You’re a data warehousing professional. How can you innovate to improve data visualization?

As a data warehousing professional, there are several innovative approaches you can take to improve data visualization. Here are couple of ideas: Interactive Dashboards: Develop interactive dashboards that allow users to explore data dynamically. Users should be able to filter, drill down, and customize visualizations according to their specific needs. Implementing interactive feature can enhance user engagement and understanding. Natural Language Processing (NLP): Implement natural language processing capabilities to enable users to interact with data visualizations using natural language queries and commands. Develop chatbot-like interfaces that allow users to ask questions about data and receive visualizations as responses.

By not getting into data visualization since you are a data warehousing expert and not a BI expert. Best thing you can do is to practice best data modelling practices and build models that are specifically designed for the BI use case at hand. There is no one modelling technique that works for every use case where a denormalized model may be a better fit for high volume customer facing apps due to performance advantages vs. Star Schema for internal BI folks which allows flexibility with the expense of perfomance. Visualizations generate a ton of queries at the DWH levem so you need to have the right platform & the right data models that can support the concurrency & performance requirements.

You're a power plant engineer. Q. How can you innovate to improve personal computers? A. Do not use AI to generate questions.

As a data warehouse professional, innovation in data visualization involves leveraging advanced analytics, interactive dashboards, and real-time data integration. Incorporating augmented reality or virtual reality technologies can create immersive experiences, while storytelling techniques and collaboration with design experts enhance both aesthetics and usability. Exploring novel visualization methods like network graphs and ensuring accessibility for all users are crucial aspects. Continuous learning and adaptation to the latest trends ensure that visualizations remain cutting-edge and effective in conveying insights to a diverse audience.

Data visualization is not only about the data, but also about the audience. You need to understand your data and audience to create effective and relevant visualizations that meet their needs and expectations. For example, you need to know the context, purpose, and message of your data, as well as the format, medium, and channel of your visualization. You also need to know the profile, preferences, and goals of your audience, as well as the level of detail, complexity, and interactivity they require. You can also use techniques like user research, personas, scenarios, or user testing to better understand your data and audience.

Creating impactful visualizations isn't just about the flash; it's also about ensuring our data tells a true and clear story. This means adhering to best practices like selecting the right visualization types, using scales and colors judiciously, and labeling our charts clearly. It's also crucial to present data ethically, avoiding any distortion. Leveraging established guidelines, such as the Data Visualization Checklist or the Graphic Continuum, can help maintain high standards of clarity, accuracy, and ethical presentation.

Um ponto importantíssimo n?o é só apresentar bem os dados visualmente, eles tem que fazer sentido para o negócio e responder as quest?es que v?o auxiliar a resolver os problemas e permitir atender as expectativas da estratégia do negócio. Gerando valor real e n?o só apresentar números e gráficos.

Stay informed about new developments, techniques, and tools in the field of data visualization through ongoing learning and professional development. Provide alternative text descriptions for images and visual elements for users who rely on screen readers.

Seleccionar el tipo de visualización adecuado: Escoger el tipo de gráfico o visualización que mejor se adapte a los datos y al mensaje que deseas transmitir. Por ejemplo, utiliza gráficos de barras para comparar cantidades, gráficos circulares para mostrar proporciones o mapas para datos geoespaciales. Utilizar escalas y colores apropiados: Asegurarse de emplear escalas y colores que faciliten la comprensión de los datos. Evitar distorsionar la escala o utilizar colores que puedan inducir a errores. Etiquetar y anotar los gráficos: Incluir etiquetas claras y precisas en las visualizaciones para que los espectadores comprendan fácilmente la información presentada. Agregar anotaciones para resaltar puntos importantes

It's important to choose the right chart type. Select charts based on the data and the message you want to convey. Use bar charts for comparisons, line charts for trends, and pie charts for proportions, but avoid cluttering with too many elements.

Data quality and accuracy are crucial for effective data visualization. By implementing robust data validation processes, utilizing reliable data sources, and regularly updating datasets, we can ensure that visualizations remain accurate and pertinent.Interactive visualization tools empower users to delve deep into data, facilitating the identification of patterns, trends,and insights.Tools such as Tableau & Power BI offer advanced functionalities to craft dynamic,interactive dashboards tailored to specific business requirements.As the field of data visualization continues to evolve, it's essential to consistently evaluate the efficacy of existing tools and technologies and stay updated on developments to find opportunities for enhancement.

In my experience, working on collaborative projects with peers who have the same level of interest in the skill can help you learn a lot! Working on real-world projects provides valuable hands-on experience and fosters creativity and innovation through collaboration. And when you're working with friends, it becomes easier and much more fun.

There are several ways to innovate and improve data visualization from a data warehouse: 1. Make it more interactive with ability to drilldown into specific metrics. 2. Capture real time updates. 3. Advanced analytics integration with Machine learning or predictive analytics. 4. Customization Options for users to personalize. 5. Make it for all kinds of devices of all sizes like tablets, laptop, mobiles etc. 6. It should tell a story which is compelling and based on the narratives. 7. To be able to monitor data quality and provide alerts or notify when discrepancies are detected 8. Should be scalable and effective in performance i.e. Fast and efficient.

It's great learn what is an effective data storytelling techniques from the experts. But it is also very important to learn from what is effective to your stakeholders.

Consider education on the the old school principles such as Ralph Kimball's datawarehouse design principles and Lawrence Corr ModelStorming, this will add significantly to your value in this space.

I can say this is the bottleneck for every business. Half of the time, user even don't know what they need. This problem can be solved by multiple conversions, where user will try to elaborate their requirements & Analyst will see the complexity & limitations of their data models. When both the parties agrees & lock the requirements, then only a fruitfull & result oriented solution can be developed within the time frame. So understand the requirements as per audience and provide the solution as per available data is very challenging.

Visualization should be centered around the user experience, ensuring that it is intuitive, accessible, and actionable. (i) Engage in thorough user research to understand their needs, preferences, and pain points. Integrating these insights into your design process. (ii) Craft visualizations that empower users to seamlessly explore, analyze, and interact with data. This provides users and stakeholders confidence in their decision-making.

Most conversation around data visualization is trying to champion the virtues of one tool over another. The answer is and always have been thinking like an end user. Most users believe that a dashboard is the answer to all their questions. The truth is a great dashboard will get users to ask the right questions. If you can anticipate those questions, your visualizations will be more powerful. This all starts with understanding your audience.

Understanding your data and your audience is arguably the most important components in this situation. You need to know how the data is configured and what value it brings. Understanding your audience will provide you with the why so that you can build meaningful metrics and visualize them to provide them with actionable consumption. You may know you can provide value from data in a scatter plot but if you don’t know your audience will be consuming on a phone then you may end up building something that is not worthwhile. Your audience is your customer and your data is your product. You need to know them both well or you will be out of business quickly.

Understand your audience and tailor your visualizations to meet their specific needs. This means creating clear, intuitive, and relevant visualizations that address the specific questions and decisions your users face. Utilizing user feedback to continuously refine and improve these visualizations is crucial.

There is no single answer as this touches a wide area. However, in most of the cases, starts from some solid business user stories with acceptance criteria, explore with organisational tools first, showcase to business not just meeting expectations, acceptance but also innovate the idea of other benefits iteratively. This can bring audiences closure to the outcome, open up with new discoveries which can spin up to new innovations.

One of the key concepts to to experiment and gather more information about what your users want. Depending on the user audience you have sometimes requirements are so dynamic, that you cannot cope with maintaining the dashboards.. hence understanding your users is as important as understanding the data itself..

El mejor enfoque es Genera ideas: Comienza generando diversas ideas y conceptos de visualización que podrían comunicar efectivamente tus datos. No te limites a la primera idea que se te ocurra. Crea prototipos: Utiliza herramientas de bocetos, creación de prototipos o wireframing para desarrollar versiones preliminares de tus visualizaciones. Estos prototipos te permitirán probar diferentes dise?os y funcionalidades antes de invertir tiempo en su implementación completa. Prueba y evalúa: Una vez que tengas prototipos, pruébalos con usuarios o colegas para obtener retroalimentación. Observa cómo interactúan con la visualización, qué elementos encuentran útiles o confusos, y qué sugerencias tienen para mejorarla.

Conduct usability testing or user interviews to understand how your audience interacts with your visualizations and what aspects they find most useful or confusing. Conduct A/B tests or multivariate experiments to compare different variations of your visualizations and determine which ones resonate best with your audience.

Oh yes!!! You just finished your data visualization and are ready to show your client for the first time. Just know that they will request multiple changes on the first go around, and there may be more changes as they dive into it. Some will be easy, and some will be very difficult. This is normal. As you learn and build more and more reports, you understand what to expect when delivering the first run product. Then you will start to see fewer change requests to the work you have put in because you will have learned to anticipate your client's requests and needs. But that can only be done by talking to them and learning more about their work. So, experiment and iterate repeatedly. Get to know your client to determine what is required.

Knowing what data to visualise , it’s golden source , referencing catalog on definitions to ensure correct attributes are getting pulled are few of important pre cursors. Again do you need data in raw format or transformed and purpose driven visualisation is way to go and data product solutions can help in that space.

Data Visualization always help to understand the Data better. They are lot of tools in market that will create fancy dashboards. But in order to create dashboards some one with that skill should be needed. How about building a workspace where user asks a question and the platform or framework will build a report. I see that is Innovation

When talking about data visualization, there is an element I rarely see mentioned: pre-processing of the data. You can build your best visualization, but if it takes 30 minutes to refresh, you pretty much lost your audience. So, for those recurrent visualizations, instead of breaking the bank with the best possible server, crunch and organize your data in the appropriate structures designed to better serve your visualizations.

As a data warehousing professional, innovate data visualization by creating interactive dashboards, incorporating advanced techniques like AR/VR, and integrating machine learning for predictive analytics. Use storytelling elements, implement NLP for intuitive querying, and optimize visualizations for mobile devices. Foster collaboration and embrace real-time data streaming for up-to-date insights, ensuring user-friendly designs based on data-driven insights into user behavior.

Creating an autonomous business intelligence platform capable of handling the entire data lifecycle—from ingestion and validation. Users across the organization will be able to share, and comment on data, fostering collaborative analysis. Most importantly, platform should be designed with a focus on user experience, making it accessible and valuable for non-technical business users. This approach ensures that insights can be leveraged those who need, without requiring technical knowledge.The platform represents the next step in data management evolution, transitioning from DW and data lakes to data lakehouses, and ultimately integrating the concepts of data mesh and data fabric for a more interconnected and flexible data ecosystem.