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Data Catalogs and Metadata Management

Data Profiling

Data profiling in a data catalog is critical to improving the usability of data understanding and quality. It enriches metadata by providing detailed insights into data, such as types and patterns. Identifies errors or duplicates, supporting data management by ensuring accuracy and consistency, including optimizing ETL and analysis processes.
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How to implement Data Profiling?

As C&F, we use data profiling in many aspects. We enrich metadata by providing detailed information about the data, such as data types, frequency of occurrence, ranges, and patterns in the data. We improve data quality by identifying and flagging missing values, errors, duplicates, or inconsistencies. We support data management to keep data accurate, consistent, and up-to-date. We optimize ETL and analysis processes for understanding data structure to ensure better ETL process design and more efficient and precise data analysis.

Ensuring data quality

Identify data issues such as inconsistencies, inaccuracies and missing values, ensuring data accuracy and reliability. Proactively manage data quality and you will experience significant improvements in your decision-making ability.

Facilitating data integration

Reduce the time and effort required to resolve data integration issues, increasing overall efficiency. By understanding the characteristics of data from different sources, data profiling will ensure that data is compatible and properly aligned, facilitating seamless data integration.

Enabling advanced analytics and Machine Learning

Improve the performance of your AI and ML models. High-quality, well-profiled data is essential for advanced analytics and machine learning applications. Data profiling ensures that the data used in these applications is clean and reliable, improving the accuracy and reliability of predictive models.

Enhancing user confidence and trust

Increase user trust by providing a clear understanding of data quality and characteristics. Users will be more likely to trust and rely on data that has been accurately profiled and verified which will significantly improve adoptions.

Our technical challenges often include handling large amounts of data efficiently and ensuring that profiling results are helpful. To overcome these, we use cloud-based solutions to provide the scalability to process large data sets. Implementing data profiling as part of our ETL/ELT workflows ensures that data quality is maintained before it reaches our data warehouse. Moreover, improving our data integration and transformation processes based on profiling ensures that data from different sources is compatible and aligned. This is critical to maintaining data integrity in our systems. For advanced analytics and machine learning, profiling ensures that the data used in models is clean and reliable, improving accuracy and performance.

Overview

Data profiling refers to examining, analyzing, and reviewing source data to better understand the structure, content, and interrelationships between data. The data profiling process involves using data profiling tools and analytical algorithms to detect characteristics such as mean, minimum, and maximum, and analyzing datasets to uncover metadata. As more organizations handle large amounts of data and begin moving to the cloud, data profiling becomes especially important for business intelligence and analytics. It allows enterprises to identify data quality issues, eliminate costly errors, support data management and integration, and enhance user confidence and trust. 

Experts in the field

Dariusz Wojtas
Senior Program Manager
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Piotr Drozd
Senior Director
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