Data analytics and insights

What Is Data Profiling? Steps and Types of Data Profiling Data Profiling is the analysis of source data to determine how it is structured, what it contains, and how it interacts with other data, as well as the identification of projects that could benefit from it. Data Profiling Procedures Step 1: Conduct data profiling at the beginning of a project to establish if the data is suitable for analysis and if the project should continue. Step 2: Before putting the source data into the target database, identify and resolve any quality concerns with the source data. Step 3: As the data moves from source to destination, look for data quality issues that can be fixed using Extract-Transform-Load (ETL). Profiling data can tell whether additional manual processing is necessary. Step 4: Use unexpected business rules, hierarchical structures, and relationships between foreign keys and private keys to refine the ETL process in step four. Data Profiling Types Content Discovery: An individual

Optimizing Your Data for Success: Data Cleaning Steps & Process Whatever type of data analytics you perform, your analysis and any subsequent processes are only as good as the data you start with. Most raw data, whether text, images, or data stored in spreadsheets, is incorrectly formatted, imperfect, or downright dirty, and must be cleaned and structured before you begin your analysis. To ensure that your data is properly prepared for analysis, you can use a variety of data cleaning, "data cleansing," or "data scrubbing" techniques. Data cleaning is the process of repairing or erasing inaccuracies, corruptions, improperly formatted, duplicate, or incomplete data from a dataset. When different data sources are combined, numerous potential for data duplication or mislabeling exist. Cleaning your data is as simple as following this six-step guide: Get Rid Of Any Information That Isn't Relevant: The first step is to find out what analyses you'll be doing