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

Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and the consumption and feedback of its products, services, production, marketing, operations, and management, etc. They involve enterprise DNA associated with domain-oriented transactions and master data, informational and operational metadata, and relevant external data. A critical challenge in enterprise data science is to enable an effective ‘whole-of-enterprise’ data understanding and data-driven discovery and decision-making on all-round enterprise DNA. Accordingly, here we introduce a neural encoder Table2Vec for automated universal representation learning of entities such as customers from all-round enterprise DNA with automated data characteristics analysis and data quality augmentation. The learned universal representations serve as representative and benchmarkable enterprise data genomes (similar to biological genomes and DNA in organisms) and can be used for enterprise-wide and domain-specific learning tasks. Table2Vec integrates automated universal representation learning on low-quality enterprise data and downstream learning tasks. Such automated universal enterprise representation and learning cannot be addressed by existing enterprise data warehouses (EDWs), business intelligence and corporate analytics systems, where ‘enterprise big tables’ are constructed with reporting and analytics conducted by specific analysts on respective domain subjects and goals. It addresses critical limitations and gaps of existing representation learning, enterprise analytics and cloud analytics, which are analytical subject, task and data-specific, creating analytical silos in an enterprise. We illustrate Table2Vec in characterizing all-round customer data DNA in an enterprise on complex heterogeneous multi-relational big tables to build universal customer vector representations. The learned universal representation of each customer is all-round, representative and benchmarkable to support both enterprise-wide and domain-specific learning goals and tasks in enterprise data science. Table2Vec significantly outperforms the existing shallow, boosting and deep learning methods typically used for enterprise analytics. We further discuss the research opportunities, directions and applications of automated universal enterprise representation and learning and the learned enterprise data DNA for automated, all-purpose, whole-of-enterprise and ethical machine learning and data science. Introduction Data science plays a pivotal scientific role in transforming enterprise innovation and thinking1–3. Enterprise data science often involves a handful of heterogeneous relational and non-relational databases, enterprise data warehouses (EDWs) or marts, and other repositories from third parties or external resources. A common practice in data warehouse-driven business intelligence (BI) is to create an ‘enterprise big table’ consisting of domain-specific transactions and master data and informational and operational metadata about customers, businesses and their consumption of products and services from the enterprise4,5. Such a mixed deep-and-wide big table is then manipulated by specific business analysts and units to understand and analyze their domain subjects of interest and tasks, e.g., related to customers, products, services, productivity, marketing, competition, new product promotion, and interactions with customers. This subject-oriented enterprise analytics has been on the standard agenda of many enterprises for their data strategies and enterprise data science, becoming increasingly adopted to achieve better business performance, transformation, and decision-making. However, such subject-oriented BI practice promoted by multinational vendors and implemented in numerous enterprises face some critical design limitations and technical and functional shortage. A fundamental issue is that no ‘whole-of-enterprise data science’ thinking, objectives, tasks and techniques are available to connect all-round but siloed businesses, processes, systems and data, as enterprise BI was deemed to address. Instead, most enterprise analytics actually ‘decouples’ enterprise businesses and data to specific domains and subject-specific analytical objectives, tasks and data, which are then addressed by