What are the key steps to developing an analytical problem-solving mindset?

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Data analytics is a valuable skill that can help you make better decisions, optimize processes, and generate insights from complex and diverse data sources. But to master data analytics, you need more than just technical tools and methods. You also need an analytical problem-solving mindset, which is the ability to approach problems logically, creatively, and critically. In this article, we will explore what are the key steps to developing an analytical problem-solving mindset and how you can apply them to your data analytics projects.

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Wilma Landaeta

Ingeniero Mecánico-Abogada
David Abecassis

Data Analyst

1 Define the problem

The first step to solving any problem is to clearly define it. This means identifying the main goal, the scope, the constraints, the assumptions, and the stakeholders involved in the problem. Defining the problem helps you narrow down your focus, avoid confusion, and communicate effectively with others. You can use tools such as problem statements, SMART goals, and stakeholder analysis to define the problem in a concise and specific way.

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Wilma Landaeta

Ingeniero Mecánico-Abogada
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Considero, además que definir el problema significa exponerlo sin ambiguedad, es describir las características y comportamientos de la o las variables o datos. Definirlo con base a estadísticas comparativas o por medio de los KPI permitirá medir los resultados.

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2 Explore the data

The next step is to explore the data that is relevant to the problem. This means collecting, cleaning, and transforming the data from various sources and formats. Exploring the data helps you understand its characteristics, quality, and limitations. You can use tools such as descriptive statistics, data visualization, and data profiling to explore the data in a systematic and comprehensive way.

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Wilma Landaeta

Ingeniero Mecánico-Abogada
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Estoy de acuerdo con esta apreciación, además a?adiría que la parte subjetiva en la exploración de datos, es mantener una mente abierta y una visión más allá de lo mero evidente para lograr la productividad de la exploración efectiva, con ayuda de herramientas formales y no formales ( entrevistas abiertas si aplica)

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3 Analyze the data

The third step is to analyze the data to find patterns, relationships, and anomalies that can help you answer the problem. This means applying appropriate techniques and models to the data, such as regression, classification, clustering, or sentiment analysis. Analyzing the data helps you generate insights, hypotheses, and evidence that can support your decision making. You can use tools such as Python, R, SQL, or Excel to analyze the data in a flexible and powerful way.

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Wilma Landaeta

Ingeniero Mecánico-Abogada
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Totalmente de acuerdo responder el problema con varias aristas, es decir, plantear podibled escenarios para la solución efectivista.

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4 Interpret the results

The fourth step is to interpret the results of your data analysis and evaluate their validity, reliability, and significance. This means interpreting the results in the context of the problem, checking for errors, biases, and uncertainties, and comparing them with other sources and methods. Interpreting the results helps you draw conclusions, recommendations, and implications that can solve the problem. You can use tools such as p-values, confidence intervals, and error metrics to interpret the results in a rigorous and objective way.

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David Abecassis

Data Analyst
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By interpreting correctly the results of your data analysis, you ensure a thorough understanding of the problem's context. As a result, you can extract meaningful insights, conclusions, recommendations, and practical implementations to address the underlying problems.

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Wilma Landaeta

Ingeniero Mecánico-Abogada
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La interpretación conlleva a una retroalimentacion objetiva y subjetiva, que genera comparaciones y plantea escenarios de conclusiones y recomendaciones, el aporte de herramientas estadísticas proyectan una interpretación relevante.

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5 Communicate the findings

The final step is to communicate the findings of your data analysis and interpretation to the relevant audience and stakeholders. This means presenting the findings in a clear, concise, and compelling way, using appropriate formats and channels, such as reports, dashboards, or presentations. Communicating the findings helps you share your knowledge, persuade others, and inspire action. You can use tools such as storytelling, visualization, and feedback to communicate the findings in a engaging and effective way.

Developing an analytical problem-solving mindset is not a one-time event, but a continuous process that requires practice, feedback, and reflection. By following these key steps, you can enhance your data analytics skills and solve problems with confidence and creativity.

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Wilma Landaeta

Ingeniero Mecánico-Abogada
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Correcto permite plantear escenarios. Comunicar los hallazgos se complementa con análisis y detalles gráfico, en lenguaje claro y preciso.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the key steps to developing an analytical problem-solving mindset?

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1 Define the problem

2 Explore the data

3 Analyze the data

4 Interpret the results

5 Communicate the findings

6 Here’s what else to consider

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