InetSoft Webinar: BI and Data Services
Below is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of Agile Data Access and Agile Business Intelligence. The presenter is Mark Flaherty, CMO at InetSoft
Mark Flaherty (MF): What we are doing now in the agile BI arena is responding to new requirements fairly rapidly through direct interactions between these various stakeholders, IT and the business with incremental prototyping and iterative prototyping, where it's all about face-to-face interaction that allows you then to quickly move towards an environment where you don’t need to, and shouldn’t, have any big grand unified BI development effort that’s a boil-the-ocean initiative but rather you can build your BI, your reports your queries, your predictive models and so forth in a very iterative fashion.
Each of these development stages delivers a working product essentially that addresses some large or small fine-grain requirements. So you are minimizing your risk moving towards an agile BI approach. So this whole approach for the agile BI involves services infrastructure. Like I said, social technologies, we are seeing more and more uptake of that in the BI and data services arena to enable direct interaction between these different groups, using a common community infrastructure.
Service-Oriented Architecture Context
In terms of integrated BI and data services component management, increasingly, we are seeing that all rolled out in a service-oriented architecture context so you can essentially leverage all of the platform and the infrastructure and the middleware as you have within your agile system’s environment into BI and analytics projects.
A Service-Oriented Architecture (SOA) is a software design approach that structures an application as a collection of loosely coupled, interoperable services. In an SOA, services are self-contained, modular units of functionality that can be accessed and reused across different applications and platforms. These services are designed to be independent of each other, communicating through standardized protocols such as HTTP, SOAP, or REST, and can be orchestrated to perform complex business processes. By decoupling functionality into discrete services, SOA enables greater flexibility, scalability, and agility in developing and deploying software systems. At the heart of SOA is the concept of service abstraction, which hides the underlying implementation details of a service and exposes only its interface or contract to other services and clients.
This abstraction allows services to evolve independently over time, as long as they maintain compatibility with the agreed-upon interface. Additionally, SOA promotes service reusability, allowing organizations to leverage existing services to build new applications or extend existing ones more efficiently. By encapsulating business logic and functionality within reusable services, SOA reduces duplication of effort, promotes modularization, and facilitates rapid development and integration of software systems.
Service Interoperability and Agile Data Access
Another key aspect of SOA is service interoperability, which enables services to communicate and interact with each other regardless of the underlying technologies or platforms they are built on. Services in an SOA are designed to be platform-independent and language-neutral, allowing them to be accessed and invoked by clients implemented in different programming languages or running on different operating systems. This interoperability fosters greater integration and collaboration across heterogeneous IT environments, enabling organizations to leverage existing investments in legacy systems while adopting new technologies and architectures. Overall, SOA provides a flexible and scalable foundation for building distributed systems that can adapt to changing business requirements and technological landscapes.
Where agile BI and agile data access stresses reuse, and that reuse is via service oriented architecture, there is a lot of REST, Representational State Transfer, for building out Web focused BI and data services capabilities through a matching of abstraction layers and similar virtualization schemes that essentially help you to hide or conceal a lot of the complexity of the source data models and schemas and formats and present a unified object model and way of accessing just for information sets in a way that’s fairly and relatively simple and visual in terms of the set of modeling tools.
Agile data access is the core of any agile BI effort. When you look at agile data access, look at it on several levels. Essentially what you need to do to build out an agile data access strategy is that you need to think about refactoring all of your data integration, data persistence, access and presentation and other BI components as the usable capabilities that can be leveraged into new projects fairly quickly.
Modular Data Components and Reusable Assets
As organizations deepen their investment in BI and data services, one of the most important evolutions is the shift toward reusable data components that can be rapidly assembled into new analytical solutions. Instead of treating each dashboard or report as a standalone project, teams are increasingly adopting modular data assets—such as standardized metrics, curated datasets, and reusable transformation blocks—that accelerate development and reduce inconsistencies. This approach aligns naturally with service‑oriented thinking, allowing BI teams to deliver insights faster while maintaining a coherent data foundation across the enterprise.
Another emerging priority is the integration of BI workflows with collaborative environments. Modern teams expect analytics to live alongside their daily communication tools, enabling real‑time discussion, annotation, and shared decision‑making. Embedding dashboards into community platforms or workflow systems helps break down silos between IT, analysts, and business users. This collaborative layer strengthens the feedback loop, ensuring that BI outputs evolve continuously based on real operational needs rather than static project requirements.
Organizations are also recognizing the importance of abstracting away the complexity of underlying data systems. As data environments grow more heterogeneous—spanning cloud warehouses, legacy systems, APIs, and streaming sources—users need a simplified, unified way to access information. Virtualization layers and semantic models play a crucial role here, presenting business‑friendly objects instead of raw schemas. This abstraction not only accelerates development but also empowers non‑technical users to explore data confidently without risking misinterpretation or breaking fragile integrations.
Data Mashup Capabilities and Maintenance Considerations
Operational agility is another area where BI and data services continue to advance. Teams are increasingly adopting iterative delivery cycles, where small, incremental enhancements replace large, monolithic BI projects. This approach reduces risk and ensures that each iteration delivers tangible value. It also encourages experimentation, allowing analysts to prototype new visualizations, test alternative data models, and refine user experiences based on direct feedback. Over time, this iterative rhythm becomes a core cultural element of successful BI programs.
Finally, the expansion of BI and data services is driving a renewed focus on governance and lifecycle management. As more users gain access to self‑service tools, organizations must ensure that data definitions remain consistent, access controls are enforced, and content sprawl is minimized. Modern BI platforms support these needs through role‑based permissions, versioning, lineage tracking, and centralized content catalogs. When governance is embedded directly into the BI workflow, teams can innovate rapidly without compromising accuracy, security, or compliance—creating a scalable foundation for long‑term analytics maturity.