Data preparation is an iterative and agile process for finding, combining, cleaning, transforming and sharing curated datasets for various data and analytics use cases including analytics/business intelligence (BI), data science/machine learning (ML) and self-service data integration. Data preparation tools promise faster time to delivery of integrated and curated data by allowing business users including analysts, citizen integrators, data engineers and citizen data scientists to integrate internal and external datasets for their use cases. Furthermore, they allow users to identify anomalies and patterns and improve and review the data quality of their findings in a repeatable fashion. Some tools embed ML algorithms that augment and, in some cases, completely automate certain repeatable and mundane data preparation tasks. Reduced time to delivery of data and insight is at the heart of this market.
Master data management (MDM) is a technology-enabled business discipline where business and IT organizations work together for the uniformity, accuracy, stewardship, semantic consistency and accountability of enterprises’ shared master data assets. Organizations use MDM solutions as part of an MDM strategy, which should be part of a wider enterprise information management (EIM) strategy. An MDM strategy potentially encompasses management of multiple master data domains (e.g., customer, citizen, product, “thing,” asset, person/party, supplier, location, and financial master data domains). Data and analytics (D&A) leaders procure MDM tools for data engineers or less-technical users, such as data stewards.