What are the common data modeling patterns used in Business Intelligence?

Let's take a retail company using a star schema to analyze its sales data. The fact table, named "Sales," records each sale's amount, date, and quantity. Surrounding this are dimension tables: the Customer Dimension with information like name and age, the Product Dimension detailing product name and category, the Time Dimension breaking down the sale date into day, week, and month, and the Store Dimension with details like store location and size. These dimension tables are linked to the "Sales" table through foreign keys, allowing for efficient queries. This setup enables the company to analyze sales patterns, like total sales for a product category in a quarter, highest sales by store location, or buying patterns of different age groups.

Like a highway interchange with multiple access ramps all converging on a central junction, the star schema models data dimensions feeding into a fact table hub for rapid analytics traffic.

The Star Schema, renowned for its simplicity and efficiency, is a predominant data modeling pattern in Business Intelligence (BI). It features a central fact table housing key measures or metrics, surrounded by dimension tables with descriptive attributes. This configuration, resembling a star, facilitates straightforward and fast querying due to fewer joins and a clear data representation. Its resemblance to relational database schemas makes it easily understandable and popular in BI. Moreover, this model enhances data analysis, supporting complex analytical queries and enabling efficient aggregation and summarization of data.

Snowflakes is designed for the further decomposition of the dimension table into the granularity, where tables of sub-dimemsions are used to reduce redundancy in the data system and also to save storage spaces. However when it comes to writing queries and understanding the schema of snowflakes it becomes complicated because it will requires several joins of tables. SNOWFLAKES is mostly used when large dataset is involved.

Although, snowflake schema is widely used by BI professionals, there are certain limitations or negative consequences of using this kind of schema. For eg., reduced flexibility and increased complexity are 2 major disadvantages of this schema. Also, there is a high chance of data redundancy. The space saving is also usually insignificant You should avoid snowflaking of dimensions unless it’s absolutely required.

The snowflake schema trims data redundancy, making storage efficient. However, its complex structure, resembling a snowflake, can slow down queries due to increased joins, affecting user-friendliness. It's a balance between storage optimization and query performance.

Like a road network with local streets and intersections branching from larger boulevards and highways, snowflake modeling decomposes dimensions into normalized sub-layers for flexibility but can cause performance bottlenecks.

In an e-commerce platform using a snowflake schema, the central "ProductSales" fact table, detailing units sold and sales amount, links to normalized dimension tables like Product, Category, and Supplier Dimensions. This schema divides product details into sub-dimensions, like categories and supplier information, for more detailed analysis. While it aids in reducing data redundancy and improving quality, the complexity increases due to more joins. This setup allows the platform to analyze trends in product sales, assess supplier performance, and refine marketing strategies based on customer demographics and location, despite its complex querying process.

Each of these patterns has its advantages and disadvantages, and the choice depends on the specific requirements of the project, such as data volume, the nature of queries, and the available infrastructure. Knowing and understanding these patterns is key to creating effective BI solutions.

In a healthcare analytics scenario, a Data Vault model captures patient data evolution over time. Hubs store unique patient identifiers, while Links record relationships, like patient-to-treatment associations. Satellites detail patient attributes (age, diagnosis) and treatment specifics (type, duration). This model tracks changes, like new diagnoses or treatment alterations, without data loss. It's particularly effective for longitudinal studies, where understanding patient health trends and treatment outcomes over extended periods is crucial, showcasing the model's ability to adapt to evolving data while preserving historical context.

As vantagens do cofre de dados s?o que ele permite armazenar e rastrear todos os dados históricos e suas mudan?as, facilita a integra??o e a padroniza??o de dados de diferentes fontes e sistemas, e suporta a análise e a auditoria de dados de forma eficiente e confiável.

As desvantagens do cofre de dados s?o que ele requer um alto nível de conhecimento e habilidade técnica para implementar e manter, pode gerar um grande volume de dados e complexidade de modelagem, e pode exigir ferramentas e processos específicos para extrair e transformar os dados.

Consider a university's student information system using anchor modeling. Here, Anchor tables hold unique student IDs, Attribute tables capture dynamic data like major or GPA, changing over time. Tie tables link students to entities like courses or advisors, documenting relationships and their evolution. Knot tables categorize static data, like course names or department codes. This model efficiently manages historical student data, such as major changes or GPA progression, and relationships like advisor assignments, allowing for a clear, time-stamped overview of each student's academic journey.

Like road markers with embedded RFID tags to enable historical tracking, anchor modeling sticks to business data entities with temporal metadata ties for tracing lineage and evolution over time.

The Kimball, Inmon or Data Vault patterns are all relevant to the challenge of Business Intelligence, but are often (rightly) invisible to the data consumers. However, a level of understanding is essential for there to be an appreciation of the foundations needed to deliver 'good' data.

The data model used for a BI dashboard, portfolio, or semantic layer requires a balance between performance and accessibility. Typically, a star schema or snowflake schema is most performant because we want to limit the number of joins required to generate BI visualizations and metrics. Some developers lean towards creating a single amalgamated table, pre-joined and totally denormalized, but this also walks into performance issues due to index processing and sorting in BI platforms. The best strategy is to create the simplest possible model in your semantic layer, with the required data aggregated to the level of granularity necessary for success.