Vous donnez votre avis sur un outil d’analyse de données. Quelles sont les erreurs courantes à éviter ?

In my experience, overlooking data quality is a critical misstep in data analysis. High-quality data underpins reliable insights, yet it's often assumed rather than verified. Common issues like missing values, duplicates, or inconsistent formats can skew results, leading to faulty conclusions. A proactive approach involves regular audits and applying robust validation rules before analysis. Remember, the accuracy of your output is only as good as the data input. Ensuring data integrity should be a foundational step, not an afterthought, in any analysis process.

- Neglecting data quality: Failure to check for completeness, accuracy, consistency, and biases can lead to flawed analysis. - Overlooking missing data: Ignoring missing values without proper handling techniques can skew results and conclusions. - Ignoring inconsistencies: Inconsistent formats, units, or coding schemes can cause errors and confusion in analysis. - Succumbing to biases: Unaddressed biases in sampling, selection, or data collection methods can distort analysis outcomes. - Using outdated data: Relying on stale data can lead to irrelevant or inaccurate insights, necessitating regular updates. - Failing to validate assumptions: Uncritical acceptance of assumptions without empirical validation can undermine analysis credibility.

Além do que foi apresentado, cito a necessidade de se conhecer bem os dados com que está trabalhando. É importantíssimo se conhecer o banco de dados e as variáveis em uso.

I believe that not checking the data quality is an absolute huge mistake as the numbers won't be right and the report/dashboard becomes pointless. Before building any tool in Analytics it is imperative to check the data for reliability, accuracy, missing, duplicated and inconsistent data. If the data quality check is inexistent the analysis will be misleading. Always check your data quality!

Data quality check is the critical step to interpreting the results accurately. One should always ask question if the values input for the variables of interest collected have true values. Another thing is how you enter missing data and data not collected in your files is important to not misreport any findings! Proper values should be assigned to report these data and should follow proper legends in the data files so there is accuracy in data analysis.

Ignoring the context and purpose of a data analysis tool can lead to ineffective feedback. It's essential to grasp why the tool was used and its intended outcomes. Assess whether the tool addresses the relevant problem or question and aligns with stakeholders' goals and expectations. Evaluate if it delivers actionable insights pertinent to the context. By considering these factors, feedback can be more targeted and constructive, ensuring the analysis meets its intended objectives.

If you don't understand the purpose of the tool (why it's needed) and context (who will use it, when, and under which conditions), you are not positioned to provide helpful, relevant feedback.

In my experience, context is key. Some facts can be misunderstood if context is neglected. Which is why it is very important to pitch analysis of a tool based on the context of what it is produced to solve instead of what we feel it should solve. The performance of the tool can only be evaluated properly when we are aware of the purpose behind its creation and use it accordingly.

Español: Elegir la herramienta adecuada es ideal para un mejor rendimiento, análisis y presentación de sus datos e informes. Al contrario de lo que muchos piensan, analizar y presentar datos no siempre son funciones del mismo software. Estudie las mejores formas y herramientas para el trabajo en cuestión y solo entonces comience sus análisis. Portugues: Escolher a ferramenta certa é ideal para uma melhor performance, análise e apresentação dos seus dados e relatórios. Diferentemente do que muitos pensam, analisar e apresentar dados nem sempre são funções de um mesmo software. Estude as melhores maneiras e ferramentas para o trabalho em questão e só depois inicie suas análises.

Penso que dado o elevado número de ferramentas e análises que o profissional da área desenvolve com frequência, é comum que haja uma espécie de padrão construído com o tempo. Contudo, essa padronização pode ser justamente a raiz do problema, visto que as demandas correm o risco de serem trabalhadas no sentido de atender o padrão, e não ao contexto e objetivos específicos definidos pelo demandante, portanto é necessário avaliar esse fator, e dar um feedback relativo ao problema raiz, não somente ao trabalho em questão.

I agree that aesthetics are less important than data quality and context, however poor data visualization can result in pitfalls that can impact the understanding, analysis, and decision-making processes based on the visualized data. Using the wrong type of chart or graph to describe the data effectively. Failing to highlight or significant patterns, trends, or outliers in the data can lead to missed insights. Not considering accessibility issues, such as color blindness, can exclude a portion of the audience from fully understanding the visualization. For these reasons it is important to consider aesthetics as part of the feedback for a data analysis tool.

When providing feedback on data analysis tools, it's important to prioritize functionality over aesthetics. While visual appeal is valuable, usability and effectiveness are paramount. Instead of fixating on superficial aspects like color schemes or font sizes, assess the tool's usability and functionality. Consider whether it is intuitive, offers ample flexibility, and reliably produces accurate results. By emphasizing these key attributes, feedback can steer improvements that enhance the tool's overall utility and user experience.

Prioritizing aesthetics over functionality in data analysis tools can detract from their effectiveness. While visual appeal and usability are important, functionality is paramount. When providing feedback, refrain from fixating solely on aesthetics. Critiques about color schemes or font sizes are irrelevant if they don’t impede readability or usability. Instead, concentrate on assessing the tool’s features and functionality. Is it user-friendly and intuitive? Does it offer sufficient options and flexibility? Is its performance robust, delivering accurate results? These considerations are essential for optimizing data analysis tool utility.

Data analysis tools should not only be functional and effective but also prioritise aesthetic,maintaining branding and adopting a user centric design. If these elements are overlooked, users may not be entirely satisfied with the tool’s results. Striking a balance between functionality and aesthetic is key for achieving user satisfaction and successful tool utilization.

The subtle link between design and functionality/ergonomics is often overlooked. It's crucial to remember that design serves as a tool for enhancing usability and UX, rather than being an end in itself. While aesthetics are important, placing excessive focus solely on design can be a mistake. Ultimately, it's the seamless integration of design with functionality and ergonomics that creates a truly impactful user experience. Let's prioritize balance and harmony in design to optimize usability and enhance user satisfaction.

O motivo principal do feedback em minha opinião é auxiliar, apoiar, e não constranger nem tampouco humilhar. Utilizar da ferramenta de forma clara e objetiva, podendo utilizar da técnica do "feedback sanduíche" onde você aponta de forma respeitosa a falha e em seguida elogia o acerto, deixando muito mais leve o ambiente, pontuando as falhas, mas favorecendo os pontos positivos do profissional.

Saber se posicionar ao fornecer um feedback construtivo é fundamental para que o receptor entenda a mensagem de maneira clara, objetiva e assertiva. Utilizar de argumentos invasivos, arrogância ou sarcasmo para pontuar algo é antiético e desnecessário, enaltecer demasiadamente antes de avaliar por um determinado período também não é o caminho, pois avaliação para melhorias sempre serão necessárias!

Feedback should strike a balance between specificity and sensitivity. It's important to avoid being overly vague or harsh when providing feedback on data analysis tools. Instead of making general statements like "this tool is bad" or "this tool is great," offer specific examples and constructive suggestions for improvement or commendation. Additionally, use language that is balanced and objective, steering clear of overly negative or positive terms. This approach fosters a supportive and collaborative environment conducive to meaningful dialogue and improvement.

Debemos tener en cuenta que en su momento estuvimos en el lugar que hoy analizamos o en el puesto de esa persona o varios en su defecto, es decir que debemos ser más empaticos y que nuestras palabras más que un reproche sea un aliento o salida para aquel que nos lee.

A importância do feedback não pode ser subestimada. Saber oferecer um bom feedback pode ser a diferença entre uma equipe motivada e uma equipe dispersa. Busque sempre enaltecer os pontos positivos e acompanhar as críticas construtivas com sugestões de como aquele ponto negativo pode ser melhorado.

When receiving feedback, consider the various perspectives that can contribute to the feedback that is received. For example, expertise, experience with similar case studies that seem applicable to the task at hand, and overall a new perspective than your own that can provide honest feedback. One note that always resonates with me is to receive feedback from someone that you know and trust with their judgment! Find your “safe” person that you can run and bounce ideas with!

When giving feedback on a data analysis tool, avoid vagueness, negativity without constructive suggestions, and overlooking positive aspects. Provide context, consider user needs, and ensure specificity to facilitate effective improvements. Consistency, alignment with user preferences, and follow-up are also crucial for impactful feedback that drives meaningful enhancements in the tool.

Tailoring feedback to the receiver's preferences and needs is essential for effective communication. Consider factors such as the receiver's experience and expertise with data analysis tools, their receptiveness to feedback, and their preferred communication style. Adapt your tone, style, and format accordingly, whether delivering feedback via email, phone, or in person. This personalized approach fosters a supportive environment and enhances the likelihood of constructive dialogue and improvement.

Na minha visão, feedback é uma troca de informações. Ao fornecer feedback, é essencial considerar o receptor e sua reação potencial. Avalie a experiência e o conhecimento deles em análise de dados, bem como sua receptividade ao feedback. Adapte o tom e o formato do seu feedback de acordo com as preferências do receptor, seja por e-mail, telefone ou pessoalmente. Essa consideração aumenta a eficácia e a aceitação do feedback.

Best on my knowledged on data collection and Analysis, When not considering the feedback rceiever , so the entire data is a fake data not real data . Because data is information such as facts and numbers used to Analyze something or make a decision. So when feedback is not be considering the whole information will become Fake.

Lack of training and support, providing inadequate training and support for users can hinder the adoption and utilization of the data analysis tool. It's important to offer comprehensive training resources, documentation, and support channels to help users effectively learn how to use the tool and troubleshoot any issues they encounter.

Any new data tool before publishing must go through vigorous testing based on different parameters relevant to your organisation or industry. Testing the data enough through different periods, seasonalities, cues from historical trends and ground reality can ensure there is no delta and the data tool is fool proof.

Start by avoid these pitfalls: Feature overload—ensure features meet user needs. Usability issues—prioritize intuitive design. Data quality concerns—verify accurate data processing. Scalability challenges—assess performance with varying data volumes. Lack of flexibility—seek customization options. Collaboration limitations—ensure support for team collaboration. Security and compliance risks—confirm adherence to data protection standards. By avoiding these, you can provide valuable feedback to enhance the tool's functionality and usability.

One of the popular mistakes to avoid when giving feedback especially in data analysis is exaggeration. I recommend that before you give feedback, take out time to gather data and analyse accurately. Be sure with the numbers, quantity, rate and other specifics. And if you don't know the actual figure or exact situation; ensure you let the receiver know. Always bear in mind that an exaggerated feedback on an issue may cause more harm than good. Hence, the need for honesty and specificity.

Tips: 1. Buscar una herramienta que ofrezca capacidades avanzadas de visualización de datos para facilitar la interpretación de los resultados. 2. Priorizar la integración con otras herramientas y sistemas que ya utilizas. 3.Considerar la posibilidad de personalización de la herramienta para adaptarla a tus necesidades específicas. 5. Realizar pruebas piloto. Errores a evitar: 1. Comprar una herramienta sin antes evaluar si realmente cumple con tus necesidades. 2. Ignorar la importancia de la compatibilidad con los sistemas existentes. 3. No involucrar a los usuarios finales en el proceso de selección. 4. No considerar los costos totales, incluyendo licencias, mantenimiento y actualizaciones. 5. Descuidar la seguridad de los datos.