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How can you ensure the data model meets business requirements?

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Data models are essential for designing, developing, and maintaining databases that support business processes and objectives. However, creating a data model that meets the requirements of the stakeholders, users, and applications is not a trivial task. It requires careful analysis, communication, and validation to ensure that the data model reflects the reality and needs of the business. In this article, we will discuss some best practices and tips on how to ensure the data model meets business requirements.

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Maziyar Gerami

Backend Developer at Avan Software Technology Advisors | Part of Flickit Team
Adriano Reis

Master Data Quality and Management Assistant na Pierre Fabre Group

1 Understand the business context

Before you start designing the data model, you need to understand the business context of the data. What are the goals, challenges, and opportunities of the business? Who are the users and consumers of the data? What are the sources and types of data? How is the data used and transformed? What are the quality and security standards for the data? By answering these questions, you can gain a clear picture of the scope, purpose, and value of the data model.

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Maziyar Gerami

Backend Developer at Avan Software Technology Advisors | Part of Flickit Team
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As a developer, I should understand every aspect of the domain. I must be familiar with and use a set of tools known as Domain Driven Design (DDD). One of the tools in the DDD toolbox is Event Storming. It provides a comprehensive domain view, allowing business owners to give developers and technical stakeholders a clear perspective. This tool facilitates effective communication between business and tech teams.

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Nitish Sharma

Multi Cloud Senior Solutions Architect || Ex Kyndryl | IBM | Deloitte | IQVIA | ARIS Global | Bioclinica | GenAI - JLPT N2
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Absolutely crucial! To ensure your data model aligns with business needs, start by robustly defining those requirements. Understand the key stakeholders' goals and objectives. Collaborate closely with teams, employ data profiling and validation techniques, and iterate as necessary. Constant communication and feedback are key! And don't forget to stay agile to adapt to changing business needs.

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Suvidha Shri Joshi

Global Change Leader |Business Architect | HSBC| Standard Chartered |Credit Suisse| Mentor
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It’s important to have the big picture in mind when creating data models. An organisation usually starts with a pilot and that pilot is often not representative of the full complexity of the data needs of the organisation. It’s useful to study industry models, understand the business requirements of the organisation, the full suite of product offerings and keep that in mind when designing the data architecture. The data models should be as modular as possible and flexible enough not only to accommodate the complexity that the organisation is currently dealing with but also the needs of the organisation as it grows and encounters further complexity and variability.

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2 Define the data requirements

Once you have a good understanding of the business context, you need to define the data requirements. Data requirements are the specifications and constraints that describe what the data model should capture, store, and provide. Data requirements can be functional or non-functional, and they can be derived from various sources, such as business rules, use cases, user stories, workflows, data dictionaries, or existing data models. You should document and prioritize the data requirements using a consistent and structured format, such as a data requirements matrix or a data model diagram.

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Adriano Reis

Master Data Quality and Management Assistant na Pierre Fabre Group
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Dentro do contexto onde estou atuando, essa é uma das partes mais importantes (e por acaso é também onde eu mais me entusiasmo!) - entender o "lá na frente" pra desenhar melhor o que tá aqui no come?o. Alguém vai precisar do CEP com hífen? A descri??o dos SKUs pode conter acentos? Tem certeza que n?o vai atrapalhar o relatório lááá no pós-venda? Toda pergunta é válida, e toda valida??o é importante. Debruce-se sobre isso e tenha a certeza de um modelo de dados robusto e que atenda todas as demandas.

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3 Design the data model

After you have defined the data requirements, you can start designing the data model. The data model is a graphical or textual representation of the data structure, relationships, and constraints that define the data. There are different types of data models, such as conceptual, logical, or physical, depending on the level of detail and abstraction. You should choose the appropriate type of data model for your project, and follow the best practices and standards for data modeling, such as normalization, naming conventions, or data types. You should also use a data modeling tool or software that supports the data model format and features that you need.

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Sadhna Singh

Sr. Principal Consultant | GenAI & AI/ML Strategist | Lifelong Learner Driving Innovation
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Designing the Data Model: The Backbone of Success ?? In the dynamic landscape of data, the foundation lies in a well-crafted data model. It's not just about structure; it's about empowering insights, scalability, and informed decision-making. A thoughtful data model is the compass that guides us through the sea of information. Let's embrace the art of designing robust data models – the key to unlocking the true potential of our data-driven endeavors!

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4 Communicate and collaborate with stakeholders

One of the most important aspects of ensuring the data model meets business requirements is to communicate and collaborate with the stakeholders. Stakeholders are the people who have an interest or influence on the data model, such as business analysts, users, developers, or managers. You should involve them throughout the data modeling process, and solicit their feedback, input, and approval. You should also use clear and consistent terminology, definitions, and documentation to avoid confusion and ambiguity. By communicating and collaborating with stakeholders, you can ensure that the data model aligns with their expectations and needs.

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Hephzber Ifunanya Obiorah

Manager, Business Intelligence & Business Development | Financial Advisor | Wealth Manager
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These are steps one should take to communicate and collaborate effectively with stakeholders: 1. Research your stakeholders: It is important to understand the individuals you will be working with so as to create a mutually beneficial collaboration. 2. Identify what is at stake for everyone: Every stakeholder is involved in the project for a different reason. It is important to understand the motive of each person as well as their roles to ensure that the data model satisfies the needs of every stakeholder thereby, meeting all business requirements. 3. Demonstrate that every stakeholder input counts. This creates the feeling of inclusion. 4. Set specific goals and timelines for everyone to ensure timely completion of the data model.

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5 Validate and test the data model

Finally, you need to validate and test the data model to ensure that it meets the data requirements and the business context. Validation is the process of checking whether the data model conforms to the rules and standards that govern the data. You can use various techniques and tools to validate the data model, such as data profiling, data quality assessment, or data model review. Testing is the process of verifying whether the data model performs as expected and supports the data operations and functions. You can use various methods and tools to test the data model, such as data load testing, data query testing, or data performance testing.

By following these best practices and tips, you can ensure that the data model meets business requirements and delivers value to the business. A data model that meets business requirements is not only a technical artifact, but also a strategic asset that enables data-driven decision making and innovation.

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Sadhna Singh

Sr. Principal Consultant | GenAI & AI/ML Strategist | Lifelong Learner Driving Innovation
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"?? Data Modeling Tip: Validate and Test ??? In the world of data, precision is paramount. That's why validating and testing your data model is non-negotiable. It's the compass guiding decision-making, ensuring accuracy and reliability. Don't just build; validate. Test, iterate, and refine. Your insights are only as good as your data.

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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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How can you ensure the data model meets business requirements?

1

2

3

4

5

6

1 Understand the business context

2 Define the data requirements

3 Design the data model

4 Communicate and collaborate with stakeholders

5 Validate and test the data model

6 Here’s what else to consider

Data Management

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How can you ensure the data model meets business requirements?

1

2

3

4

5

6

1 Understand the business context

2 Define the data requirements

3 Design the data model

4 Communicate and collaborate with stakeholders

5 Validate and test the data model

6 Here’s what else to consider

Data Management

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