Marketing analytics skills are among the most important capabilities in the marketing organization, yet it remains difficult to recruit, hire and retain people with strong skills to support in-house teams. Most marketing teams still struggle with a skills gap in this domain. As a result, marketers seek to augment internal teams by using advanced analytics service providers that offer third-party expert resources, proprietary methodologies and models, and even managed technology to help marketers tackle some of their toughest challenges. Vendors in this market specialize in advanced analytics, including sophisticated methods such as mapping the customer journey, attributing marketing spend to measured outcomes, simulating and measuring business impact of marketing and advertising campaigns, and implementing predictive models. Engagements may be project-based or part of an ongoing partnership, and may include the use of proprietary technology. (Retired as of Jul-02-2026).
Analytics and business intelligence (ABI) platforms prepare, model, analyze and visualize data to support decision making. They deliver insights through AI-powered conversational experiences, interactive dashboards and classic reporting. They support collaboration between business and technical users for defining the dimensions, measures and business rules used to create and maintain semantic models. The platforms provide functionality for agentic analytics, where AI agents coordinate tasks across the data-to-insight workflow to automate insight delivery under governance and audit controls. 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.
Gartner defines a data science and machine learning platform as an integrated set of code-based libraries and low-code tooling. These platforms support the independent use and collaboration among data scientists and their business and IT counterparts, with automation and AI assistance through all stages of the data science life cycle, including business understanding, data access and preparation, model creation and sharing of insights. They also support engineering workflows, including the creation of data, feature, deployment and testing pipelines. The platforms are provided via desktop client or browser with supporting compute instances or as a fully managed cloud offering.
Data and Analytics refers to products and services that enable organizations to collect, integrate, analyze, and act on data to drive informed decision-making and business outcomes. This category includes markets that focus on empowering enterprises to manage data pipelines, ensure data quality and governance, extract insights through advanced analytics, and machine learning across structured and unstructured data environments.
Gartner defines decision intelligence platforms (DIPs) as software to create decision-centric solutions that support, augment and automate decision making of humans or machines, powered by the composition of data, analytics, knowledge and AI. DIPs enable enterprises to collaboratively design and explicitly model decisions, orchestrate decision flow during execution at scale, and enable monitoring and governance of decision quality, while learning from actions and outcomes. Features can include a combination of rule- and logic-based techniques, machine learning, real-time event stream processing, business intelligence, multimodal data and analytics preparation, natural language, graph technology, optimization, simulation or AI agents for decision intelligence. DIPs provide a solution to enhance how organizations make decisions, whether by humans or machines, individually or collectively. They address the growing challenge of making timely and accurate decisions in volatile, uncertain, complex and ambiguous ecosystems, for more demanding customers in disruptive, competitive and regulated markets. DIPs help by creating executable decision models that improve decision service composition and all-source intelligence to achieve better situational awareness, better recommendations or autonomous actions, tailored to specific decisions and outcomes. They can reduce the risk of poor decisions, allow organizations to anticipate change and respond more swiftly to opportunities at scale.
Finance refers to the products and services that support the planning, management, analysis, and optimization of financial operations across enterprises and financial institutions. This category includes markets that support core accounting, financial planning, treasury, tax, audit, compliance, investment management, and digital banking—enabling organizations to maintain financial integrity and ensure regulatory compliance.
Gartner defines supply chain network design as the optimization of the location and function of supply, manufacturing and distribution networks in support of an overarching company strategy and customer requirements. Supply chain network design tools support the creation of network models with the application of advanced analytics to determine the optimal supply chain design in a structured, scalable and repeatable way. Supply chain network design tools support the determination of recommendations about the structure of the supply chain network. This includes decisions about facility locations and size, transport lanes, and modes in the end-to-end supply chain. The scale of the changes being evaluated varies from small modifications, such as changing a mode of transport or swapping transportation lanes, to large-scale changes that involve opening/closing/repurposing several facilities in the network and the associated impacts.