Here's how you can use strategic thinking to solve complex problems in data visualization.

Defining clear goals before embarking on data visualization is a critical step in creating effective and impactful visual representations of data. By establishing specific and measurable objectives, you provide a clear direction for your visualization design and ensure that it serves a purposeful role in conveying information to your audience. These goals guide your selection of relevant data sets, visualization techniques, and level of detail, facilitating a focused and intentional approach to data presentation.

Data visualization is a medium for story telling using data. Before starting the visualization, we need to define the story which we are designing to tell. Define the goal which is aligned with the story we want to project.

Defining clear goals is essential before embarking on data visualization tasks. It's crucial to establish what you want your audience to grasp or accomplish upon viewing your visualizations. By setting specific, measurable objectives, you can craft a purposeful and targeted design. Clarity in your goals influences the selection of data sets, visualization types, and the level of detail required for effective communication. It also facilitates the evaluation of your visualizations' effectiveness in achieving the desired outcomes, ensuring that your efforts are aligned with the intended goals.

Establishing clear goals for your data visualization is crucial for its effectiveness. It ensures that you're not just presenting data for the sake of it but rather to achieve a specific outcome or understanding. Setting measurable objectives helps in crafting a focused design that resonates with your audience and facilitates easier evaluation of its success later on.

Understanding the data always play an important role in the storytelling behind it. In order to present the story with your data, framing the data pointers, designing the KPIs always play a pivotal role.

While crafting or strategizing on building data visualization solutions, it is important to align with these three main factors for setting highly data immersive and engaging experience. Firstly, understanding the data (KPI’s / metrics to be visualized), which ‘enlightens’ you to set ‘engaging’ visualization experiences, which are highly intuitive and ‘self-explanatory’ for the end users, with a ‘narrative’ approach. Data storytelling is an art to represent data in an easy to interpret (understand), which helps translating for meaningful, and actionable outcomes.

"Know your data", is a crucial step in solving complex problems in data visualization. It involves gaining a deep understanding of the data, including its: 1. *Origin*: Where did the data come from? Was it collected through surveys, sensors, or other sources? 2. *Structure*: What is the data's format? Is it tabular, hierarchical, or unstructured? 3. *Quality*: Is the data accurate, complete, and consistent? Are there any missing or duplicate values? 4. *Variables*: What are the different variables or columns in the data? What are their data types (e.g., numeric, categorical, text)? 5. *Relationships*: How do the variables relate to each other? Are there any correlations or dependencies? 6. *Patterns*: Are there any patterns in the data?

I think this is the most important point to consider. Knowing your data is very key to solving problems with data visualization. If you don't know and understand the data you are working with, you might not get the best out of it. You won't be able to properly analyze the data for possible solution to the business problem.

Before embarking on data visualization, grasp a thorough understanding of your data. Explore its origins, format, and potential constraints. Identify any discernible patterns, trends, or irregularities that may impact your visualization strategy. This preliminary analysis guides the selection of the most suitable visualization type—be it a bar chart, scatter plot, or heat map—to faithfully depict your data. Emphasize clarity and transparency in your visualizations, aiming to facilitate comprehension rather than generate confusion or misinterpretation among your audience.

Selecting a tool for visualization is a crucial step in visualization. While some programming languages like Python & R and a few others offer us a chance to visualize and simultaneously manipulate or even model data, we need to see which tool is more comfortable for us to operate. This may be based on our domain expertise / the size of the data or any other factors. For huge datasets, I personally prefer Python, SAS, and other cases I use Tableau, Power Bi or looker to visualise my data. While the programming languages have their aura while creating the visuals, the visualization tools definitely have their own aesthetic looks. So I say based on our domain expertise, data size, and especially the goal, we need to choose the right tool.

This is a strong debate on selecting the right tool for visualization. I could relate this to situations like I see people from Chennai support RCB because of their favourite Virat is playing for that franchise. Similarly I have seen people working in one tool praise the other tool because of a feature that is not available in the one they are using but available in another tool and this is vice versa!! In my choice I would say select a tool and get familiarised every tool has its own advantages and disadvantages as well getting familiarised and creating customised output will definitely reflect in the overall look of the visualization no matter what tool is used.

Por lo general, optamos por especializarnos por una herramienta que nos permita desarrollar todo el proceso de análisis y visualización de datos, lo que puede limitar nuestras posibilidades de contar una buena historía. Si tenemos clara la importancia de elegir el visual adecuado para nuestra comunicación, entenderemos que hay herramientas que ofrecen visuales y personalizaciones que otras no, por lo que es importante expandir nuestro arsenal de herramientas de visualización de datos.

Selecting the appropriate tools is paramount for successful data visualization. Evaluate the complexity of your data and the extent of customization required. While some tools are suited for rapid, straightforward visualizations, others offer greater flexibility but demand a higher learning curve. Your decision should reflect your objectives and the level of technical proficiency available. Opting for the right tool streamlines the communication of your data narrative, eliminating unnecessary complexities and ensuring clarity in your visual representations.

Depending on the intricacy of your data and the narrative you intend to articulate, it is essential to select the appropriate tools for the task. For straightforward data, opting for a tool with minimal customization yet capable of effectively conveying your message is advisable. Overcomplicating simple concepts should be avoided. It is crucial to understand both your data and your audience when making such decisions.

Iterative design is indeed a strategic approach that fosters continuous improvement. Starting with a basic version of your visualization and gradually incorporating complexity based on feedback and insights ensures that your final product remains clear, accurate, and relevant. This method enables adaptability and ensures that your visualization evolves in alignment with your objectives and audience needs.

Make iteration a continuous process, the more you iterate with your users, the richer your visualization experience is delivered. Understand various aspects of your data, review progress and discuss innovative ideas and possibilities of data representations / visuals with your users.

Complex problems are hardly ever solved in one go which is why its important to get in an early prototype and get initial feedback. Refinements can then be made with different iterations.

Iterative design is essential in data visualization, starting with simplicity ensures clarity. As the visualization becomes clear and accurate, complexity can be gradually introduced. This strategic approach guarantees relevance and effectiveness throughout the development journey, leading to impactful insights.

O simples é trabalhoso, porém muito poderoso! Resultado não se dá pelo rebuscamento, poluição do output, mas pela usabilidade.

Ensure your data visualization connects with your audience by understanding their background, expertise, and viewing context. Customize complexity, language, and design to meet their expectations and needs. This tailored approach enhances audience engagement, fostering better comprehension and action based on the insights conveyed in your data visualization.

.Audience Understanding: Begin by thoroughly understanding your audience—their background, expertise, and the context in which they will interact with your visualization. Tailored Complexity and Language: Adapt the complexity and language of your visualization to match the needs and expectations of your audience. Design Elements and Visual Appeal: Pay attention to design elements that will resonate with your audience. Interactive and Immersive Experiences: Consider ways to make your visualization interactive and immersive, allowing your audience to actively engage with the data. Encouraging Action and Impact: Ultimately, the goal of engaging your audience is to inspire them to understand and act upon the insights presented.

Assess the journey map of an audience's interaction with your visualization. Irrespective of the tool, assess what someone notices in the visualisation. What stands out for them. How they shift their gaze. As data analysts/scientists we understand our data in and out and that makes us ignorant to a layman's view. I also advocate making visualizations interactive in any way possible for the same reason.

It is important to engage your audience, but the main component is to be able to tell the story with data. At this stage the only thing you have with data is a snapshot of what is happening, and maybe start some understanding of the response variables to analyze.

Engaging your audience is essential for effective data visualization. Suppose you're presenting healthcare data to a diverse audience, including medical professionals, policymakers, and the general public. Tailor your visualization to cater to each group's needs and preferences. For experts, include detailed charts and statistics to facilitate in-depth analysis. For policymakers, provide clear, concise summaries highlighting key trends and implications. For the general public, use intuitive visuals and plain language to convey complex information. By understanding your audience and customizing your visualization accordingly, you enhance comprehension and foster meaningful engagement with the data.

After completing your visualization, assess its success by evaluating whether it achieved your objectives and how the audience responded. Utilize this feedback to refine future visualizations and enhance your communication strategies. Continuous learning and adaptation are essential for strategic data visualization, enabling you to evolve your problem-solving approach and effectively convey insights through visual data.

More often than not, a good indicator that your dashboards and reports are being useful to the organization is if you regularly receive feedback from end users on improvement. It means that they are seeing the potential that your reports can take, and help build the bigger picture to better understand how the organization is performing and where else it can improve.

Reflecting on the success of your visualization is crucial for continuous improvement. By assessing whether it met your goals and understanding how your audience reacted, you can gather valuable insights for future visualizations. This feedback loop allows you to adapt and refine your approach, ultimately becoming more effective in communicating complex information visually. Continuous learning and adaptation are indeed essential components of strategic thinking in data visualization, enabling you to evolve and excel in this field over time.

Reflecting involves assessing the effectiveness of techniques, data representations, and communication methods used in visualizations. This introspective process helps identify strengths and areas for improvement. Adaptation, on the other hand, involves adjusting strategies based on reflections to refine approaches for future visualizations. This might include experimenting with different visualization types, improving storytelling techniques, or optimizing data processing methods. By cyclically reflecting and adapting, practitioners can enhance their skills and approaches, ultimately leading to more effective solutions for complex data visualization challenges.

Após você mostrar o seu relatório, procure pontos de melhoria e busque aprimorar cada vez mais a visualização! Sempre busque melhorar e colocar mais respostas para que o consumidor final entenda de forma simples!

Moreover, clearly defined goals enable you to assess the success of your visualization efforts and determine whether they have effectively communicated the intended message or insights to your audience. By aligning your data visualization strategy with your objectives from the outset, you can enhance the clarity, relevance, and impact of your visual representations.

Establishing well-defined objectives upfront aids in creating meaningful and impactful visualizations that resonate with your audience and support the communication of key insights.

One thing most people forget is to clearly state the limitations of your dashboard. Most real life data aren't clean enough and often contain many assumptions and caveats. Everyone has confirmation bias, and will always seek to see what they want to see. Therefore, you have to state the limitations upfront. Hopefully, that can help prevent people from making wrong decisions based on their flawed interpretation of your dashboard.

It starts with communication and collaboration and ends with communication and collaboration. It’s about being aligned. What’s the message you’re trying to get across? What does your audience or stakeholders need to know? What data do you have to available, does it answer the question or provide the answer that’s needed? If these are in place you can start to think about different ways of getting the insights across. Try different options, test them with colleagues and stakeholders and see which ones land best.

Un paso importante antes de llegar a una presentación con datos, puede ser validar como otros lo perciben antes de presentarlo a tu audiencia, esto podría ayudarnos a identificar aspectos que talvez merezcan mayor contexto. Hace poco estuve en una reunión donde el expositor tuvo que retrodecer 10 páginas para explicar la unidad de medida en que estaba expresado el gráfico presentado. Lo que para el pudo parecer obvio como conocedor de los datos, generó muchas preguntas en la audiencia, que cambiaron el rumbo de la presentación.