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Any data quality project requires the investigation of metadata - the data about your data - to understand your information at a macro level.
DataFlux technology can scan any sort of data to determine its associated metadata - data that indicates the characteristics present within the data, such as data type, field length, whether the data should be unique, and whether a field can be missing or null.
A complete metadata analysis helps determine if the data matches the expectations of the developer when the data files were created. Has the data migrated from its initial intention over time? Has the purpose, meaning and content of the data been intentionally altered since it was first created? By answering these questions, DataFlux solutions help you make decisions about how to use the data moving forward.
DataFlux can catalog metadata across an organization and, using the metadata, group similar types of data into projects. This provides a starting point to understanding relationships across data sources to facilitate data consolidation and master data management projects.
When data and metadata disagree
Oftentimes, data and metadata do not agree, causing far-reaching implications for your data quality and data integration efforts.
For example, consider a 10 million row field with a field length of 255 characters. If the longest data element in the data is 200 characters, the field length is longer than required, and you are wasting 550MB of disk space. Missing values in a field that should not have missing values can cause joins to fail and reports to yield erroneous results. The figure above shows the types of information that a typical metadata report should contain.