“High-level” and “low-level” are terms used to describe and classify systems. High-level systems are generally more abstract than low-level systems, which tend to focus on discrete data specificities within the system rather than on how the system produces information as a whole.
Typical Systems Stack for Bespoke Risk Analytics Production
The graphic above depicts a typical business systems stack that includes data warehousing, integration, and analytical components from multiple vendors. Note that the data warehousing and integration systems appear as low-level components, while the analytical systems appear as high-level components. A key objective of this system is to throughput data into the hands of analysts on a self-help basis.
Over the past decade, systems engineers have worked diligently to install the lower-level components of their systems stacks, including the hardware and software associated with data warehousing and rudimentary integration. However, progress on the upper-level components of these systems stacks has typically lagged. As a result, the realization of the business intelligence (BI) vision in firms has generally been limited to simple performance monitoring with only marginal successes in higher-order analytics production.
The emerging shift in priority from low-level to high-level systems components, and from performance monitoring to higher-order analytics production also means shifting certain decision prerogatives away from information technology (IT) departments toward subject matter experts and analysts. The fact is that BI is not only a production process that requires systems, but also a thinking process that requires both ad hoc and post hoc analysis and testing by subject matter experts. The future of BI requires restoration of the decision support function. Moreover, analysts rather than technologists must assume greater responsibility and leadership over the overall BI effort.
While the shifting emphasis from lower to higher-level systems components brings value-adding potential in the form of higher-level analytics production, this shift also introduces risks and responsibilities that firms must consider in order to better align IT investments with expanding BI requirements. High-level systems components and integration are now the priority.
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