How can you make data-driven decisions without getting bogged down in analysis?

Hard fact: there is no such a thing as objective data. For as much as data analysts like to present their work as objective, data are never neutral. You always start from a question in your mind, and inevitably look for data than confirm your idea. This is the so-called confirmation bias. Even the most thorough researchers can hardly escape the temptation to look for the set of data that confirm their ideas more than the ones that would prove those ideas wrong. In other words, a solid research question is the necessary starting point to be able to dig into data analytics with a purpose - without that, it is even pointless to dive into the data.

The best way to get started with a Data Driven decision making is to know 'Why' are you analysing the data? 'What' Problem am I trying to solve? 'How' will this data help me identify the patterns and the drivers behind the trend/pattern? Once we have an answer to these questions and have some business / domain knowledge, it can help us move in the right direction of the analysis without getting bogged down. It would also be quick & easy to decide if the technique being used is leading towards the desired result or not.

It is critical to rationalize your decision by data- drive Cen analysis; however , the critical aspect is how can a leader to make emotional decision. To shape and stick with your decision when information or data is not perfect information. Therefore, it is critical to make sure that you are able to stay with your gut feeling, with some support from data, to make the best decision possible

Immediate Solution Focus is the key as the deeper the dive the data keeps us enticed for even deeper dig and understanding. Smart objectives often get addressed thru one or two layers of data dive and post that the action must continue to evolve with each step begining based on the initial understanding of the solution. Result Trackers come in most handy for such exercise. Strategic analysis must continue in parallel to support the longterm strategy and futuristic evolution.

While carrying out the analytics, you need to remember the objective of the particular exercise. Also, it is advised not to make any prior assumptions before/during the analytics process. Once the analytics is completed only then do the analysis and present the findings.

Measure what Matters! Often people start with a bunch of data and try to figure out what it means. It makes a lot more sense to determine what data will provide the most value, for the smallest cost. If you have it already, great, but if you need to go get it, make sure to only get it if it provides more value than it costs to get. There is a lot of information available out there on the Value of Information, how to calculate it, and how to use it to inform your decisions.

In addition to quality, reliability and validity of data sources, it is pivotal to assess where data sources take their data from. One example: Eurostat - it is commonly considered as "the" data souce for European statistics, but if you dig a bit deeper into some of their statistics (particularly for agricultural markets) you may discover that they rely on national statistical institutes for their data, and that significant differences exist among those institutes on how to collect/process data and communicate them to Eurostat. In particular, comparability of data sets is often an issue when it comes to 27 different EU member states, let alone more (and more different) countries in the world.

Choosing the right data sources and methods is a pivotal step in the journey of solving problems and achieving goals through data analysis. The quality of your insights is inherently linked to the choices you make at this stage. Consider the reliability and validity of your data sources, ensuring they align with the questions you're aiming to answer. Simultaneously, evaluate the appropriateness and feasibility of your chosen methods, keeping in mind the potential limitations. Ethical considerations play a crucial role in data utilization. In the ever-evolving landscape of data analytics, making informed decisions about your data sources and methods is not just a technical requirement but a strategic imperative.

To ensure data-driven decisions are made efficiently, it's essential to carefully choose the data sources and methods in line with the identified problem and goal. Opt for data that directly relates to the issue at hand and is of high quality. Select methods that are both effective and streamlined, providing meaningful insights without delving into unnecessary complexities. The emphasis should be on maintaining focus and avoiding the trap of overanalysing, allowing for a more efficient decision-making process.

La data a utilizar debe ser de utilidad ya que al tener data con mucha "paja" solo nos estorba para encontrar "la aguja", la data debe ser concreta, homologada y cuantitativa de preferencia, ya que una data cuantitativa nos puede llevar a varias aristas, los métodos y recopilación de la data son parte de la fiabilidad del resultado

Continuous Monitoring: Implement continuous monitoring of key metrics. This allows you to stay informed about changes in the data landscape and make adjustments to your decisions in a timely manner. Focus on Actionable Insights: Concentrate on actionable insights. Rather than exploring every possible detail, prioritize findings that have a direct impact on the decision at hand.

Once the relevant data has been gathered, the next step is to analyze it thoroughly to draw meaningful insights. Employ analytical tools and techniques that align with the nature of the data and the goals of your analysis. Look for patterns, trends, and correlations within the data that provide valuable information related to the defined problem. The goal is to extract actionable insights that can inform decision-making. It's crucial to strike a balance between depth of analysis and practical application, ensuring that the insights gained are relevant and directly contribute to addressing the identified problem and achieving the set goal.

Al contar con la información deben ser conscientes del resultado que buscamos, y todas las variables serviran de apoyo para justificar el resultado y este sea lo suficientemente inobjetable ya que al presentarlo de forma gráfica no deberá tener algún resquicio que ponga en duda las consideraciones que arrojó el análisis y sirvieron para alcanzar el resultado mediante la data

Involve Cross-Functional Teams: Foster collaboration between data experts and decision-makers. By involving cross-functional teams, you can benefit from diverse perspectives and ensure that analysis is targeted towards addressing the key concerns of various stakeholders. Iterative Decision-Making: Embrace an iterative approach to decision-making. Rather than aiming for a perfect analysis, make decisions based on the best available data and be open to refining your approach as more information becomes available.

After analyzing the data, communicate key insights clearly to relevant stakeholders. Take prompt and decisive actions based on these findings, ensuring alignment with the defined problem and goal. Regularly reassess and adapt strategies for a dynamic decision-making process.

Con las conclusiones obtenidas del análisis que serviran para alcanzar el objetivo, manejar el mejor lenguaje y forma para presentar de manera que sea convincente para los interesados, y en caso de alguna actualización está logré ubicarse en el plano del desarrollo y asi recuperar la mayoría posible del proceso en base al ajuste solicitado y/o propuesto

From my experience, people are generally good at making the right decisions. However, the weakness lies in communication. For example, if you are in a leadership position and make changes to the reward system for your employees, it is very important to communicate it correctly. Otherwise, even if your aim was to improve the reward system, your employees might feel that you plan to take away their rewards, resulting in the total opposite outcome than the intended one. So, you must give equal or even more attention to communication.

Analysis paralysis is real! The more the data and the more you like this data, the higher the paralysis. One very useful tip I have found to work is that when wallowing in data, have someone by your side who does not understand analysis i.e the “how of doing it”. They will ground you by holding you accountable to the outcome rather than the process. Most often people fall in love with the data and their fancy analysis and lose sight of the purpose. Avoid that trap by co-opting someone who is unlikely to enjoy that process!!!

El análisis de datos es mucho más profundo, incluye definición de variables, áreas involucradas, impacto, etc. Más allá del método el “why” es prioritario.

Use Automated Tools: Leverage automated data analysis tools and algorithms to expedite the analysis process. Automation can handle repetitive tasks, allowing you to focus on interpreting results and making decisions. Embrace Data Visualization: Use data visualization techniques to quickly communicate insights. Visualizations, such as charts and graphs, can convey complex information in a digestible format, making it easier to draw conclusions without extensive analysis. Implement Real-Time Analytics: Where feasible, use real-time analytics. This allows you to access the most up-to-date information and make decisions promptly, minimizing the need for prolonged analysis periods.

Contar con información relevante (sí, aunque parezca obvio), sistematizada y visual. Entender que las métricas evaluadas son dinámicas y deben estar acorde al contexto económico del negocio, quizá a contraciclo, es decir, en tiempos buenos centrarse en hallazgos de posibles flancos débiles que puedan ser explotables por la competencia.

Consider the human element in decision-making. While data is crucial, it's essential to factor in the human experience, intuition, and context. Sometimes, qualitative insights, anecdotes, or unique perspectives can provide valuable context that quantitative data alone may not capture. Encourage open communication within your team and be receptive to diverse viewpoints to enrich the decision-making process. Additionally, stay mindful of ethical considerations related to data usage and decision outcomes to ensure responsible and equitable decision-making practices.