Comment pouvez-vous utiliser des modèles dimensionnels pour améliorer les décisions commerciales ?
Informatique décisionnelle (BI) est le processus qui consiste à transformer les données en informations qui peuvent vous aider à prendre de meilleures décisions pour votre organisation. L’un des aspects clés de la BI est la modélisation dimensionnelle, une technique qui organise les données dans une structure logique facile à interroger et à analyser. Dans cet article, vous apprendrez comment utiliser les modèles dimensionnels pour améliorer les décisions commerciales en comprenant les avantages, les composants et les types de modèles dimensionnels.
Les modèles dimensionnels sont conçus pour prendre en charge une analyse rapide et flexible des données sous différents angles et niveaux de détail. Ce type de technique de modélisation des données présente plusieurs avantages par rapport aux autres, tels que la simplification de la structure des données en séparant les faits des dimensions, l’agrégation et le filtrage efficaces des données via des hiérarchies et des catégories, la facilitation des requêtes intuitives et conviviales grâce à des termes et concepts commerciaux courants, et l’amélioration des performances et de l’évolutivité en diminuant le nombre de jointures et en optimisant le stockage et l’indexation des données.
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A well built dimensional model can be the cornerstone of long term growth and success for an organization. The unique ability to afford complex analysis and trend discovery without the need for complex technical skills can help key stakeholders drive critical business decisions with confidence. If setup properly, the same model can also be utilized for validation of decisions and long term tracking of A/B testing results. For the technical analytical team and partners, the model should provide unrivaled performance and efficiency when conducting highly complex reviews, while also ensuring auto ingestion of new dimension items as created; limiting time to market for any new or changed viewpoints and categories.
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Alex Souza
Generative AI | Analista de Dados | Ciência de Dados | Mentor em Dados | Professor | MTAC
Costumo dizer que são bancos de dados otimizados para consulta (olap), enquanto os bancos de dados transacionais são otimizados para inserções, deleção e atualização (oltp). Sempre reforço a questão, consultas analíticas devem ser feitas em um modelo dimensional, obedecendo o conceito! Pois já vi diversos profissionais dizendo que usam uma modelagem dimensional e na verdade não! Para estes, recomendo a leitura do Kimball e do Inmon!
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Dimensional models enhance business decisions by organizing data into a structure that aligns with business processes. They provide a clear, intuitive view of information, facilitating better analysis and decision-making. By incorporating dimensions like time, geography, and product categories, businesses gain insights into trends, patterns, and relationships. This model simplifies complex data, enabling users to easily query and extract relevant information. Additionally, dimensional models support scalability, making it easier to adapt to changing business needs. Overall, they empower organizations to make informed decisions, optimize processes, and respond more effectively to market dynamics.
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Dimensional models organize and structure data for easier analysis and insight. They are useful for large amounts of data and multiple dimensions. Some of the benefits are as follows: - Query performance: faster and better for complex queries. - Data models: simpler and clearer for users and developers. - Report and analysis: more flexible and detailed for business questions.
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Query Performance: Designed for optimal query performance, dimensional models support faster and more efficient retrieval of data, especially in analytical scenarios common in business intelligence. Flexibility : Dimensional models are adaptable to changes in business requirements. It is easier to add or modify dimensions and measures without significant disruption to existing structures. Aggregations: Dimensional models inherently support aggregations and summarization of data, facilitating the generation of reports at various levels of granularity. Ease of Understanding: Dimensional models are intuitive and closely mirror how business users perceive and understand data, making it easier for them to navigate and analyze information.
Les modèles dimensionnels se composent de deux composants principaux : les tables de faits et les tables de dimensions. Les tables de faits stockent les valeurs numériques qui représentent les faits, tels que les ventes, les revenus ou les bénéfices. Les tables de dimension stockent les attributs descriptifs qui définissent le contexte des faits, tels que le produit, le client ou l’heure. Chaque table de faits est liée à une ou plusieurs tables de dimension par des clés étrangères, créant ainsi un schéma en étoile ou en flocon de neige.
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Alex Souza
Generative AI | Analista de Dados | Ciência de Dados | Mentor em Dados | Professor | MTAC
Tabelas fato, costumo explicar que contém fatos que aconteceram (uma compra, uma venda..) Já as dimensões, são como desejo detalhar (explodir) o fato em questão (por data, por cliente, por estado) Outra observação em relação a taba Fato, ela deve conter dados numéricos, datas e chaves para as dimensões! Uma tabela fato com o nome do cliente não é um fato! #dica
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A dimensional model has 2 major components. 1 or more fact tables 1 or more dimension tables. Fact tables are tables with records of transactions. They typically would have columns that can be meaningfully aggregated. Dimension tables house data attributes. These attributes are the 4Ws. The who, what, when, and where attributes. These put together and connected using the common key columns creates a dimensional model
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Categorical Attributes: Dimensions are descriptive data elements that provide context to the measures. Examples include time, geography, customer, and product. Dimensions are often hierarchically organized, allowing for easy navigation and analysis.
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Details of Dimensions: Attributes provide additional details or characteristics about a dimension. For example, a "Time" dimension may have attributes like day, month, quarter, and year.
Les modèles dimensionnels peuvent être utilisés à diverses fins et scénarios, tels que l’analyse de la fréquence, du volume et de la distribution des transactions au fil du temps et entre les dimensions. Les modèles transactionnels capturent les détails de chaque événement ou transaction individuel qui se produit dans un processus métier. Les modèles d’instantanés périodiques capturent l’état d’un processus métier à intervalles réguliers, tandis que les modèles d’instantanés d’accumulation capturent l’historique et l’état d’un processus métier avec une séquence prévisible d’étapes. Tous ces modèles sont utiles pour analyser les tendances, les modèles, les changements dans les indicateurs clés de performance (Indicateurs clés de performance
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Alex Souza
Generative AI | Analista de Dados | Ciência de Dados | Mentor em Dados | Professor | MTAC
Em relação aos modelos, sempre comento dos tradicionais Star Schema e Snowflake. Star Schema, é composto de uma tabela fato rodeada de dimensões (formando uma "estrela") Já o Snowflake, é o Star Schema permitindo que dimensões tenham outras dimensões associadas! Costumo explicar assim de uma forma mais simples! Mas vai bem além disso!
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1. Star Schema: Central fact table surrounded by dimension tables. Dimensions directly linked to the fact table. Simplified structure for ease of use and performance. 2. Snowflake Schema: Central fact table linked to dimension tables, but these dimension tables may further normalize into sub-dimensions. Hierarchical structure with more complex relationships. 3. Galaxy Schema: Multiple fact tables share dimension tables. Enables more complex relationships and analysis across different business processes.
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There are 3 major schemas for dimensional models. Schemas are the pictorial representation of how the model is structured from the relationships that exist between the fact(s) and dimension(s) tables. 1. Star - a fact and multiple dimensions 2. Snowflake - a fact and multiple dimensions with dimensions falling off other dimensions as offshoots. 3. Galaxy or conformed - multiple facts comformed to speak to multiple dimensions
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Dimensional models organize and structure data for easier analysis and insight. They are useful for large data and multiple dimensions. There are two types: - Star schema: A single fact table connected to multiple dimension tables, forming a star-like shape. Easy, fast, and flexible. The relationship model used in this schema is usually one to many. - Snowflake schema: Some dimension tables normalized into sub-dimension tables, forming a snowflake-like shape. Reduces redundancy and improves quality, but increases complexity and slows queries.
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Handling Changes in Dimension Data Over Time: SCDs manage changes in dimension data over time. There are different types of SCDs, such as Type 1 (overwrite), Type 2 (add new version), and Type 3 (add a new attribute).
Lors de la conception de modèles dimensionnels, il y a quatre étapes principales à prendre en compte. Tout d’abord, vous devez identifier le processus métier et les questions auxquelles vous souhaitez répondre avec les données. Deuxièmement, définissez le grain ou le niveau de détail des faits que vous souhaitez mesurer et stocker. Troisièmement, identifiez les faits et les dimensions pertinents pour le processus opérationnel et les questions. Enfin, choisissez le type et le schéma du modèle dimensionnel qui correspondent le mieux à vos besoins et à vos sources de données.
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6 steps actually if you are building it out of a single flat denomalized table 1. Identify the table which has all the fields related to the business requirements for the project. 2. Write out all the column headers from the table on a paper 3. Split the column headers into values(typically columns that can be aggregateed) and reporting items (columns that can't be aggregated) 4. Look at the reporting items and out together those that are related into the same bucket. These naturally forms your dimension tables. 5. from (4), each buckets, identify the column with the lowest level of detail. This becomes your key to that table. 6. collate all the keys from (5) in addition to the values in (3) to create uour fact table.
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Alex Souza
Generative AI | Analista de Dados | Ciência de Dados | Mentor em Dados | Professor | MTAC
O projeto de modelagem pode ser projetada de algumas maneiras, esse é um fator primordial! Como mencionado no artigo, as 4 etapas são fundamentais para o entendimento e projeto! Porque, a modelagem vai variar muito dependendo da necessidade! Muitos me perguntam, qual o melhor modelo, sempre digo, o que atende sua necessidade! Não existe melhor nem pior, existe o que lhe atende! Aqui sempre já penso na próxima etapa, aproveite a etapa 1, pra já ver com as áreas, qual a relação delas com as demais áreas da empresa! E já começo pensando em como podemos começar, montando um todo(dw)? Ou ir montando por partes (dm)?
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Tudo começa pelo requisito, saber quais são os principais indicadores, como a área de negócio acessa essa informação atualmente, se existe trabalho manual para chegar nesta informação, mapear as fontes de dados, com base nisso o modelo de dados é criado e uma vez validado pelo cliente se durante a implementação o cliente questionar se falta algum indicador ou alguma tabela e esta informação não estiver no modelo de dados, esta demanda entra como melhoria após a entrega do projeto.
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Dimensional models organize and structure data for easier analysis and insight. Steps to design them are: - Identify business requirements: Engage with stakeholders and end-users to gather insights into their reporting and analysis needs. - Identify grain: Define the level of detail at which the facts are captured. - Identify dimensions: Identify the characteristics by which you want to analyze your data. - Identify measures: Identify the numeric values that provide quantitative information about the business. - Define dimensional hierarchies: Identify and define the hierarchical relationships within each dimension. - Design fact tables: Design the fact tables based on the grain, dimensions, and measures.
Une fois que vous avez conçu et mis en œuvre votre modèle dimensionnel, vous pouvez l’utiliser pour effectuer différents types d’analyse et de reporting, tels que le découpage en tranches, l’exploration et l’enroulement, le pivotement, la comparaison et le contraste. Par exemple, vous pouvez sélectionner et afficher des sous-ensembles de données en fonction de valeurs spécifiques ou de plages de valeurs dans une ou plusieurs dimensions. Vous pouvez également naviguer à travers différents niveaux de détail dans une hiérarchie de dimensions, par exemple d’une année à un mois ou à un jour. De plus, vous pouvez faire pivoter les données pour modifier l’orientation et la présentation des faits et des dimensions. Enfin, vous pouvez comparer et contraster les données entre différentes dimensions, périodes ou scénarios.
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Alex Souza
Generative AI | Analista de Dados | Ciência de Dados | Mentor em Dados | Professor | MTAC
Depois de montado falo aos usuários consumidores, façam seu show! Mas sempre, no momento da entrega, forneço um treinamento ao usuário! Explicando tudo de forma detalhada e deixo documentado! Aqui reforço sempre a importância do feedback, tá fazendo sentido, precisa de mais informações? Como tá a performance?
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Tahmin Salmanov
Power BI Expert | Microsoft Certified Azure Data Analyst | AWS Cloud Practitioner
(modifié)I would strongly recommend reading book "The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling" by Ralph Kimball , Margy Ross to get full understanding of dimensional modeling and its benefits
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Disadvantages of Dimensional Modeling: Data Redundancy: Denormalization in dimensional models can lead to some degree of data redundancy, as certain information may be duplicated across different dimensions. Complexity in Maintenance: Over time, as business requirements evolve, maintaining and updating dimensional models can become complex. Potential for Inconsistency: Inconsistencies can arise if changes to dimensions or measures are not carefully managed, potentially leading to discrepancies in reporting. Resource Intensive: Building and maintaining dimensional models may require substantial computing resources, particularly in environments with large volumes of data and complex structures.
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Dimensional models enhance business decisions by structuring data for intuitive analysis. Incorporating dimensions like time or product, these models provide comprehensive views of operations. By leveraging measures and dimensions, businesses can gain insights into trends and relationships within their data. This approach facilitates a deeper understanding of performance metrics. In summary, dimensional models support informed decision-making through versatile and holistic data exploration.
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