Year: 2022
Type: article
Abstract: The ability to cross data from multiple sources represents a competitive advantage for organizations. Yet, the governance of the data lifecycle, from the data sources into valuable insights, is largely performed in an ad-hoc or manual manner. This is specifically concerning in scenarios where tens or hundreds of continuously evolving data sources produce semi-structured data. To overcome this challenge, we develop a framework for operationalizing and automating data governance. For the first, we propose a zoned data lake architecture and a set of data governance processes that allow the systematic ingestion, transformation and integration of data from heterogeneous sources, in order to make them readily available for business users. For the second, we propose a set of metadata artifacts that allow the automatic execution of data governance processes, addressing a wide range of data management challenges. We showcase the usefulness of the proposed approach using a real world use case, stemming from the collaborative project with the World Health Organization for the management and analysis of data about Neglected Tropical Diseases. Overall, this work contributes on facilitating organizations the adoption of data-driven strategies into a cohesive framework operationalizing and automating data governance.
Source: Journal Of Big Data
Institution: Universitat Politècnica de Catalunya
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,790
Awards: G1175115578, G1450986124, G1551405195, G1855990336, G2262748287, G2309174360, G2601397997, G2660406951, G300979063, G3480869486, G3518538256, G3681454997, G4042783231, G425668148, G430777504, G5080475149, G5195188938, G5292893947, G5400894464, G5421974903, G561707894, G5805502524, G6366842693, G7084143925, G8130184278, G99296377
OpenAlex ID: W4311927641