Como você lida com os desafios de limpeza de dados?

Understanding your data is a crucial step in any data analysis project. Data is the raw material that you use to generate insights, make decisions, and communicate your findings. However, data can also be complex, messy, and overwhelming. Therefore, you need to have some strategies to help you understand your data better and make the most of it.

Data Cleaning is the most critical step in any Data Analytics and Machine Learning project. Bad data leads to bad decisions. Step 0: Don’t apply any missing value/outlier treatment at the beginning. Step 1: Understand the business processes that generate the data (which we are going to use for analysis). Step 2: Understand the business rules and transformation rules that were applied previously. Often, the rules applied are inconsistent or irrelevant for your Data Analytics and Machine Learning Project objectives. Step 3: Create your own data transformation rules by consulting with a business domain expert. Step 4: Codify those rules. By this step, you might have solved most of your data quality issues. Step 5: You can apply step 0 now.

Data problems are issues that affect the quality, accuracy, completeness, timeliness, or usability of data. They can have serious consequences for businesses, such as poor decision-making, reduced customer satisfaction, increased operational costs, and compliance risks. Therefore, it is important to identify and resolve them as soon as possible. However, it is also important to note that identifying data problems is not a one-time activity but a continuous process that requires regular monitoring and evaluation. Moreover, identifying data problems is not enough; one also needs to solve them by implementing appropriate solutions that address the root causes and prevent recurrence.

Data problems are common challenges that many organizations face when dealing with large and complex datasets. Data problems can affect the quality, accuracy, reliability, and usability of the data, which can have negative impacts on decision-making, analysis, reporting, and business performance. Therefore, it is important to identify and fix data problems as soon as possible. There are different types of data problems, such as missing values, duplicates, inconsistencies, errors, outliers, and anomalies. Some data problems are easy to detect and correct, while others may require more sophisticated methods and tools. Fixing data problems can be a challenging but rewarding task that can enhance your organization’s data capabilities and value

Data cleaning is a vital process for ensuring the quality and accuracy of your data. Data cleaning involves identifying and resolving errors, inconsistencies, outliers, duplicates, and missing values in your data. Data cleaning can improve your data analysis, reduce bias, and enhance your decision-making. Documenting your data cleaning is a good practice that can help you keep track of the steps you took to clean your data, the reasons behind your choices, and the results of your actions. Documenting your data cleaning can also help you share your data and methods with others, reproduce your analysis, and verify your findings. Documenting your data cleaning is an essential part of ensuring the validity and reliability of your data.

Patience. Patience. Patience. Data cleaning is the most important step of the process, as they say “Garbage in, Garbage out”. You want to take the time needed to have accurate data. Use visualizations to see how the data looks. This also helps to decide next steps regarding outliers, missing values or inaccuracies in data entry. Always remember to DOCUMENT for others who come behind you. This helps everyone know what you did and most importantly WHY the step was done.