More and mo re organizations accept that data lineage is a key capability for BI groups to manage their data properly, and that without it data stewards are unabl e to perform the root cause analysis necessary to identify and consequently remedy data quality issues. However, many companies struggle to get on-dem and access to accurate and reliable data lineage mappings via traditional d ata lineage methods because the complexity of the data flows from the vario us sources to the business intelligence environments demands manual review, inspection, and documentation. Getting metadata out of reporting systems i s perhaps one of the most common challenges preventing complete data lineag e.
\nOrganizations need to introduce techniques, procedures , and tools that will operationalize the different aspects of data quality measurement, reporting, analysis, and remediation, as well as modern techno logies that leverage new algorithms and machine learning to automate how da ta lineage maps are built - beyond processing sequences. This session explores:
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