What is a Semantic Layer? (Components and Enterprise Applications)

Over the last decade, many organizations went through expensive migrations – either moving data into a data lake, a data warehouse, a modern datastack, or to the cloud. Yet, the business problems that many are looking to solve through these transformation initiatives still persist, including: Related data is fragmented, and information is not accessible at the time of need, resulting in siloed decisions and missing holistic context; Business meaning and knowledge is lost, despite expensive migrations; Date teams are struggling to collaborate effectively with business, domain/content owners, and data consumers; Complex infrastructure and proprietary platforms make it hard to enable consistent or meaningful connections, resulting in vendor lock as well as compliance, security, and regulatory violations; and The pace and dynamism of data affects trust in and the integrity of evolving data, resulting in stifled automation and progress towards innovation and enterprise AI. So, what is a semantic layer and how does it address these challenges? Back in 2020, I first discussed a Semantic Layer through a white paper I published, What is a Semantic Architecture and How do I Build One?. In 2021, Gartner dubbed it “a data fabric/data mesh architecture and key to modernizing enterprise data management.” As the field continues to evolve and technical capabilities advance with developments in data and AI solutions, I have broken down the definition based on this fast-paced industry maturity to reflect the latest developments.