decision-making, while uncontrolled data growth may hinder scalability and performance. In today's environment, neglecting normalization can result in missed opportunities and increased costs. Therefore, prioritizing normalization is essential for businesses to manage data effectively and maintain a competitive edge.

A normalised data model follows principles of ERD (Entities Relationship Diagram). ERD is developed based on business process design. Having robust data model which reflects relationships between configuration data, master data and transactional data is important for the successful development of a data model. It’s easy to maintain, customise, update and improve a normalised data model. Further, a normalised data model helps in performance improvement and productivity. It helps in data security by providing only required access to relevant persons leaving less room for widespread data access.

Normalization is important for several reasons such as it reduces data redundancy, prevents data anomalies, improves data integrity, facilitates database maintenance, supports scalability and performance, enforces data consistency. Overall, normalization is a fundamental principle in database design that promotes efficiency, consistency, and integrity in data management. It provides a structured approach to organizing data, making databases more manageable, reliable, and scalable.

To design a normalized data model efficiently, identify entities and attributes, organize them into tables, and apply normalization principles (those xNF). Leverage tools like ER diagrams and automation scripts for faster development. Involve stakeholders to ensure the model meets business needs.

If we are building data model from scratch it is good to start with conceptual model - where you start skatching main business entities with brief descriptions e.g. "Party - Any individual or corporate entity to which our company has some business relationship" or "Contract - Written agreement for providing our product or service to at least one party". Then start to explain relations between entities - between party and contract there could be "Customer - Party who is the named individual or party on the the contract", "Contact person - Party who was communicating with us in the process of creating of the contract" Agree understanding with business and then move to logical and physical model where you add columns, normalization, etc.

Designing a normalized data model involves following a set of guidelines. Here's a step-by-step approach to design a normalized data model: 1. Identify Entities: Identify the main entities or objects in your data domain. 2. Define Attributes: Determine the attributes or properties of each entity. 3. Identify Relationships: Identify the relationships between entities. Relationships define how entities are related to each other and help establish connections between different parts of the data model. 4. Apply Normalization Rules: 1NF, 2NF, 3NF, 4NF 5. Refine the Model: Review the data model to ensure that it meets the requirements and properly represents the relationships and constraints in the data domain. 6. Test and Iterate

A primeira forma norma (1FN) é o primeiro nível de normaliza??o e estabelece que os atributos das tabelas sejam indivisíveis. Ou seja, cada célula na tabela deve conter apenas um valor (atomicidade). A tabela deve ter uma chave primária para identifica??o única de cada registro. N?o pode haver duplica??o de linhas ou colunas.

First Normal Form (1NF) is the initial stage of normalization in database design. It establishes the fundamental rules for organizing data in a relational database to eliminate redundancy and ensure data integrity. A relation schema is in 1NF, if and only if: - All attributes in the relation are atomic (indivisible value) - There are no repeating elements or group of elements

Second Normal Form (2NF) is a further stage of normalization in database design that builds upon the principles of First Normal Form (1NF). It addresses partial dependencies within a table by ensuring that all non-key attributes are fully functionally dependent on the entire primary key. A relation is said to be in 2NF, if and only if: - It is in 1NF - No partial dependency exists between non-key attributes and key attributes.

A segunda forma normal (2FN) indica que os atributos que n?o s?o chave dependem unicamente da chave primária da tabela. Ou seja, n?o pode haver dependência parcial dos componentes da chave primária. Dica: A 2FN só pode ser aplicada em tabelas com chave composta (mais de uma coluna na chave).

A terceira forma normal (3FN) estabelece que os atributos n?o chave s?o independentes entre si e dependentes única e exclusivamente da chave primária da tabela. Dica: se a tabela está na 3a. forma normal, automaticamente estará na 1a. e na 2a. formas normais.

There more normalized techniques to handle complex data redudancy: 4NF for multivalue dependency & 5NF for join dependency. Evaluate how these techniques need to apply on your case.

3 steps .. 1. Determine the entities in your system and organize them into tables. Each table should represent a single entity, such as customers, products, or orders. 2. Establish relationships between the entities using foreign keys. For example, a customer may place multiple orders, so there would be a foreign key in the orders table linking it to the customers table. 3. Apply normalization techniques to reduce redundancy and dependency in the database. This involves breaking down larger tables into smaller ones and ensuring each table represents a single subject. This typically involves normalization to at least the third normal form (3NF) to minimize data redundancy and ensure data integrity.

In addition to normalization, consider factors such as data requirements, performance, scalability, security, flexibility, data consistency, maintainability, and collaboration. These elements ensure that the data model not only minimizes redundancy and enhances integrity but also meets the specific needs of the application, optimizes performance, and facilitates future growth and adaptation.

Its essential to note that over-normalizing can lead to performance issues, especially with complex joins in queries. Therefore, it's sometimes practical to denormalize data for efficiency.