How can you build adaptability when working with data visualization?

Data visualization requires strategic integration of design, technology, and processes. Understand your audience and context to tailor visualizations. Choose flexible tools that support various data sources and formats, and ensure responsive design. Data architecture and integration are key for adaptability. Uphold data governance and quality standards for trust and adoption. A data-driven culture and collaboration improve visualization design. Consider deployment and usage, allowing customization and interaction. A skilled team in data science, design, and user experience is crucial for adaptability.

Before jumping into working on data visualization, always start with understanding the question your audience is trying to answer. This will save you some time by helping you focus on the right data and avoid the pitfall of presenting too much data which is counter-productive for your audience. Understanding the context of how they plan to use or interact with the data is also important. It will help you determine how much control and details your data visualization need to provide. For example, if the nature of your audience interaction with the data is deep exploration then you may want to consider the right level granularity and the type of control functions (e.g., filters, slicers, drills) to help them do their job well.

Knowing your audience is key to crafting effective data visualizations. It's like customizing a solution to perfectly fit their needs, ensuring your visualization resonates with their understanding, addresses their pain points, and aligns with their goals. It's not just about presenting data; it's about creating a meaningful and tailored experience for your audience. It’s important to remember that the needs go beyond the obvious and non functional needs are also discussed .

When creating visualizations, understanding the audience and the story you want to tell is essential. Visualizations should be clear, intuitive, and informative, allowing viewers to grasp the main points quickly. Additionally, knowledge of visualization techniques and best practices enables you to choose the most appropriate chart types, color schemes, and layouts to convey your message effectively. Overall, mastering data visualization empowers you to present information in a compelling and impactful way, driving better understanding and decision-making.

To increase adaptability of your data visualization start by treating it like any other customer facing product in Product Management life cycle. Steps will then come easily: 1. Identify Personas: Stakeholder vs End User 2. Conduct user interviews 3. Frame the Business goal 4. Dashboard requirements template 5. Prioritization by Data quality, Business Value and Effort 6. Aligning on Dashboard vision and strategy 7. Layout design 8. Build and launch iteratively

Different types of data require different visual representations to effectively convey the intended message. For example, bar charts are great for comparing values, while line charts work well for showing trends over time. By selecting the appropriate visualization, you can ensure that your audience grasps the key insights quickly and accurately. This not only enhances comprehension but also makes the information more engaging and memorable. Ultimately, the right choice of visualization can significantly improve the effectiveness of your communication and decision-making processes.

Dependendo dos dados e do objetivo, escolho diferentes tipos de gráficos. Para tendências ao longo do tempo, linhas ou áreas; para compara??es, barras ou colunas. Ferramentas como o Microsoft Power BI, Looker, Qlik, Tableau, Quicksight oferecem uma variedade de op??es de visualiza??o. Fica a dica! Mas lembre-se, o tipo depende do que quer mostrar!

One size does not fit all so carefully consider and choose the right format for your visualization that will best tell the story and answer key questions generated by data—all of it connecting with your main purpose. Sometimes even combining related charts is helpful; it can fuel deeper exploration that leads to helpful business insights and answers that driven action.

The following are essential tactics to improve data visualization's adaptability: Build a Solid Foundation in Design Principles: Developing a solid understanding of fundamental design concepts like hierarchy, balance, contrast, and colour theory enables the creation of powerful and flexible visualizations. Promote a Data-Driven Culture: . Iterative Approach: . Learn a Variety of Tools: Recognize Your Personas and Audience: . Keep up with Trends: Use responsive design. Acquire the skills of storytelling with statistics: Put an emphasis on interactivity. Develop Critical Thinking Skills

Defendo que o visual n?o precisa ser bonito e sim eficiente. Logicamente precisa ser agradável visualmente para n?o causar nenhum inc?modo no usuário.

Look for interactive features from web designs that helps in displaying a lot of information within the given/same space (for example: accordion menu) and try to replicate it in Tableau. It is important to keep oneself updated on all the latest features that Tableau brings out and make use of them to enhance the user experience (for e.g. Dynamic Zone Visibility is a game changing feature that eliminates a lot of workaround solutions).

Studies show that people like visualizations better when they use the same colors, easy-to-read fonts, and clear labels. Also, following good design rules, like keeping things neat and not tricking people with the way things are shown, helps people understand and remember the information in visualizations much better.

Manter um design consistente (cores, fontes, layout) ajuda na compreens?o. Por exemplo, uso sempre a mesma cor para representar uma métrica específica em todos os gráficos. Isso facilita a leitura e a compara??o.

And while choosing colors and theme for your visualisation you must know your audiance because different colors means different meanings in different cultures. In short, no matter how good looking your visualisations seems but if aint conveying the real meaning its useless.

There is no magic formula for choosing a coherent and meaningful design. Regarding the presentation of information: - Prioritize clarity, using a simple and legible design (data/ink ratio). - Limit the amount of information on screen. - Highlight what is important and allow interaction to explore the data in depth. - Tell a story with your data, to make the visualization memorable. I recommend reading some books on functional design and studying Gestalt theory and its laws applied to visualization design. Once you are clear on how you want to present the information, it is time for the data modeling phase, usually in the form of a star following the Kimball methodology.

It allows viewers to engage with the data by filtering, drilling down, or adjusting what they see according to their interests. This level of interaction makes complex information more accessible and understandable, as users can explore data at their own pace and focus on what matters most to them. Interactive features also help in uncovering hidden insights, as users can identify patterns, trends, and outliers that might not be immediately apparent. In summary, interactivity transforms static charts into dynamic tools, making data exploration more intuitive, personalized, and insightful.

Enhance data visualization adaptability by incorporating interactive features like filters and sliders, enabling users to customize views. Utilize tooltips and drill-downs for deeper insights, while animations enhance engagement. Ensure responsiveness by implementing dynamic elements like zooms and buttons. Accommodate diverse user needs by offering flexibility in adjusting visualization parameters. Seamlessly integrate interactivity to create a user-friendly experience, allowing users to explore data effortlessly.

Integrate interactive elements such as tooltips, zooming, panning, and hover effects to allow users to explore data points and gain deeper insights. Interactive features can improve engagement and enable users to interact with the data dynamically. Implement filtering and selection options to enable users to customize their views and focus on specific subsets of data. Interactive filters based on categories, time periods, or numerical ranges can enhance usability Create linked visualizations where interactions in one chart or graph dynamically update other related visualizations. For example, selecting a data point in a scatter plot could highlight corresponding data points in a parallel coordinates plot or update a summary table.

Parameterize anything you can think of that may be used in filters OR detail in reporting. Tools like PowerBI have become more and more flexible in allowing the users to define their own requirements. Setting up a modern data warehouse design becomes key to delivering high-level and atomic levels of detail while providing a stable and responsive interactive environment, preventing timeouts and server availability issues.

This is a double edged sword. It is vital to allow drill downs, text highlights with the "Mouse Over" however restraint is needed to ensure interactions do not create distractions. User based design as well as testing will help address what is value VS over embellished visuals.

Don’t be scared to try an alternative view of what the business wants. There is a bias in organisations that they want to see the data in the same way each time as that’s how it’s always been. Deliver what they want and maybe an alternative view that brings to the fore. You may just find the users don’t like the alternative, or maybe just maybe you find a new way to convey an important message to a broader audience.

Good data visualizations don't just show information, they let you and hence your audience to play with it! This helps you understand the data better and make smarter decisions.

Nada sai perfeito de primeira. Apresento um rascunho, recolho feedbacks, e ajusto. Isso é fundamental para garantir que a visualiza??o esteja atendendo às necessidades do usuário final (do usuário final!).

Sometimes you need to put the ego aside to realize that the right visualization(s) are the ones the customers love and use. Produce a quality product then seek feedback, it will make the product better while contributing to strengthening the relationship.

To get to really know your audience (their priorities, values, preferences), using an iterative approach to test and make marginal improvement will assure a product that fits for purpose, with high usability and utilization.

In my experience , Data Visualisation is as good as the Data Model on which it’s based. If the underlying Data model allows to easily build various slices & dices of data , allows to drill up & down without degrading performance then that makes it more usable. Value of Data Visualisation solution built should be measured in terms of how many users use it consistently to add value to their day to day decisions and course corrections in the respective business line. Ensure Data Visualisation story boards are built on a strong foundation of Data Modelling that is tied to the value addition you planned in first place for stakeholders. Keep iterating this cycle as business needs change & so does the underlying Data & Visualisation needs!

Attend conferences, webinars, and workshops related to data visualization to gain insights into new techniques, tools, and emerging technologies.

Build a good foundation. I recommend reading some books on functional design and study the Gestalt theory and its laws applied to visualization design. Learn how to model data correctly, the Kimball star modeling methodology will help you. Learn about the business you are going to generate visualizations about, if you don't understand it you won't be able to do a good job. Join communities where you can get feedback from other professionals (linkedin, meetup, discord) and follow references in the visualization world

En algunas ocasiones por ejemplo al usar Power BI pueden salir nuevos conectores con herramientas externas, además de la misma suite de Microsoft con Power Automate para poder automatizar procesos son de suma utilidad para crear un ecosistema de consumo de información quizás con Sharepoint y Microsoft Teams de forma más consolidada.

Continuously learning and updating your data visualization is essential for maintaining its relevance and effectiveness. Stay informed about the latest trends, tools, and techniques in data visualization. Learn from best practices and examples shared by others in the field. Keep track of changes, updates, and feedback on your data and message, and incorporate them into your visualization as needed. By staying proactive and adaptive, you can ensure that your visualization remains accurate, relevant, and valuable to your audience and purpose.

Além das técnicas, considero a narrativa. Uma boa história com os dados pode ser t?o importante quanto a visualiza??o em si. Contextualizo os dados, explicando tendências e anomalias, para que a visualiza??o fa?a sentido no quadro maior.

Ongoing monitoring of usage (Active vs Registered users), feedback, periodic review with key stakeholders is vital to keep them relevant. Key changes in business, parameters and process should trigger a review else this will result in Zombie visualisations not adding value and lurking in corners.

For both performance and adaptability, perform as much of the data transformations, including data cleaning, joining disparate data sources, and applying business logic, in the data warehouse. Then point the visualization tool to these modeled tables. This makes it much easier to switch visualization tools, because the processing logic exists in your transformation layer, not your visualization layer.

Understanding the context is key to making data visualizations adaptable. Consider elements such as data granularity, structure, and possible biases. It's crucial for data visualizers to be familiar with business objectives, data flow, KPIs, and the tech stack. Since the audience may request new designs, customized dimensions, and metrics on the fly, investing in a Data warehouse with detailed data is essential for adaptability in visualizations.

The first step to building adaptability while working with data visualization is to choose the right visualization based on the data available, variables, measures, and dimensions you choose and want to visualize. Visualizations have the ability to summarize large amounts of data into a graphical format. It is imperative that you choose the right visual to represent the metrics, kpis, or measures. Choosing the wrong visual may confuse the user and depict incorrect, ambiguous, or non-meaningful data, which in turn impacts decision-makers and may result in business failures.