The exponential growth of data on entities, persons, etc. is accompanied by a parallel surge in data quality, leading to more accurate clustering, description, accessibility, and interconnectivity. This enhancement extends beyond traditional library ecosystem data, such as authority files, to encompass a broad spectrum of non-library data sources like Wikidata, DBpedia, and OpenAlex. In this context, we present an approach that leverages linked data principles, persistent identifiers, and knowledge-based systems to construct comprehensive representations of author profiles. Rather than resorting to localized storage with isolated data silos, our approach prioritizes the direct ingestion of information from its original sources. This approach, complemented by the integration of harvested data with authors' research outputs indexed in our discovery portal EconBiz, yields a nuanced and thorough overview. The application of Natural Language Processing (NLP), text mining, and Machine Learning (ML) techniques further refines insights, offering diverse perspectives and deepening the understanding of authors. Author profiles provide a multifaceted gateway to understanding a scholar's research contributions. Visual representations enable quick identification of expertise areas and output. Thesaurus- or ML-enriched word tags enhance search capabilities, allowing users to refine queries based on semantically related terms. Co-authorship network visualizations and other features, such as “related authors” expand exploration options, uncovering scholars with similar research interests who are not necessarily co-authors. Additionally, the discovery of other persistent identifiers unlocks new avenues for profile enrichment with diverse information, including citation metrics and research data. We will present the main functionalities of the EconBiz Author Profiles including a glimpse of new developments.