You’re struggling to analyze data. What are the best ways to improve your analytical skills?

Define clear objectives, set measurable targets, understand stakeholder needs, establish success criteria, identify key questions, gather relevant data, choose appropriate tools, and iterate for refinement.

One thing I have discovered is that we fail to analyze data because we do not understand what it is that we want to achieve. As a result, we end up going in circles. The most important aspect of successful data analysis is understanding where you are coming from and where you are going. Setting clear objectives and milestones is undoubtedly the key to unlocking successful data analysis.

1. Focus: What specific business problem do you want to solve? What decision do you want to inform? 2. Impact: How will the results of your work be used to improve a product, a process, or drive business decisions? 3. Measure: Avoid vague goals like "understand customer behavior." Instead, aim to "increase customer retention by X%." 4. Be realistic: Don't overcomplicate things. A complex goal might derail your motivation. Start with a well-defined, achievable target.

First things first, define your goal. Know exactly what you're aiming to achieve with your analysis. Use the SMART criteria to make sure it's specific, measurable, achievable, relevant, and time-bound. This clarity will guide your entire analytical process and ensure you're on the right track.

Before diving into the data understand the business requirements and end goals needed by the C-suite or the stakeholders. This will help the data engineers to better lookout for the data points relevant to the requirements.

Choose the right method for your analysis. Depending on your goal, consider methods like descriptive, exploratory, inferential, predictive, or prescriptive analysis. Each method serves a different purpose, so pick the one that aligns best with your data, question, and desired outcome.

1. Method alignment: What are you trying to discover or identify? A trend, relationship, or predict an outcome? 2. Data type: Are you handling numbers (e.g., sales), categories (e.g., demographics), or text (e.g., reviews)? Different methods are better suited to handle different data types. 3. Basics. Descriptive statistics (e.g., mean), tables, and visualizations can reveal a lot. Don't underestimate. 4. Experiment / refine. Data analysis is iterative. Try different methods for deeper insights. If one doesn't feel right, switch. 5. Consider asking for help. Consult resources (tutorials, online communities) or collaborate with an expert for complex analyses.

To improve analytical skills, start with the scientific method: observe, question, research, hypothesize, experiment, analyze, and conclude. Apply it iteratively to enhance problem-solving and critical thinking abilities.

Por ejemplo: Supongamos que su objetivo es mejorar la detección y prevención de accesos no autorizados a instalaciones críticas utilizando análisis de video. Aquí le muestro cómo podría escoger su método: Objetivo Específico: Implementar un sistema mejorado de vigilancia que utilice análisis de video para detectar comportamientos sospechosos y alertar sobre posibles intrusiones en tiempo real. Método Elegido: Decide enfocarse en el aprendizaje profundo (Deep Learning) para el reconocimiento de patrones en video, dado que esta técnica puede ser especialmente efectiva para identificar comportamientos anómalos o acciones específicas entre grandes cantidades de datos visuales.

Excel, Python, R, or SQL are versatile tools for data analysis. Choose based on your familiarity, project requirements, and desired level of complexity.

1. Needs: What are you trying to solve with data? Do you need in-depth statistics or simple visualizations? Is speed crucial? 2. Compatibility: Make sure it can easily import your data sources (spreadsheets, databases, etc.) 3. Ease of Use: A complex tool you can't learn hinders progress. Consider your technical expertise and available training time. 4. Visualizations Matter: Your tool needs to create the charts, graphs, and dashboards that communicate effectively to your audience. 5. Collaboration: If teamwork is key, ensure your tool works well for sharing, editing, and real-time collaboration.

Choose the best tool for your analysis. Consider factors like your skill level, budget, and data complexity. Options range from spreadsheets and databases to software like Excel, SQL, Python, R, Power BI, Tableau, and Google Analytics. Look at features, functions, and ease of use to find the right fit for your needs.

Paso 3. Supongamos que su objetivo es desarrollar un sistema de análisis predictivo para prevenir el fraude financiero dentro de una organización. Ha decidido utilizar técnicas de aprendizaje automático para identificar patrones de transacciones sospechosas basándote en datos históricos. Herramienta Elegida: Python con bibliotecas especializadas como Scikit-learn para modelado de aprendizaje automático, Pandas para manipulación de datos, y Matplotlib o Seaborn para visualización de datos. Razones de la elección: Compatibilidad con el Método, Facilidad de Uso, Capacidad de Manejo de Datos, Integración y Compatibilidad, Soporte y Comunidad.

Now it's time to put your logic to work with the data. Apply critical thinking, reasoning, and creativity to interpret, manipulate, and visualize the data. Ensure accuracy, validity, reliability, and consistency by checking data quality, cleaning, removing outliers, and handling missing values. Use the right formulas, functions, queries, and algorithms to perform calculations, transformations, and analyses.

Share your discoveries effectively. Present findings clearly with charts, graphs, tables, or reports. Explain methodology, assumptions, limitations, and recommendations. Be ready to address questions and feedback from your audience. Keep it clear, concise, and compelling.

1. "So What?": Highlight the most important takeaways. What does the data mean for the bigger picture? 2. Audience: Language and explanations should match audience. Executives need a different summary than analysts. 3. Visuals: Graphs, charts, and infographics make complex info easier to understand and add interest. 4. Ditch Jargon: Avoid overly technical language unless you're speaking to that audience. 5. Context: Connect analysis to the original questions or business problems. 6. Recommendations: Suggesting potential actions or next steps to take based on your findings.

Seek mentorship from experienced data analysts and engage in collaborative projects; doing this will create a platform where you can learn from others. Remember, different people have different perspectives and approaches to analysing data.

Share your analyses and conclusions with others, and be open to feedback. This can help you identify blind spots and areas for improvement. I suggest to study the approaches and methodologies used by experts in fields relevant to your interests. Analyze how they approach problems and derive data driven decisions.