Vous vous noyez dans un océan d’analyse de données. Comment pouvez-vous naviguer jusqu’à terre avec vos compétences analytiques ?

Data can be contagious, I would always start with a Goal and start data modelling with defined variables to drive towards your goal. In that process, have your variables modeled in such a way that you can play around with multiple scenarios. Then start generating insights from your scenarios which can get you closer to your defined goal and repeat the same process until you reach your goal.

Raw data is storehouse of information but of little value unless sorted, analysed with well defined goals. You need to sort data based on what analysis you want. So sort data then analyse trends and then it becomes knowledge and an effective tool for decision making.

➔ Prioritize the most important data. ➔ Break the data into manageable chunks. ➔ Use tools and software to help analyze the data. ➔ Look for patterns and trends. ➔ Simplify complex information into clear insights. ➔ Communicate your findings effectively. ➔ Continuously refine your analysis process.

Al enfrentarte a un volumen considerable de datos, es fundamental establecer objetivos claros y específicos para tu análisis. En primer lugar, comienza por identificar la pregunta o problema clave que deseas abordar. Luego, delimita tus objetivos para garantizar que sean medibles, alcanzables, relevantes y acotados en tiempo. Prioriza la precisión en tus metas y asegúrate de que estén alineadas con los intereses y necesidades de tu organización. Además, es recomendable involucrar a stakeholders relevantes en el proceso de definición de objetivos para garantizar un enfoque completo y acertado en tu análisis de datos

A goal is a specific, measurable objective or desired outcome that an individual or organization aims to achieve within a certain timeframe. It provides direction and motivation, guiding actions and decisions toward accomplishing a particular purpose or result. Goals are often categorized into short-term and long-term, and they help in setting priorities and evaluating progress.

In my experience, once goals are set, sorting data becomes essential. For example, while working on a project to optimize our sales strategy, we categorized data based on relevance and quality. We prioritized information such as sales figures, customer demographics, and market trends, discarding less relevant details. This process is like filtering seawater to extract salt; valuable insights emerge from the vast data. One thing I've found helpful is effective sorting, which saves time and energy, allowing you to focus on analysis that drives you towards your objectives.

Hay que tener cuidado de caer en los análisis innecesarios ya que provocan parálisis y así no se avanza. Está bien medir para mejorar y para que sea útil es necesario definir previamente qué se quiere medir y cada cuánto tiempo hacerlo. Bastarán unas 3 métricas diferentes para organizar un buen panorama de información para hacer los ajustes necesarios entre el producto/servicio y el mercado objetivo. El resto, es música que desafina.

Build a data extraction bot that discerns the data for you making it much easier to use. Many of the largest companies in the world use data bots to organize the data into understandable and readable data.

Ordenar y revisar la calidad de datos es fundamental para lograr hallazgos que generen valor , un problema común en mantener bases de datos sin seguir un estándar

Being able to weed out what you don't need helps prevent analysis paralysis. It's easy to delve into the trap that everything is important. Everything could be important, but not necessarily important to what you are trying to accomplish.

Analyzing trends in a vast amount of data involves several key steps: Define Objectives: Clearly identify what you're trying to learn or achieve from the data. This helps in focusing the analysis. Collect and Organize Data: Gather relevant data from various sources and organize it in a structured format for analysis. Clean the Data: Remove any inconsistencies or errors in the data to ensure accuracy

Trends in Recruitment, help to refine recruitment strategies. This might be in response rate of some certain regions, best platforms to get certain demography of candidates, best tools or culture etc that creates great traction for job post etc.

Meiner Erfahrung nach ist es entscheidend, sobald Sie die relevanten Daten in der Hand haben, mit dem Erkennen von Mustern und Trends zu beginnen. Hier kommen Ihre analytischen Fähigkeiten zum Tragen. Suchen Sie nach wiederkehrenden Themen oder Anomalien, die auf tiefere Einblicke hinweisen könnten. Ähnlich wie ein Segler die Sterne liest, um sich zurechtzufinden, können Sie Trends nutzen, um Ihre Analyse in die richtige Richtung zu lenken. Das Erkennen dieser Muster wird Ihnen helfen, das Gesamtbild zu verstehen und fundierte Entscheidungen zu treffen. 🌟🔍🚀

One time at work, we were tasked with improving product features. In my experience, analyzing trends was crucial. We began by identifying patterns in user feedback and usage data, looking for recurring themes and anomalies. Just as a sailor reads the stars to navigate, these trends guided our analysis. One thing I’ve found helpful is recognizing patterns, which helped us understand the bigger picture and make informed decisions about which features to enhance or modify. This approach ensured that our improvements were aligned with user needs and expectations.

When we know what we want to achieve using this data set and we are done with sorting and cleaning then your expertise comes to play as here you have to reflect your knowledge by adding different perspective as per the experience and industry trends.the person who is doing this part should be well versed with the domain so that he or she can add value to the data.

These tools help in identifying trends, making data-driven decisions, and ultimately improving outcomes. Embracing these technologies not only enhances productivity but also empowers you to uncover deeper insights and communicate findings more effectively.

In my experience, utilizing analytical tools is essential. One time at work, we had to analyze a large dataset to improve our marketing strategy. We used spreadsheet software and advanced data analysis programs to organize and examine the data. These tools are like navigational instruments for sailors, helping to chart the course. One thing I’ve found helpful is leveraging these tools to simplify complex tasks and uncover hidden relationships in the data. They are crucial for efficient and effective analysis, ensuring you can draw meaningful insights and make well-informed decisions.

There are currently various analytical tools at the disposal of the working populace currently. Identifying the best one tailored to your company and the type of data you’re looking to refine and clean is very important. Be it Ms Excel or PowerBI, the end result is to be able to make actionable decisions.

To effectively communicate my findings, here are a few steps that I follow: 1. Align it with the business goals 2. Keep it simple and structured 3. Make it visually interesting using appropriate charts and graphs 4. Have a summary of key findings and implications. When your findings are communicated clearly, it ensures that stakeholders understand them and take appropriate actions. It reduces the risk of misinterpretation.

It is important to keep your findings simple and understandable. Infographics are an effective option for presenting information in a clear and convenient way. You should specify why the features you have selected for analysis are important and how they align with your objectives. It is crucial to answer questions such as: What are your findings? How do they align with your objectives? Why is it important to achieve these objectives?

When you're communicating data analysis results aim for clarity and simplicity. Avoid jargon and acronyms that may confuse your audience, and take time to define any unfamiliar terms. Have a summary of key findings and implications. Use reports, presentations, or infographics, to determine the trait of your analysis, which make it valuable. A logical flow with clear language will allow everyone to understand the results and implications of your data analysis.

Use statistical methods, data visualization, and machine learning techniques to analyze your data. Look for patterns, correlations, and insights.

Solicitar Apoyo y Colaboración: Si te sientes abrumado, no dudes en buscar apoyo de colegas con más experiencia o expertos en análisis de datos. La colaboración puede proporcionarte nuevas perspectivas y soluciones creativas.

La reflexividad es un componente clave que debe estar presente a lo largo de todo el proceso frente al análisis de los datos. Este proceso debe ser planificado, debe tener su espacio y tiempo, ya que se constituye en un proceso de sistematicidad sostenido en el tiempo acorde a los objetivos buscados.

It’s very easy to be overwhelmed with any analysis and lose direction. My simple and easy to remember formula is - never start coding after getting the dataset. - look at the dataset and write down all the questions you want the dataset to answer. Some questions might be redundant at this stage, but that’s ok - once we have exhausted writing all the questions, this is going to serve as a direction for our EDA and pre processing - at this stage we should also ask business for the problem/s we are trying to solve for a single dataset can be used to solve more than one problem. These steps ensure that we are not lost in our analysis

El proceso completo de indagación y análisis de datos, merece del compromiso y la responsabilidad ética necesaria para un trabajo de calidad, de profundización y veracidad de los datos a través de la objetividad. Por otro lado, el disfrute y placer por recorrer el proceso es clave también. La pasión y el amor por la ciencia es un componente que debe estar presente.

Navigating through a sea of data analysis can feel overwhelming, but with the right approach, you can effectively make your way to shore. 1. Set Clear Objectives 2. Organize Your Data 3. Choose the Right Tools 4. Break Down the Analysis 5. Visualize the Data 6. Collaborate and Seek Feedback 7. Make Data-Driven Decisions

Ser efectivo con los datos y las habilidades analíticas es clave en el mundo actual, con las siguientes pautas: -Visualización clara: Convertir datos complejos en gráficos y diagramas fáciles de entender. -Comunicación efectiva: Explicar los hallazgos de forma clara y concisa, tanto a expertos como a no expertos. -Preguntas relevantes: Formular preguntas específicas que los datos puedan responder. -Herramientas adecuadas: Elegir las herramientas que mejor se adapten a las necesidades y nivel de experiencia: Excel, Python, SQL. -Práctica constante: Aplicar habilidades a diferentes conjuntos de datos y proyectos para mejorar continuamente. -Aprendizaje continuo: Mantener actualizado con las últimas tendencias y tecnologías.

Before analyzing data make sure that you understand it, preferably talk with people who have day to day experience working in the fields from which the data is collected. What internal (f. e. maintenance, outages, business process or even company events) or external factors (events like Black Friday, third-party service provider or supplier issues) might be skewing your data? Addressing these early in your analysis will help you identify clear patterns and prevent drawing incorrect conclusions.