According to Gartner’s Magic Quadrant for Business Intelligence Platforms, modern analytics and business intelligence platforms represent mainstream buying, with deployments increasingly cloud-based. Data and analytics leaders are upgrading traditional solutions as well as expanding portfolios with new vendors as the market innovates on ease of use and augmented analytics.

By 2020, augmented analytics — a paradigm that includes natural language query and narration, augmented data preparation, automated advanced analytics and visual-based data discovery capabilities — will be a dominant driver of new purchases of business intelligence, analytics and data science and machine learning platforms and of embedded analytics.

By 2020, the number of users of modern business intelligence and analytics platforms that are differentiated by augmented data discovery capabilities will grow at twice the rate — and deliver twice the business value — of those that are not. By 2020, natural-language generation and artificial intelligence will be a standard feature of 90% of modern business intelligence platforms. By 2020, 50% of analytical queries will be generated via search, natural-language processing or voice, or will be automatically generated. By 2020, organizations that offer users access to a curated catalog of internal and external data will derive twice as much business value from analytics investments as those that do not.

Visual-based data discovery is a defining feature of the modern analytics and business intelligence (BI) platform. This wave of disruption began in around 2004, and has since transformed the market and new buying trends away from IT-centric system of record (SOR) reporting to business-centric agile analytics with self-service. Modern analytics and BI platforms are characterized by easy-to-use tools that support a full range of analytic workflow capabilities. They do not require significant involvement from IT to predefine data models upfront as a prerequisite to analysis, and in some cases, will automatically generate a reusable data model.

A self-contained in-memory columnar engine facilitates exploration, but also rapid prototyping. Modern analytics and BI platforms may optionally source from traditional IT-modeled data structures to promote governance and re-usability across the entire organization. Many organizations may start their modernization efforts by extending IT-modeled structures in an agile manner and combining them with new and multi-structured data sources. Other organizations, meanwhile, may use the analytic engine within the modern analytics and BI platform as an alternative to a traditional data warehouse. This approach is usually only appropriate for small or midsize organizations with relatively clean data from a limited number of source systems. The rise in the use of data lakes and the logical data warehouse also dovetail with the capabilities of a modern analytics and BI platform that can ingest these less-modeled data sources (see "Derive Value From Data Lakes Using Analytics Design Patterns").