How can you tailor your data scrubbing techniques to different data types?

Be aware of the distinctive qualities of each form of data. Choose appropriate methods in light of this: Handle missing values, deal with outliers, and normalize numerical data. Address missing values in categorical data and properly encode. Tokenization, stop word removal, and stemming/lemmatization are the preprocessing steps for text data. Take into account domain-specific needs, such as security or data privacy issues. Utilize methods that maximize data quality and get it ready for modeling or analysis.

Data scrubbing, or data cleansing, is a fundamental practice to ensure the quality and integrity of data. Data scrubbing techniques can be adapted to different types of data based on their specific characteristics and requirements. For structured data, such as relational databases or spreadsheets, data scrubbing techniques can involve validating data formats (for example, date or number formats), correcting syntax errors, and handling null or missing values . In general, the suitability of data scrubbing techniques depends on the specific characteristics of the data and the objectives of the data cleansing process. It is important to carefully evaluate the needs and requirements of the data in question.

In my experience, date formats can pose a significant risk in terms of data cleansing. This is because various regions use different date formats in their source data, which can result in inconsistencies when we collect data globally. To address this issue, it's best to standardize the date format across all regions in our database, following our data warehouse architecture rules. It's also recommended to store the date with timezone information (e.g. UTC) or convert them to a common timezone during analysis. To ensure consistency in date formats, you can use an alternate column to identify the months, such as the MonthName field.

In my opinion, before performing data scrubbing, you must have a good understanding of the data and its types. There are several data types such as textual data (names, descriptions, etc.), numerical data (finance, sales figures, quantities, aggregation, etc.), date and time (timestamps, records modified date), and categorical data (types of products, status codes, regions, markets, etc.) To apply specific techniques to each data type, we use different approaches. For textual data, we use string manipulation techniques to correct typos, standardize naming conventions, and remove unnecessary white spaces. For numerical data, we validate the min, max ranges, identify outliers, and handle them appropriately.

The different inputs into information about opinion are fundamental for Political Science. The big challenge is translating the questions into measurable indicators. Good training can solve the problem.