Having an up-to-date data catalog with the right business context is key for organizations trying to enable users to leverage and understand their data to make better and faster decisions. To add business context, companies need to connect business terms (e.g. First Name, Revenue, Region) to physical columns or fields in data sources (e.g. f_name, column-numeric-sale, state1) which is generally a tedious, manual, and long process. Having the ability to do this accurately and automatically is critical for companies to extract the right value from their data.
Organizations can now automate and simplify this challenge with a patented data fingerprinting process. This process applies unsupervised machine learning techniques to create fingerprints for each column of data, compares those fingerprints, and clusters them into similar and duplicate content columns. Information about column similarity allows BigID to automatically map columns to their most appropriate business terms, eliminating the manual effort required.
Additionally, BigID can suggest business terms so you can choose and apply them automatically across the board.
- BigID clustering capabilities allow to auto-populate business terms for columns, making technical data more accessible and easy to understand to the data catalog users
- Include the right business context in your data to make it easy for security and data governance professionals to find similar datasets to execute security, consolidation, retention, and minimization strategies
- Discover dark and vulnerable data. A scalable approach to locate data, map business terms to physical columns, and easily manage data that you have – and you have no idea you have.
- Group or cluster information based on a fuzzy definition of similarity to discover similar, duplicate, and redundant data – comply with the data retention policies and find similar data with better quality to use in ML models or analytics reports