¿Cómo puedes utilizar la investigación de usuarios para identificar las necesidades de tu audiencia?

To reduce the bias in user research it is advised to use techniques that 'observe' participants rather than those that use the method of 'ask and tell'. Claimed data is almost always exaggerated and can lead to wrong hypotheses. Hence it is critical to incorporate principles of ethnography into user studies where-in the researchers integrate themselves into the life of the consumer as unobtrusively as possible.

Gathering requirements and Asking Right questions is a key before jump direct to solution - understand buisness logic and business domain -where data wil coming from -what form data will be in - anticipated scale and frequency of incoming data - important KPI

User research is essential: as in many other fields, understanding what the customer wants to achieve (their outcome) is essential to tailor data visualisation. Therefore, "understanding the customer" is required even before than starting to code or package up the data. Essentially, this means understanding the exam questions upfront, then preparing the most appropriate answer. Clearly, it's never an exact science and things (such as clients' demands) often change, hence agility is always key. However, focusing on user research, allows to save time and deliver quality output, where quality is measured in customer satisfaction.

The most important part of any visualisation is understanding who will be reading it and understanding how to visualise the information in a way they can absorb it. In medical communications data that is to be shared with a patient should be communicated in a way that gives reassurance, hope and an understanding of results and any possible side effects. For a Healthcare Practitioner they need more of a science-based data solution and would perhaps want to see in the data scientific proof. Knowing your audience is half of the battle.

The entire point of data visualization is to process (huge) quantities of data at a glance. Ask audiences what they see in the data visualization without providing them too much context. See if based on the visual(s) they're telling the story you want your users to tell to their peers and management. If the data visualization does not allow 95% of the users to correctly interpret the data and drill down into the root causes/key drivers, head back to the drawing board.

User research involves defining goals, selecting diverse participants, using balanced qualitative and quantitative methods, creating tailored materials, recruiting effectively, conducting sessions for open feedback, recording and analyzing data, iterating based on findings, sharing insights through clear communication, and applying learnings to drive meaningful changes in products or services.

To conduct UX research for data visualization, combine qualitative methods like in-depth interviews for detailed insights with quantitative surveys for broad feedback. Utilize observational studies and usability testing to evaluate user interactions and performance. From a UX governance perspective, ensure the study complies with GDPR, prioritize informed consent, and maintain strict privacy standards. Embrace ethical practices and cultural sensitivity while doing research Employ diverse sampling to avoid biases and manage and share data in accordance with data protection laws. This blend of methods and ethical considerations ensures effective and responsible UX research

As usual, the answer depends on what the outcome is - something that again underlines the important of user research. Depending on the outcome, I'd split user research in two macro areas: 1. a "loose hypothesis" area - especially valuable in early stages, when ideas need to be tested quickly and often (or need to be formed altogether!). Typically, here, qualitative interviews take precedence as the outcome is identify trends and insights. Hence, open questionnaires and interviews are common. 2. a "solid hypothesis" area - when hypothesis are stronger (but not necessarily corroborated yet). In this case, quantitative tests are more common: A/B testing, scenarios, or tasks - so that data can be used to corroborate or falsify the hypothesis.

Our business has two branches - dashboard products and consulting services. Dashboard products form 90% of our income and are far more profitable and scalable. On paper, we should be focusing fully on dashboards. However, we absolutely insist on keeping our consulting services because they are our touch point with our clients, their needs and their struggles. These consulting services are absolutely invaluable as user research. I can heartily recommend similar approaches - in job search parlance "learn or earn". Accept that for some clients, you lose out on the "earn" since the "learn" is so valuable.

Conducir una investigación de usuarios efectiva implica: * Definir Objetivos: Establecer metas claras para entender las necesidades y preferencias del usuario. * Selección Representativa: Elegir una muestra diversa que refleje a los usuarios finales, considerando diferentes perfiles y niveles de experiencia. * Entrevistas y Encuestas: Realizar entrevistas estructuradas y encuestas para obtener percepciones cualitativas y cuantitativas. * Análisis de Datos Demográficos: Examinar datos demográficos para identificar patrones y tendencias entre los usuarios. * Test de Usabilidad: Implementar pruebas prácticas con prototipos para evaluar la experiencia del usuario y recopilar retroalimentación directa. Ojo: Cada contexto es diferente.

User Research for Analytics and Visualizations use cases gives an insight, that otherwise is 'Guessed'. The data points received from research are invaluable, first hand, responses from End Users. The binary responses are easy to trace and analyze. It gives better idea deriving percentages from such responses. Whereas explanatory responses are tough to get Sentiment analysis. The most reliable method would be, to trace individual words and see which one are most frequently mentioned. Coming emergence of LLM and Natural Language, I guess, its not far when Descriptive feedbacks are also decoded in right way.

Yo sigo el siguiente proceso: 1. Identifico Patrones: Busco tendencias y comportamientos recurrentes en respuestas cualitativas y cuantitativas. 2. Segmento Datos: Agrupo datos según características comunes, comportamientos o preferencias para intentar comprender distintos perfiles. 3. Priorizo Hallazgos: Destaco los aspectos más relevantes y frecuentes. 4. Mapeo Customer Journey: Creo diagramas que ilustren el viaje del usuario, identificando puntos críticos y oportunidades de mejora. 5. Integro: Relaciono los hallazgos con los objetivos iniciales para garantizar que las propuestas aborden las necesidades identificadas. Haciendo bien este ejercicio podremos asegurar que las visualizacion se alineen con las expectativas del usuario.

Always ensure that you are able to separate your customer base into groups. You'll want to analyse your high-volume or high-spending base separately from the other. Demographics grouping is also important as it helps us understand the voice of customer not as a single voice but rather a diversified overview of opinions across user/customer types. Most companies don't spend too much time doing this and brand all their customers as the same which may lead to insufficient insights on understanding the user better.

A mi me ha resultado útil el hecho de entender profundamente el contexto de los datos, saber de qué tratan, comprender las variables cualitativas y cuantitativas, aquellas categorías implícitas, y aquellas también que enriquecerán nuestros datos, luego ya tendrás que utilizar técnicas de analítica como el business Intelligence para saber si lo que quieres es realizar análisis descriptivo o un analisis predictivo, y con ello de la mano ya desarrollas los cálculos adecuados y las visualizaciones precisas para transmitir la historia al usuario, es importante aquí mencionar que el diseño y la estética juegan un papel importante y clave a la hora de vender pero siempre será más importante como primer entregable un resultado útil y diciente.

Keep the end goal in sight: Always connect back to defining the core visualization objectives, key tasks enabled, and measurable outcomes. Let this direction anchor and filter analysis to what matters most.

User experience research insights that are uncovered from diverse data sets such as market research, social intelligence and business intelligence could lead to better understanding of the sub-segments within target audience and customise the content strategies or devise campaigns targeting their needs. Another interesting fact about the user experience insights is that it will help organisations to gauze the overall tonality among the diverse set of audience…. I agree that the personas can be generated based on the attributes of interest.

Por ejemplo, tuve la oportunidad de participar en un proyecto de visualización de datos para un equipo de ventas. La investigación de los usuarios destacó principalmente la importancia de tener un acceso rápido a métricas clave. Sin embargo, cada usuario ponderaba diferentes indicadores según su propio criterio. Aplicando esta data al proyecto, el modelo resultante presentó un panel personalizable, permitiendo a cada representante de ventas priorizar las métricas según sus objetivos individuales. Al incorporar directamente los aprendizajes de la investigación de usuarios, se crea una visualización de datos que no solo es estéticamente atractiva, sino también altamente funcional y adaptada a las necesidades reales del equipo.

Leverage user research to develop in-depth user personas that represent diverse segments of your target audience. These personas provide valuable insights into user needs, motivations, and pain points, enabling you to prioritize product features and functionalities that align with user expectations and address their challenges effectively. User research insights can also guide the marketing strategies, ensuring that messaging and content resonate with the specific needs and preferences of your target audience. Consequently, user research insights empower you to create products, services, and marketing strategies that truly connect with your target audience, leading to increased customer satisfaction and business growth.

To effectively test and iterate data visualizations: Understand Your Audience: Tailor to their background and needs. Define Goals: Ensure clear, objective-aligned messaging. Right Visualization: Choose types that best represent your data. Prototype: Create initial versions using tools like Tableau or Python. Accuracy Testing: Check data correctness and avoid statistical errors. Feedback: Gather input from your audience and experts. Iterate: Refine based on feedback, often through multiple rounds. Accessibility: Ensure color and text choices are inclusive. Format Testing: Verify responsiveness across devices and formats. Final Review: Conduct a thorough last check. Monitor Post-Launch: Observe interactions and update as needed.

Take a step back, detach yourself from what you designed, take a breack if neccessary. Then come back with the user in mind. Try to embody the persona, try to think like the user. Use your imagination and make use of the data that you already have. Looking at your design from another perspective, helps solving emergent issues.

Here, we need two ingredients: having clear in mind what the outcome of the audience is; and the ability to test and learn quicky, in an Agile fashion. The most important element is the possibility to prepare a view and test it with the audience (perhaps as a Pilot, in limited format). Then, get feedback and iterate (changing the visualisation being used, the labels, or adding some other view that hasn't been included first). This cycle is constant - and, hopefully, quality improves at each iteration.

Keep your users involved in development. Communicate often so they know what's going on. Verifying a visualization is showing what they want it to show is really important and it's something that often be undervalued in development.

Ensayo y error, por siempre y para siempre. Como en todo, en la visualización de datos, la clave es optimizar hasta el infinito. Siempre habrá espacio para mejorar y adaptar nuevas funcionalidades. Para esto será muy importante: ↳ Obtener retroalimentación constante. ↳ Realizar pruebas prácticas con usuarios representativos, observando cómo interactúan con la visualización y recopilando comentarios específicos. ↳ Analizar métricas cuantitativas, como el tiempo de interacción y la tasa de error, para evaluar el rendimiento y la eficacia de la visualización. ↳ Implementar versiones alternativas de la visualización y comparar el rendimiento, identificando posibles mejoras. Y luego, realizar ajustes graduales y seguir testeando.

User research to identify the needs of your audience in data visualization might sound intimidating, complicated, and time-consuming. However, it's better to run some research than none at all. While I don't advocate for a half-done job, you can start your research by asking one or two users for 15 minutes of their time. Ask them to say out loud what they think your article or dashboard is supposed to mean, and how they would execute some simple tasks (e.g. filter for a country). Will it be as exhaustive and elaborate as what this article outlines? No. But better than nothing and keeping you in the habit of researching your user? Yes.

Tools currently in user research ,to understand the audience ,be it, customer success, employee engagement, leadership effectiveness , performance appraisals, 360 assessments are done ,systematically and periodically.Data visualizations help with trend analysis and the drawn insights. Interestingly,Sentimental and Statistical analysis available in few tools like Qualtrics are highly efficient in spotting the focus areas. However helping the audience understand the insights and interpretations,needs invested energy and a focused approach. Extensive & Transparent communication cam ensure success.

UX research isn't just about "collecting data". It is about understanding our users, their expectations, and their goals. It is about really understanding your customers, instead of making assumptions about them.

Honestly, the most important thing I've learned is to ask the following question: "what decisions and actions are you [as a user] able to take, and what information do you need for that". Actionability, as much of a catchphrase as it is, is the most important word in user research and data visualisation I think. Your solution should directly contribute to the decision or action your user needs take and each element of your solution needs to contribute to it. Adding additional information that is not actionable actually reduces the usefulness of your solution, since it dilutes and distracts usage.

Look for as many instances of qualitative data analysis that can be applied to your user research to really bring out the best insights. Look beyond word clouds and sentiment analysis. Assign qualitative variables to your user research which can drive better insights for user friendly changes to be identified better.