What do you do if your data analysis in Systems Management lacks logical reasoning?
Data analysis is a crucial skill for systems management, as it helps you monitor, optimize, and troubleshoot your IT systems. However, data analysis is not just about collecting and presenting data, but also about interpreting and explaining it with logical reasoning. Logical reasoning is the ability to use facts, evidence, and rules to draw valid conclusions and make sound decisions. Without logical reasoning, your data analysis may be flawed, biased, or irrelevant. In this article, you will learn what to do if your data analysis in systems management lacks logical reasoning, and how to improve your analytical thinking skills.
The first step to fix your data analysis is to identify the problem. What is the goal of your analysis? What questions are you trying to answer? What data sources are you using? How are you processing, analyzing, and visualizing your data? How are you communicating your findings and recommendations? By reviewing these aspects of your data analysis, you can pinpoint where you may have gaps, errors, or inconsistencies in your logic. For example, you may have used inappropriate methods, ignored important variables, made false assumptions, or drawn invalid inferences.
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Si carece de razonamiento logico es porque se parte de una serie de premisas erroneas y de un objetivo mal definido, por lo tanto hay que reevaluar lo que se desea, los datos y fuentes a analizar, la coherencia, validez y correlacion de los mismos. Existen numerosos programas de analisis de big data y BI que pueden servir para el analisis de datos y reporting, como PENTAHO, con un modelo OLAP. En caso de que exista mayor complejidad esto llevara a un analisismayor a fin de definir los algoritmos necesarios y armar los cubos de datos relevantes para un analisis mas especifico. Al dia de la fecha, podria ser util ayudarse con las IA disponibles en el mercado, claro esta, teniendo bien definida la logica inicial
The next step is to evaluate your evidence. Evidence is the information that supports your claims, arguments, and conclusions. You need to make sure that your evidence is relevant, reliable, and sufficient for your data analysis. To do this, you can ask yourself some questions, such as: How did you collect your data? Is your data accurate, complete, and up-to-date? How did you validate your data? How did you handle missing, outlier, or conflicting data? How did you choose your sample size and sampling method? How did you control for confounding factors and biases?
The third step is to check your logic. Logic is the system of rules and principles that guides your reasoning and arguments. You need to make sure that your logic is sound, consistent, and coherent for your data analysis. To do this, you can use some tools, such as: Logic models, which are diagrams that show the relationships between inputs, outputs, outcomes, and impacts of your system. Logic tests, which are methods to verify the validity and strength of your arguments, such as deductive reasoning, inductive reasoning, and abductive reasoning. Logic fallacies, which are common errors or flaws in reasoning that can undermine your arguments, such as hasty generalization, false dilemma, or circular reasoning.
The final step is to improve your skills. Data analysis and logical reasoning are skills that can be learned and improved with practice and feedback. You can enhance your skills by doing some activities, such as: Reading books, articles, and blogs on data analysis and logical reasoning, and learning from experts and best practices. Taking courses, workshops, or webinars on data analysis and logical reasoning, and acquiring new knowledge and techniques. Doing exercises, puzzles, and games on data analysis and logical reasoning, and challenging yourself and having fun. Seeking feedback, mentoring, or coaching on data analysis and logical reasoning, and getting constructive criticism and advice.
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