Self-Service Business Intelligence and Analytics in Digital Transformation

Abstract

The digital-transformation era has changed how organizations generate, govern, and consume analytical insight. This paper analyzes the shift from centralized, IT-centric reporting toward self-service business intelligence (BI), in which business users author governed analyses without continuous engineering intervention. The discussion traces the architectural move from on-premises data warehouses to cloud data warehouses and lakehouse platforms that separate compute from storage and unify structured and semi-structured data under open table formats. A layered reference architecture is proposed. It comprises integration, storage and processing, augmented analytics, semantic and governance, and consumption layers. The paper then examines augmented analytics and the use of large language models for natural-language querying and natural-language-to-SQL translation. It pairs these capabilities with the governance controls that make self-service trustworthy at scale: semantic metric layers, row-level security, data catalogs, and lineage. A structured comparison of four leading platforms, namely Microsoft Power BI, Tableau, Google Looker, and Qlik, is presented across authoring, semantic modeling, cloud scalability, augmented analytics, and governance dimensions using illustrative capability scores. A five-level maturity model is introduced to help organizations benchmark their analytical capability and sequence investment. The analysis concludes that durable value comes not from tool selection alone but from coupling self-service freedom with a governed semantic foundation that treats data as a managed product. The metrics reported here are illustrative and synthesized from public industry sources, not from a single deployed study.