TDWI: "In today's economic environment, organizations must use business intelligence (BI) to make smarter, faster decisions. Yet, in too many organizations, decisions are still not based on business intelligence because of the inability to keep up with demand for information and analytics. One way to satisfy this demand is to set up a self-service BI (SS BI) environment. Self-service BI offers an environment in which information workers can create and access specific sets of BI reports, queries, and analytics themselves—without IT intervention. This approach extends the reach and scope of BI applications to address a wider range of business needs and problems. At the same time, this extension must support the information workers’ need for a personalized and collaborative decision-making environment. Information workers must become more self-sufficient by having a BI environment that is more usable and more consumable. It is these two themes—usability and consumability—that play crucial roles in a fully functioning self-service business intelligence (SS BI) environment."
Self-service BI is defined as the facilities within the BI environment that enable BI users to become more self-reliant and less dependent on the IT organization. These facilities focus on four main objectives: easy access to source data for reporting and analysis, easy-to-use BI tools and improved support for data analysis, fast-to-deploy and easy-to-manage data warehouse options such as appliances and cloud computing, and simpler and customizable end-user interfaces.
TDWI provides a report describing the technological underpinnings of these four objectives in great detail while recognizing that there are two opposing forces at work—the need for IT to control the creation and distribution of BI assets and the demand from information workers to have freedom and flexibility without requiring IT help.
Some of the interesting bits of the report...
What are the benefits to IT of Self-Service BI? In their research, TDWI noted that IT garnered a number of key benefits from the deployment of SS BI as well. • IT can often step aside as the intermediary Self-service capabilities give users what they have wanted for years—more hands-on control over the information they access and use. This directly leads to better satisfaction with BI and IT in general. • IT can focus on more value-added activities Rather than constantly being pulled off projects to create a new report or analytic, IT can focus on more value-added activities. For example, they can concentrate on developing new applications, expanding data in the data warehouse from existing and new sources, improving data quality processing, and incorporating new technologies to improve performance. • IT becomes a partner to business users rather than a roadblock Business users move into a role that is more responsible for BI capabilities. IT moves into a role that better supports business needs. Both sides become respected partners in the organization.
What do they have to say about SaaS BI? Cloud computing is gaining significant attention from organizations because it potentially offers a Industry interest in faster and lower-cost approach to developing, deploying, and maintaining IT applications. From a BI cloud computing and and data warehousing perspective, there are three aspects to cloud computing that are of interest: • Packaged software-as-service (SaaS) BI applications that can be deployed in a cloud environment. Like on-premises packaged BI solutions, these solutions offer the benefits of fast development and deployment as well as on-demand and elastic scalability. Deploying packaged applications in a cloud environment provides the additional benefit of a pay-as-you-use subscription model with lower up-front and predictable costs, which reduces risk. • SaaS BI tools that can be used to develop BI applications for deployment in a cloud computing or on-premises environment. This approach reduces the need to install in-house development tools, which speeds development and possibly lowers costs. It is also a good approach for evaluating tools and building prototypes. • Data warehousing in the cloud. This approach is discussed in the section “Cloud-Based Data Platforms”.