Introduction Most organizations involved in advanced analytics are using big data to feed their AI projects. Many analytics teams are familiar with data in Hadoop and Spark, but are often much less fluent in legacy data sources, such as data from relational databases, enterprise data warehouses, and applications running on mainframes and high-end server platforms. Tools for AI and ML are targeted toward data in widely accessible modern formats, and legacy data structures are often arcane, created in an era when storage and memory were at a premium. Exacerbating the challenge of using legacy data, mainframe ops and skillsets are often specialized and siloed, and there are few ways to bridge these siloes. This paper explains why you need to incorporate legacy data in your analyti

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