**Global:** Organisations increasingly adopt active data architectures—software-defined abstraction layers that decouple data consumption from storage—to enhance governance, metadata management, and integration. A Dresner Advisory Services report highlights growing recognition of this approach’s importance, especially among large firms and BI professionals.
In the evolving landscape of data management, companies are under increasing pressure to maximise the value derived from their data assets. However, existing data architectures often fall short of supporting the complex demands of modern data consumption. IT analyst Howard Dresner has highlighted an emerging model known as the active data architecture, which promises to offer organisations greater flexibility and strategic advantage in their data initiatives.
The active data architecture, as explained by Dresner, functions as a software-defined abstraction layer that decouples the physical storage of data from the various ways it is consumed across an organisation. While it draws inspiration from established concepts such as data meshes and data fabrics—particularly the focus on data as products—it extends beyond the typical frameworks associated with these models.
Organisations cannot purchase an active data architecture as a turnkey solution. Instead, they must construct it themselves by integrating components from a broad spectrum of data disciplines. These include data integration, data engineering, data governance, metadata management, and both operational and analytical data infrastructures. According to Dresner Advisory Services’ latest report titled “Active Data Architecture,” the architecture comprises a suite of capabilities like virtualised and distributed data access, alongside governance and security measures.
“The active data architecture helps to elevate the status and importance of data to the level of a ‘product’ by separating the management, governance, and use of data from the specific technical systems in which it may be housed,” the report states. This separation delivers a layer of abstraction, allowing data to be managed and applied independently of the underlying applications.
Key elements of an active data architecture include data catalogs that utilise metadata, which assists organisations in categorising and discovering datasets. Equally vital is a semantic layer, which translates business-oriented data definitions familiar to users into the technical definitions necessary for data processing and storage.
Dresner Advisory Services conducted a global survey to assess awareness and interest in active data architectures. It found that 28% of respondents now view this architecture as “of critical importance,” marking a slight increase from 26% in 2024. Meanwhile, fewer than 5% of respondents categorised it as unimportant, a decrease from 7% the previous year.
Larger firms based in Western countries tend to regard active data architectures as more crucial. Similarly, employees working in operations, sales and marketing, business intelligence (BI), and IT show greater appreciation for the concept compared to those in data science, finance, strategic planning, or executive management roles.
The report draws a connection between successful BI implementations and the perceived importance of active data architectures. Among organisations considering their BI projects “extremely successful,” 62% rated active data architecture as critically important, with none dismissing its relevance.
“The buildout of an active data architecture approach to accessing, combining, and preparing data speaks to a degree of maturity and sophistication in leveraging data as a strategic asset,” Dresner Advisory Services notes.
Regarding data integration, which is fundamental to active data architecture, the survey indicates that most practitioners rely on batch and bulk integration methods such as ETL/ELT. Use of data virtualisation, real-time event streaming technologies like Apache Kafka, or message-based data movement tools such as RabbitMQ, is less widespread.
Other important facets include metadata management and data governance. The dynamic and distributed nature of active data architectures necessitates robust capabilities to collect, understand, and leverage metadata about data sources, governance policies, models, and metrics.
Semantic layers are highly valued, with 84% of respondents classifying them as critical or important. These layers are essential in providing consistent views of data, ensuring interoperability among tools, and supporting security and control measures.
Among the most sought-after features in these architectures are metadata ingestion, impact analysis, lineage visualisation, integrated data modelling, component modelling, and optimisation capabilities.
Automated governance is another focal area, with organisations prioritising security, privacy, data quality, and the use of open-source technologies in their active data architecture deployments.
Performance and scalability also rank highly. Dresner’s report links the demand for persistence, caching, and distributed query optimisation to the growing use of data virtualisation, which requires such capabilities for effective functioning.
Adaptability is embedded into active data architectures, which explains why dynamic optimisation techniques, enabling adjustments in data placement or integration methods, are gaining traction. Similarly, monitoring mechanisms like key performance indicator (KPI) tracking and API-driven management are becoming instrumental.
In terms of sourcing components, over half of survey participants obtain data integration tools from specialised providers, followed by BI and analytics vendors, metadata and data catalog suppliers, and providers focused on data fabrics and meshes. Other suppliers include database vendors, cloud infrastructure firms, and data governance specialists.
Interest in active data architecture is also rising among software vendors. Some 55% of vendors rated it as critically important, another 21% as very important, and 14% as important. Only a small proportion viewed it as less relevant.
Dresner conducted vendor ratings that placed Dremio and Denodo in a tie for first place, with Pentaho, Palantir, and Informatica tied for third; Fivetran, Cube, and Astera tied for fourth; and Altair securing fifth position.
Commenting on the recognition, Read Maloney, Chief Marketing Officer at Dremio, said, “As organisations race to build agentic applications powered by AI, the ability to deliver governed, real-time, and AI-ready data is becoming the key differentiator. This recognition from Dresner—based entirely on customer feedback—reinforces Dremio’s role in accelerating this shift by providing fast, flexible, and open access to data.”
Despite vendors’ claims that their products offer comprehensive functionality for building active data architectures, Dresner notes that many actually provide only partial solutions covering specific features such as data integration, governance, or metadata management.
The report indicates that there is still a need for greater market education concerning the precise nature of active data architectures, much like the confusion historically surrounding data fabrics and data meshes.
As companies seek to evolve their data strategies, the active data architecture presents a highly configurable and mature approach that may underpin next-generation data management and utilisation practices.
Source: Noah Wire Services
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