Gartner defines analytics and business intelligence platforms (ABI) as those that enable organizations to model, analyze and visualize data to support informed decision making and value creation. These platforms facilitate the preparation of data and the creation of interactive dashboards, reports and visualizations to uncover patterns, predict trends and optimize operations. By doing so, they empower users to collaborate and effectively communicate the dimensions and measures that drive their organization. The platforms may also optionally include the ability to create, modify or enrich a semantic model, including business rules. Analytics and business intelligence platforms integrate data from multiple sources, such as databases, spreadsheets, cloud services and external data feeds, to provide a unified view of data, breaking down silos and transforming raw data into meaningful insights. They also allow users to clean, transform and prepare data for analysis, in addition to creating data models that define relationships between different data entities.
The supply chain A&DI technology market spans capabilities that provide different types of analytics, focusing on predictive and prescriptive ones. Many of these offerings have been enhanced with AI and DSML capabilities to support supply chain decision making. These capabilities could either be part of a broader supply chain application/suite or a separate encompassing A&DI platform. Such a platform consists of existing and emerging technologies, including: Graph technology, Advanced analytics, AI, DSML, Model development & Digital supply chain twin (DSCT).
Data preparation is an iterative and agile process for finding, combining, cleaning, transforming and sharing curated datasets for various data and analytics use cases including analytics/business intelligence (BI), data science/machine learning (ML) and self-service data integration. Data preparation tools promise faster time to delivery of integrated and curated data by allowing business users including analysts, citizen integrators, data engineers and citizen data scientists to integrate internal and external datasets for their use cases. Furthermore, they allow users to identify anomalies and patterns and improve and review the data quality of their findings in a repeatable fashion. Some tools embed ML algorithms that augment and, in some cases, completely automate certain repeatable and mundane data preparation tasks. Reduced time to delivery of data and insight is at the heart of this market.
Gartner defined Embedded analytics: Allows analytics, data science or low-code application platform (LCAP) features to be included in a business workflow via APIs. Analytics outputs can either be included in host business applications or be exposed in extranet applications to customers, suppliers or partners. Embedded analytics tends to be narrowly deployed around specific processes, such as marketing campaign optimization, sales lead conversions, inventory demand planning and financial budgeting. It is software that delivers real-time reporting, interactive data visualization and/or advanced analytics — including machine learning — directly into an enterprise business application.