How can you standardize data modeling across projects and teams?

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Data modeling is the process of designing and documenting how data is organized, stored, and accessed in a database or system. It is a crucial step in any data analytics project, as it affects the quality, performance, and usability of the data. However, data modeling can also be challenging, especially when working across different projects and teams that may have different standards, preferences, and expectations. How can you standardize data modeling across projects and teams to ensure consistency, efficiency, and collaboration? Here are some tips to help you.

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Byron Tyers CA(SA)

Data Analytics & Visualisations Specialist | Digital Innovation & Consulting Manager | Power BI Certified

1 Establish a common vocabulary

One of the first steps to standardize data modeling is to agree on a common vocabulary for naming and describing data entities, attributes, and relationships. This can help avoid confusion, ambiguity, and duplication when communicating and documenting data models. A common vocabulary can also facilitate data integration and reuse across projects and teams. To establish a common vocabulary, you can use existing standards and conventions, such as ISO 11179 for metadata, or create your own glossary or dictionary based on your domain and context.

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Giovanni Dicandio

Senior Analytics Leader and BI specialitst | Advancing Corporate Strategy with Actionable Intelligence insights | Tableau | Python | QlikSense
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In my experience, I created a data vocabulary that was quickly adopted across several other analytics projects for being so easily scalable. However, a big task was to offer training sessions and other resources to ensure that all team members of the team/business were up-to-date with the latest data modeling techniques and standards. How I did do it? Workshops, dedicated in-person office hours, and where possible I provided my colleagues with access to key literature in the field.

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2 Follow best practices and guidelines

To standardize data modeling, it's important to follow best practices and guidelines for designing and developing data models. Doing so can help ensure that your data models are clear, accurate, and consistent, as well as optimize the performance, security, and scalability of your data systems. For example, you should use appropriate data modeling techniques and tools to represent and visualize data structures and relationships. Additionally, normalization and denormalization principles should be applied to balance data integrity and efficiency, avoiding anomalies and redundancy. Furthermore, primary keys, foreign keys, and indexes should be used to enforce data constraints and improve data access and retrieval. Finally, you should document your data models with metadata, annotations, and descriptions to explain the purpose, meaning, and context of data elements and their relationships.

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3 Implement a data modeling process

Standardizing data modeling requires a process that defines the roles, responsibilities, and activities involved in creating and maintaining data models. This process helps ensure that data models are aligned with business requirements, objectives, and standards, while also promoting collaboration and feedback among stakeholders and users. Additionally, it enables data governance and quality control by establishing rules, policies, and procedures for data validation, verification, and testing. The specifics of a data modeling process may vary depending on the project scope, complexity, and methodology; however, some common steps include defining the scope and objectives of the project, collecting and analyzing data requirements and sources, designing and developing the data models with appropriate techniques and tools, reviewing and validating the models with stakeholders and users, implementing and testing the models in a database or system, and monitoring and updating them as needed.

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Giovanni Dicandio

Senior Analytics Leader and BI specialitst | Advancing Corporate Strategy with Actionable Intelligence insights | Tableau | Python | QlikSense
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One thing I've found useful was to develop a set of data governance policies that outlined the standards for data quality, security, privacy, and ethical usage. Those sets of "rules" ensured that all teams adhered to the same guidelines and legal requirements.

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4 Use a data modeling standard

A final step to standardize data modeling is to use a data modeling standard that provides a set of rules, principles, and conventions for creating and documenting data models. A data modeling standard can help ensure that your data models are compatible, interoperable, and reusable across different projects and teams, as well as compliant with industry and regulatory requirements. A data modeling standard can also facilitate data exchange and communication, by enabling a common understanding and interpretation of data models among different data systems and users. There are various data modeling standards available, such as UML, ER, and SQL, or you can create your own custom standard based on your needs and preferences.

Standardizing data modeling across projects and teams can bring many benefits, such as improving data quality, performance, and usability, as well as enhancing collaboration, efficiency, and innovation. By following these tips, you can create and maintain consistent, reliable, and effective data models for your data analytics projects.

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5 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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Byron Tyers CA(SA)

Data Analytics & Visualisations Specialist | Digital Innovation & Consulting Manager | Power BI Certified
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As far as possible, have one source of truth. As organisations or data teams grow, it can be difficult to standardize everything. Visuals, colours, layouts, metrics etc. What's important is to make sure that the information used is consistent. So where possible, creating data models that various teams or people can use to ensure that the data is consistent throughout the different projects and teams, and having procedures and policies in place to ensure that new data models go through a vetting process first to see if an existing model can be used or altered before a new one is created.

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How can you standardize data modeling across projects and teams?

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1 Establish a common vocabulary

2 Follow best practices and guidelines

3 Implement a data modeling process

4 Use a data modeling standard

5 Here’s what else to consider

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