How do you measure and improve data model documentation version control performance and efficiency?

Data model documentation version control is the process of managing the changes and revisions of your data models and their associated documentation. It helps you keep track of the history, purpose, and logic of your data models, as well as the dependencies, relationships, and constraints among them. Data model documentation version control can improve the quality, consistency, and usability of your data models, as well as facilitate collaboration and communication among data modelers and other stakeholders. But how do you measure and improve the performance and efficiency of your data model documentation version control process? Here are some tips and best practices to help you.

1 Define your goals and metrics

Before you can measure and improve your data model documentation version control process, you need to define what you want to achieve and how you will measure it. For example, you may want to increase the accuracy, completeness, and clarity of your data model documentation, or reduce the time and effort required to create, update, and review it. You may also want to align your data model documentation with your business requirements, data governance policies, and data quality standards. To measure your progress and performance, you need to identify and collect relevant metrics, such as the number and frequency of changes, the number and severity of errors, the level of compliance, the feedback from users, and the impact on data analysis and decision making.

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Carlos Fernando Chicata

Data Engineer | AWS User Group Perú - Arequipa | AWS x3 |
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El control de version de documentación de los modelos de datos depende directamente de las politicas de gobierno de datos junto a los requerimientos del negocio: no hay mejoras al modelo de datos si estos 2 puntos no estan definidos claramente. Los objetivos y metricas se relacionan fuertemente con ambos puntos; algunos ejemplos son: > (Objetivo): Poder garantizar que los cambios en la documentación del modelo de datos completamente documentados en todas sus partes. > (Metrica) No se generará un cambio al modelo de datos, si no se tiene un ticket del negocio sobre el cambio aprobado por el equipo de datos y debe estar adjunto su modelo de datos propuesto o sql scripts que realice el cambio.

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2 Choose the right tools and methods

The tools and methods you use for data model documentation version control can make a big difference in your performance and efficiency. You need to choose the ones that suit your data modeling needs, preferences, and environment. For example, you may use a data modeling software that has built-in documentation features, such as annotations, descriptions, glossaries, diagrams, and reports. You may also use a version control system that allows you to store, manage, and compare different versions of your data models and documentation, such as Git, SVN, or Mercurial. You may also use a documentation format that is easy to create, update, and share, such as Markdown, HTML, or XML.

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Nandakishore Babu Raut
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Lot of time the tool selection depends on the 1. size of the organization 2. their ability to spend 3. Size of data model 4. Frequency of changes. Tools like ER Studio, Erwin and IBM infosphere data architect do provide a good documentation features to track changes and manage versioning. But they do cost a bit. If the models are huge and would see lot of changes over time it would be still wise to go for these tools even if they cost. If the models are small then versioning and changes can be still tracked on excel

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3 Follow the best practices and standards

To improve your data model documentation version control process, you need to adhere to certain best practices and standards. This will help enhance the quality, consistency, and usability of your data models and documentation. Clear and consistent naming conventions and terminology should be used for the data models and documentation elements. Metadata, assumptions, rules, and definitions should be documented for the data models and components. Additionally, changes, revisions, and reasons for the data model and documentation updates should be recorded. Furthermore, dependencies, relationships, and constraints among your data models and other data sources should be documented. Visual aids such as diagrams, charts, and tables can help illustrate the data models and documentation. Comments, annotations, tags can explain or highlight the data models and documentation. Templates, styles, formats can organize and present the data models and documentation in a cohesive manner. Lastly, references, citations, sources can support and validate the data models and documentation.

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4 Review and update regularly

To measure and improve your data model documentation version control process, you need to review and update your data models and documentation regularly. You need to check for errors, inconsistencies, gaps, and redundancies in your data models and documentation, and correct them as soon as possible. You also need to update your data models and documentation to reflect the changes in your data sources, business requirements, data governance policies, and data quality standards. You also need to solicit and incorporate feedback from your data modelers and other stakeholders, such as data analysts, data engineers, data stewards, and business users. You also need to compare and evaluate different versions of your data models and documentation, and identify the best ones for your current and future needs.

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5 Learn and improve continuously

To measure and improve your data model documentation version control process, you need to learn and improve continuously. You need to monitor and analyze your metrics, results, and feedback, and identify the strengths, weaknesses, opportunities, and challenges of your data model documentation version control process. You also need to benchmark and compare your data model documentation version control process with the best practices and standards in your industry, domain, and organization. You also need to explore and experiment with new tools, methods, and techniques that can enhance your data model documentation version control process. You also need to share and exchange your knowledge, experience, and lessons learned with your data modelers and other stakeholders.

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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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Sunandan Das

Data Management and Insights
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Data models represent the business capability or subject area with entities that are integral to it. Any model should reflect the capabilities and its processes, citing relationships and identifiers. Systems architects and designers should be able to resonate with the data model for data life cycle management. Business SMEs should be able to correlate business architecture from the data models to have use cases defined on it.

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How do you measure and improve data model documentation version control performance and efficiency?

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1 Define your goals and metrics

2 Choose the right tools and methods

3 Follow the best practices and standards

4 Review and update regularly

5 Learn and improve continuously

6 Here’s what else to consider

Data Modeling

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