Integrating Data

Overcome the challenges appreciate the varieties and apply the process of data integration. Learn all about data integration and become a data integration hero instead of following the masses and running in the opposite direction at the mere mention of the word integration. Understand why organizations avoid data integration and often wind up with spider web environments containing siloed applications instead of an enterprise database which excites analysts and data scientists. Distinguish the different types of integration: database attribute key index encoding measurement format definition KPI calculations summarization selection criteria data exclusion lineage and timing. Apply identification equivocation and physical conversion levels of integration for both structured and textual data. Leverage deidentification proximity analysis alternate spelling stop word resolution homographic resolution stemming taxonomical resolution inline contextualization classification and acronym resolution. Learn how to combine structured and textual data in the context of three levels of interaction. Follow the steps of scope model and map in integrating structured data. Follow the steps of scope connect taxonomies ingest raw text and determine analytical processes in integrating textual data. Apply integration best practices including identifying integration roles developing a reusable data integration process and documenting the integration benefits. Compare taxonomies with data models. Know how data integration helps data science. To reinforce all of the concepts within the book we include a detailed case study on data integration.