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Enterprise data typically involves multiple heterogeneous data sources andexternal data that respectively record business activities, transactions,customer demographics, status, behaviors, interactions and communications withthe enterprise, and the consumption and feedback of its products, services,production, marketing, operations, and management, etc. A critical challenge inenterprise data science is to enable an effective whole-of-enterprise dataunderstanding and data-driven discovery and decision-making on all-roundenterprise DNA. We introduce a neural encoder Table2Vec for automated universalrepresentation learning of entities such as customers from all-round enterpriseDNA with automated data characteristics analysis and data quality augmentation.The learned universal representations serve as representative and benchmarkableenterprise data genomes and can be used for enterprise-wide and domain-specificlearning tasks. Table2Vec integrates automated universal representationlearning on low-quality enterprise data and downstream learning tasks. Weillustrate Table2Vec in characterizing all-round customer data DNA in anenterprise on complex heterogeneous multi-relational big tables to builduniversal customer vector representations. The learned universal representationof each customer is all-round, representative and benchmarkable to support bothenterprise-wide and domain-specific learning goals and tasks in enterprise datascience. Table2Vec significantly outperforms the existing shallow, boosting anddeep learning methods typically used for enterprise analytics. We furtherdiscuss the research opportunities, directions and applications of automateduniversal enterprise representation and learning and the learned enterprisedata DNA for automated, all-purpose, whole-of-enterprise and ethical machinelearning and data science.

Table2Vec: Automated Universal Representation Learning to Encode All-round Data DNA for Benchmarkable and Explainable Enterprise Data Science