What are the best ways to present data modeling projects in a BI portfolio?

Opter pour une variété de techniques de modélisation et mettre en avant l'impact commercial de ses réalisations peut renforcer l'attrait de son portfolio.

The initial step of curating a data modeling portfolio is crucial as it sets the foundation for showcasing your expertise and passions. Carefully choosing projects ensures that your portfolio accurately reflects your skills and leaves a lasting impression on potential employers or collaborators. Think of it like picking your best work examples. If you're into healthcare data, choose a project where you organized patient info effectively, showing what you're good at and what you like.

In the ever-evolving landscape of business intelligence (BI), the role of robust data modelling is paramount. Key elements are: Objectives Complex & Predictive Analysis Data Visualisation Scalability Profiling & Exploration Entity Relationship Modelling Normalisation & Denormalisation Predictive Modelling Techniques Agile Development Relationship Database Warehousing Solutions BI Tools Integration Machine Learning Libraries Improved Data Accuracy Enhanced Query Performance Increased User Adoption Proactive Decision Aim is elevating BI from reporting tool to a proactive strategic organizational decision-making. With growth in complexity and volume, equip businesses with the tools needed in the data-driven landscape of the future.

La mejor forma de presentar proyectos de modelado de datos en un portafolio de BI. Primero explicar las implicaciones e impactos de tus hallazgos y como pueden ser utilizados para la toma de decisiones, ser corto y preciso, siempre debe utilizarse un lenguaje claro, no técnico, que sea fácil de entender para la audiencia, al utilizar imágenes y gráficos que sean para a ilustrar y ayudar a comprensión de los hallazgos.

Start with a brief but comprehensive project overview. Clearly state the problem or business challenge the project aimed to address. Highlight the business goals and objectives to provide context for your data modeling work.

Clearly articulate the data requirements for the project. Specify the sources of data, data formats, and any data cleaning or preprocessing steps undertaken. If applicable, discuss how you gathered and cleaned the data, and any challenges or unique aspects of the data.

Importante documentar cada projeto de forma clara, incluindo seus objetivos, metodologias utilizadas, conclus?es alcan?adas e quaisquer impactos positivos mensuráveis. Essa abordagem proporciona aos potenciais empregadores ou clientes uma compreens?o profunda de suas capacidades em BI.

A?adiría: ?Impacto y Resultados: Incluye información sobre el impacto específico o los resultados que tus proyectos de modelado de datos lograron, como mejoras en la toma de decisiones o en la eficiencia operativa. ?Rol Personal y Contribuciones: Detalla tu rol específico en cada proyecto y destaca tus contribuciones personales, diferenciando tu trabajo del de los demás en proyectos colaborativos. ?Desafíos y Soluciones: Describe brevemente los desafíos clave que enfrentaste y cómo tus soluciones de modelado de datos los abordaron, destacando tu capacidad de resolución de problemas. ?Feedback o Reconocimientos: Si es aplicable, menciona cualquier feedback positivo o reconocimiento que hayas recibido por tu trabajo en estos proyectos.

To showcase your Data Modeling Skills in a Portfolio you can - ? Use diagrams and charts to illustrate entities and relationships ? Label elements like attributes, keys, and constraints clearly ? Explain optimizations made through star schema design ? Highlight hierarchies, dimensions, and measures ? Call out advanced features like role-playing dimensions ? Outline integration of various data sources and flows ? Summarize preparation steps like cleaning and transforming ? Include sample queried reports, describing the business utility ? Note assumptions, considerations, and simplifications ? Append descriptive records detailing project contexts

Including the actual data model designs in your portfolio is like providing a visual blueprint of your skills. It helps others see how you structure and organize information, making your expertise more tangible and impressive.

Explain your data modeling approach. Describe the methodologies and techniques used, such as dimensional modeling, entity-relationship modeling, or any specific frameworks like Kimball or Inmon methodologies. Discuss the rationale behind your chosen approach and how it aligns with the project goals.

Mettre en avant les innovations, optimisations et bonnes pratiques appliquées dans la conception renforce la démonstration de votre expertise en modélisation de données.

When deciding what to include in your documentation, relevance and clarity are king. You want to provide enough detail to ensure understanding without overwhelming with unnecessary information. Consider what your audience needs to know. For example, include the rationale behind a specific choice, but perhaps omit the exhaustive list of every alternative considered.

Skipping clear documentation in your data modeling portfolio is like showcasing a puzzle without the picture on the box. It makes it harder for people to understand your work and could lead to missed opportunities.

Showcase the schema design of your data model. Use diagrams (ER diagrams, star schema, snowflake schema, etc.) to visually represent the structure of the data model. Provide clear explanations of the relationships between entities, tables, and fields.

Para enriquecer la documentación del modelo de datos en tu portafolio: ?Visualizaciones del Modelo: Incluye diagramas o gráficos que representen la estructura del modelo de datos, facilitando su comprensión visual. ?Casos de Uso y Aplicaciones: Describe cómo se aplicó el modelo en situaciones reales, resaltando su funcionalidad y utilidad. ?Cambios y Versiones: Si hubo revisiones o actualizaciones del modelo, menciona estos cambios y explica las razones y los resultados. ?Feedback y Mejoras: A?ade comentarios o feedback recibido y cómo este influyó en la mejora o adaptación del modelo.

(English) It is very important to have a BI portfolio with multiple case studies so that you can interact in various dashboards and visualizations in business intelligence tools such as: Power BI, Tableau, Python, among others that respond to real situations. (Spanish) Es muy importante tener un portafolio BI con múltiples casos de estudio de tal modo que se pueda interactuar en diversos dashboards y visualizaciones en herramientas de inteligencia de negocios tales como: Power BI, Tableau, Python, entre otras, que den respuesta a situaciones reales.

For example, you can showcase a sample report illustrating how your model helped analyze sales trends or a dashboard that visualizes key performance indicators for a business. This gives viewers a tangible glimpse into the practical applications of your data modeling skills.

Detail the data transformation and ETL (Extract, Transform, Load) processes involved in your project. Explain how raw data was transformed into the final data model. Discuss any data cleansing, normalization, or enrichment steps undertaken.

Consider adding anecdotes about overcoming challenges in a specific project or insights gained from a unique data modeling scenario. For instance, you might share how you addressed unexpected data discrepancies or highlight a eureka moment where a particular model adjustment led to significant improvements. These stories provide a personal touch and showcase your problem-solving skills and adaptability.

Highlight the key metrics and Key Performance Indicators (KPIs) that your data model supports. Explain how these metrics provide insights into the business problem or challenge.

Consider sharing git links where your personal projects, if any, reside. Always make sure you don't give away sensitive information from your existing projects. Information that could be proprietary to the business. I was once working on data provided on apartments, commercial properties, shopping complexes, etc. We had 'tab' as the separator. As you can expect, a 'text' field had a 'tab' in it and right-shifted all the elements. We landed with an apartment complex with 2,000 floors. There was a column right before the 'no-of-floors' that was 'area-in-sq-foot' :-) Cleansing the input files before the load, and requesting the vendor to take care of the special character at source were some of the steps we took.

Cualquier proyecto de BI, debe ser presentado con un buen Dashboard. Hay que adecuar ese Dashboard al interlocutor o al proyecto que vayas a presentar ya que hace más interesante para el cliente ver una muestra del posible resultado del trabajo que nos soliciten..

Ao apresentar projetos de modelagem de dados em um portfólio de BI, é eficaz incluir: 1. Diagramas Visuais: Utilize diagramas claros e visualmente atrativos para representar o modelo de dados. 2. Descri??es Concisas: Forne?a descri??es breves, destacando as principais decis?es de design, como escolhas de chaves primárias, rela??es e tipos de dados. 3. Exemplos Práticos: Inclua exemplos de consultas SQL ou visualiza??es que demonstram como o modelo de dados é utilizado na prática. 4. Contexto de Negócios: Conecte a modelagem de dados ao contexto de negócios. 5. Resultados Alcan?ados: Destaque os resultados positivos. 6. Desafios Superados: Compartilhe os desafios enfrentados durante o processo de modelagem e como foram superados.