Matt Aslett's Analyst Perspectives

As enterprises embrace the potential opportunities presented by artificial intelligence (AI), they are quickly finding that good data management is a prerequisite. As was explained in ISG’s State of Generative AI Market Report, AI requires data that is clean, well-organized and compliant with regulatory standards. There are multiple challenges to delivering AI-ready data, including combining structured and unstructured data, ensuring that the combined data can be trusted, and validating that the connections between data and entities are captured and exposed to relevant applications. The need to address these requirements is the driving force behind Progress Software’s data-related product strategy, which brings multiple products together as the Progress Data Platform to help customers accelerate data, analytics and AI projects. Progress Software was founded in 1981 and initially best known for its application development language (now known as the Progress OpenEdge Advanced Business Language). The software provider has been through multiple acquisitions and divestitures in the decades since its formation. Today, Progress offers a diverse portfolio of products that address application development and testing, digital experience, infrastructure management and operations, DevOps, collaboration, project management, data connectivity, data platforms and semantic artificial intelligence. Together, these products generated revenue of $753.4 million in Progress’s fiscal 2024. The company’s revenue increased 8% compared to the previous year, with Progress anticipating revenue to be between $958 million and $970 million in fiscal 2025. A significant driver of this growth is likely to come from the Progress Data Platform, given the provider’s strategic focus on enabling customers to develop, deploy and manage AI-powered applications. Progress Data Platform combines Progress’ MarkLogic data platform, Semaphore semantic AI platform, DataDirect connectivity software and OpenEdge application development and deployment environment. The company also recently announced the launch of Progress Data Cloud, which provides managed hosting of Progress MarkLogic and Progress Semaphore, with plans to add managed versions of the other Progress Data Platform products. Progress MarkLogic, acquired in 2023 for $355 million, is a relatively recent addition to the Progress portfolio. MarkLogic is a multi-model database platform designed to support operational and analytic workloads. It was evaluated in the 2024 ISG Buyers Guides for Data Platforms, Analytic Data Platforms and Operational Data Platforms, with Progress rated as a Provider of Merit in all three reports. Initially developed to store and process data as XML documents, MarkLogic evolved beyond XML with native support for storing data as JSON documents and RDF triples as well as viewing and querying data using SQL. The ability to process and manage structured and unstructured data has made MarkLogic a key data platform to support semantic web and other applications reliant on managing relationships between data entities, with notable success in the media and publishing, healthcare and pharmaceutical, government and finance sectors. In addition to managing structured and unstructured data assets, MarkLogic also offers data harmonization, mastering and enrichment via MarkLogic Data Hub. Progress also announced the addition of MarkLogic Flux for data movement and transformation. Prior to its acquisition by Progress, MarkLogic had expanded its capabilities for managing and analyzing complex data by acquiring what is now Progress Semaphore. Described as a semantic AI platform, Semaphore enables users to create, maintain and enrich knowledge graphs that represent and expose the relationships between data entities. Specifically, Semaphore enables knowledge models, automatically classifies data using machine learning and natural language processing and integrates with existing data and analytics platforms. MarkLogic and Semaphore are the key pillars of Progress’ strategy of enabling customers to develop, deploy and manage AI-powered applications. They are complemented by Progress DataDirect for application and data source connectivity and Progress OpenEdge for application development and deployment. Each of these products is available separately, but the combination as Progress Data Platform is particularly relevant given the increased interest in knowledge graphs to improve trust in AI applications. I have previously written about the role of vector search and retrieval-augmented generation in improving trust in GenAI by providing semantically similar information from trusted enterprise content. I assert that by 2027, almost all enterprises developing applications based on GenAI will invest in data platforms with vector search and retrieval-augmented generation to complement foundation models with proprietary data and content. Knowledge graphs representing relationships between data and information can complement RAG processes by augmenting similarity search and GenAI models with additional information related to relationships between entities not represented by features or attributes stored as vectors. While not all AI workloads benefit from identifying relationships between data and information, knowledge graph proponents claim that this “graph RAG” approach provides an intrinsic functional advantage for those that do. Progress Data Cloud is clearly a work in progress, but the software provider has multiple opportunities for delivering additional value. This Analyst Perspective focuses on the products that make up the Progress Data Platform, but the company’s product portfolio includes a variety of complementary products, including Progress Corticon business rule management and automation and Progress Chef for application infrastructure configuration and deployment. I recommend that all enterprises considering potential data platforms to support AI-powered applications include Progress in the evaluations, especially for use cases that might benefit from knowledge graph functionality for identifying and managing relationships between data. Regards, Matt Aslett

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