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How can you document your data model for easy reference and maintenance?

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Data models are essential for designing and managing data warehouses, as they define the structure, relationships, and meaning of the data. However, data models are not static, and they need to be updated and communicated as the data warehouse evolves. How can you document your data model for easy reference and maintenance? Here are some tips and best practices to follow.

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Hariharasudhan Balasubramanian

Data Insights & Innovation | HSBC Life
Claudio Bonel, M.Sc.

Professor | Pesquisador | Cientista de dados | Desenvolvedor de Machine learning | Inteligência artificial | Business…

1 Use a standard notation

One of the first steps to document your data model is to use a standard notation that is widely accepted and understood by your stakeholders. There are different types of data models, such as conceptual, logical, and physical, and each one has a specific notation. For example, you can use the entity-relationship (ER) notation for conceptual and logical models, and the unified modeling language (UML) notation for physical models. Using a standard notation helps you avoid ambiguity and confusion, and ensures consistency and clarity across your data model documentation.

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Sanjeeb Mohapatra

Data Solution Architect - AWS + Databricks | Data Modernization | Passionate about Data and Analytics | Organizer - Data and AI Meet up - Bhubaneswar
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As data warehouse is the central repo for answering all business queries and it constitutes large number of tables, it is very important to follow standard naming convention during the model preparation. For different steps like building conceptual, logical and physical the entities and the attributes should follow the standards for easy understandable. For example in physical model, usually for star schema model, dimension tables should be prefixed as dim_ and fact table should be prefixed as fact_. The column names should be given for easy understanding . This will make end users to identify the objects easily and develop their BI and analytics reports quickly as well.

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2 Include metadata and annotations

Another important step to document your data model is to include metadata and annotations that describe the attributes, constraints, and business rules of your data model. Metadata is the data about the data, such as the name, type, format, source, and description of each data element. Annotations are the comments or notes that explain the rationale, assumptions, or limitations of your data model. Including metadata and annotations helps you provide context and meaning to your data model, and makes it easier to validate, verify, and audit your data model.

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3 Generate data dictionary and glossary

A data dictionary and a glossary are useful tools to document your data model and facilitate communication and understanding among your data warehouse users. A data dictionary is a comprehensive list of all the data elements in your data model, along with their metadata and annotations. A glossary is a list of all the terms and concepts used in your data model, along with their definitions and synonyms. Generating a data dictionary and a glossary helps you standardize and simplify the terminology and nomenclature of your data model, and avoid confusion and inconsistency.

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Satish Lepakshi

Technical Product Management |Product Analytics| Business Intelligence
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There needs to be multiple versions of glossary which caters to 1. Developers/Data engineers- more technical 2. Product team- mix of technical and business 3. Stakeholders- more business oriented information

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4 Use a data modeling tool

A data modeling tool is a software application that helps you create, document, and manage your data model in a graphical and interactive way. A data modeling tool can help you automate and simplify many tasks related to data model documentation, such as generating diagrams, metadata, data dictionary, glossary, and reports. A data modeling tool can also help you validate, test, and optimize your data model, as well as integrate it with other tools and systems. Using a data modeling tool can help you save time and effort, and improve the quality and accuracy of your data model documentation.

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5 Update and version your data model documentation

The last step to document your data model is to update and version your data model documentation as your data warehouse changes and grows. Your data model documentation should reflect the current state and structure of your data warehouse, and capture any modifications or enhancements that you make to your data model. You should also keep track of the history and evolution of your data model, by assigning version numbers and dates to your data model documentation. Updating and versioning your data model documentation helps you maintain and monitor your data warehouse performance, and ensure alignment and compliance with your data warehouse requirements and goals.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Hariharasudhan Balasubramanian

Data Insights & Innovation | HSBC Life
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Bridge the gap: Move beyond technical jargon and instead explain how the data model directly impacts business decisions and objectives through real-world examples and relatable scenarios. Empower wider audience: Focus on the "why" before the "how" by starting with business processes and use cases, making the data model accessible and meaningful even for non-technical users.

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Claudio Bonel, M.Sc.

Professor | Pesquisador | Cientista de dados | Desenvolvedor de Machine learning | Inteligência artificial | Business Intelligence | Python | SQL | Power BI | CSD | PhD Student
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Ora ora fico feliz em ver a documenta??o sendo a estrela de um artigo colaborativo do Linked In! Ela que, nos último tempos, tem sido julgada, xingada e até joga para escanteio mas, que de fato é ESSENCIAL para o sucesso do seu projeto. Em engenharia de software, qdo trata-se da garantia da qualidade, vamos aprender sobre "gest?o da configura??o de software". E, acredite, o modelo de dados é total parte da configura??o de software, portanto N?O negligencie a Engenharia de software no desenvolvimento do seu sistema de apoio à decis?o. Serinho, vai por mim. Modelo conceitual (MER) e Modelo lógico s?o excelentes técnicas e ferramentas para você documentar seu modelo! Dúvidas? Chamaí!

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How can you document your data model for easy reference and maintenance?

1

2

3

4

5

6

1 Use a standard notation

2 Include metadata and annotations

3 Generate data dictionary and glossary

4 Use a data modeling tool

5 Update and version your data model documentation

6 Here’s what else to consider

Data Warehousing

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How can you document your data model for easy reference and maintenance?

1

2

3

4

5

6

1 Use a standard notation

2 Include metadata and annotations

3 Generate data dictionary and glossary

4 Use a data modeling tool

5 Update and version your data model documentation

6 Here’s what else to consider

Data Warehousing

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