¿Qué hacer si la información de los datos se está desmoronando?

Clarify the purpose of your analysis. Are you trying to identify a trend, explain a specific phenomenon, or suggest an action? Clearly defining your objective from the beginning will guide your data analysis, visualization selection, and presentation narrative, ensuring that each element contributes to this unified purpose.

In the data world, truly understanding what is the business problem you are trying to solve and tailoring your data analysis to that specific audience is very important. Without that, the whole insight generation process becomes counterproductive and leads to a lack of trust. Hence, one has to tailor their data analysis findings as per the audience in a format that helps them get to the right output that is required.

👥 Antes de sumergirte en los datos, es fundamental comprender quién es tu audiencia. ¿Son ejecutivos buscando tomar decisiones estratégicas? ¿O son equipos operativos buscando mejorar el rendimiento diario? Conocer las necesidades, el nivel de conocimiento y las expectativas de tu audiencia te permitirá presentar la información de manera que resuene con ellos y satisfaga sus necesidades específicas.

Breaking down a complex problem into smaller, more manageable parts. Using data and evidence to make informed decisions. Identifying cause-and-effect relationships. Using critical thinking and logical reasoning to evaluate different options. Developing and testing hypotheses. Applying mathematical or statistical models to solve problems. Brainstorming and considering multiple solutions. Evaluating the strengths and weaknesses of different options. Seeking out additional information or resources when needed. Implementing and adapting solutions as new information becomes available.

Audience analysis is crucial in data storytelling. Understanding the audience's interest in data analysis and their level of data literacy is key. To engage the audience effectively, conduct audience analysis. The same data story may have different implications for different audiences. It all comes down to what will pique their interest.

In my experience, understanding the “why” rather than focusing on the “how” is the key to defining the purpose. What is it that you are trying to solve and why are you doing the same will help you draft a more concise analysis to your end stakeholders leading to effective data storytelling.

🎯 Clarifica el propósito de tu análisis. ¿Estás tratando de identificar una tendencia, explicar un fenómeno específico o sugerir una acción? Definir claramente tu objetivo desde el principio guiará tu análisis de datos, la selección de visualizaciones y la narrativa de tu presentación, asegurando que cada elemento contribuya a este propósito unificado.

Adopting a KISS (keep it simple silly) methodology can help one choose the right kind of visuals. Rather than over cluttering your dashboards/ reports with a bunch of complex visualizations, understanding the data and then picking the right visuals that can effectively generate the insights in a concise manner.

We should follow the below 3 main steps 1) 1) Prioritize: align the required time and effort needed for the analysis with the potential value/impact of the decision you're making. 2) Understand what caused movement in the data. This is such an elementary concept, but it is missed so often. Over 99% of the time, the story in the data is going to be where movement occurred. "Point in time" data will tell us how KPIs performed, but looking at where the movement occurred is the gateway to understanding the "why's"

The data serves as the base of a data story. It's information from accurate data gathering and analysis. Data can be gathered from such places as charts and dashboards using data analysis tools. The narrative is a verbal or written storyline that's used to effectively communicate insights from the data.

When you are not getting useful information from your data, it is important to take a step back and look at how you are collecting and analyzing it. Try to get different perspectives and consider using other data sources to improve your predictions. RCA tools like the 5 Whys can help you dig deeper into why things aren't working as expected. Keep learning and stay flexible to make sure you are getting the most out of your data.

El éxito de una presentación de datos depende de la adecuación de la misma a la audiencia. No es lo mismo explicar los resultados de un modelo de prevención de abandonos al CEO que al equipo de ciencia de datos. Por esto, en primer lugar debes reflexionar acerca de las expectativas de tu audiencia y actuar en consecuencia. Piensa qué les interesa saber y céntrate en ello. A continuación utiliza elementos gráficos apropiados, se coherente con los estilos y sobre todo, cuenta una historia. Los datos tienen que decir algo, si no los hacem, no son más que una retahíla de gráficos sin sentido.

To revitalize stagnant data insights, first verify data accuracy and relevance, then collaborate with domain experts for deeper understanding. Reframe analysis questions for clearer focus and iterate on methodologies. Effective communication of findings is crucial for actionable insights.