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11/5/15 Data Profiling Helena Mallards DEI/IST References Slides Data Profiling course, Felix Baumann, Trent, July 2015 Z. Abidjan, L. Golan, F. Baumann, Profiling Relational Data A Survey, LBJ 2015

How to fill out data profiling

How to fill out data profiling?

1. Start by gathering all the relevant data sources that you want to profile. This can include databases, spreadsheets, files, or any other data repositories.
2. Analyze the data sources to understand their structure and format. This will help you determine how to effectively profile the data. Identify any anomalies, missing values, or inconsistencies in the data.
3. Use a data profiling tool or software to automate the profiling process. This will save time and provide more accurate results compared to manual profiling. The tool should be able to perform tasks such as data validation, data quality checks, and statistical analysis.
4. Specify the profiling objectives based on your specific needs. This could include identifying duplicate records, determining data completeness, assessing data integrity, or discovering patterns and trends in the data.
5. Run the data profiling tool on the gathered data sources. Allow the tool to analyze the data and generate reports or visualizations that highlight the characteristics of the data. This will provide insights into the data quality, structure, and potential issues.
6. Review the profiling results and identify any data quality issues or anomalies that need to be addressed. These could be missing or inconsistent data, duplicate records, or outliers.
7. Clean and transform the data based on the identified issues. This could involve standardizing formats, filling in missing values, removing duplicates, or performing data integration.
8. Validate the cleaned data to ensure its accuracy and completeness. Use validation techniques such as data sampling, cross-checking with external sources, or running data queries to verify the cleaned data.
9. Document the data profiling process, including the steps taken, the tools used, and the results obtained. This documentation will serve as a reference for future data profiling projects and can help maintain data quality.

Who needs data profiling?

1. Data analysts: Data profiling is essential for data analysts who need to gain a comprehensive understanding of the data they are working with. It helps them identify data quality issues, assess data completeness, and discover meaningful patterns in the data.
2. Data scientists: Data profiling is a crucial step in data preparation for data scientists. It helps them ensure the accuracy and reliability of the data they use for their analyses and models. It also aids in understanding the data distribution and potential biases.
3. Data engineers: Data profiling is beneficial for data engineers who are responsible for data integration and data cleansing tasks. It helps them identify inconsistencies in data sources, detect duplicate records, and ensure data integrity before performing data transformations.
4. Business stakeholders: Data profiling is essential for business stakeholders who rely on data for decision-making. It provides them with insights into the quality and reliability of the data they use, enabling them to make informed decisions based on accurate and trustworthy data.
5. Data governance teams: Data profiling plays a crucial role in data governance initiatives. It helps data governance teams identify data quality issues, establish data standards, and ensure compliance with data regulations and policies.

Overall, data profiling is essential for anyone working with data, as it helps ensure data quality, enhance data understanding, and support effective data-driven decision-making.

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What is data profiling?

Data profiling is the process of examining, analyzing, and summarizing the content and structure of data.

Who is required to file data profiling?

Organizations or individuals who collect, store, or process data are required to file data profiling.

How to fill out data profiling?

Data profiling can be filled out by providing accurate information about the data being analyzed, including its source, quality, and relevance.

What is the purpose of data profiling?

The purpose of data profiling is to understand the characteristics of data and ensure its accuracy and quality.

What information must be reported on data profiling?

Information such as data source, data types, data quality, and data relevance must be reported on data profiling.