Rethinking Traditional Business Intelligence: Why Analytics are the Fabric of the Future

Traditional Business Intelligence (BI) tools and approaches focus on reporting and analysis as a distinct process and task set which requires a separate application or tool. These same tools are costly, complicated and completely severed from the transaction applications that originated the business-critical data sources. These are challenging barriers that prevent common usage of these tools and invite understandable skepticism even when they are used.
The thesis of “Analytic Fabric” is that insight should be available within the applications used every day, not as a separate process or system that requires different credentials and its own workflow. In an ideal construction, analytics becomes part of the overall fabric (framework or structure) of the application system, leading to more and faster usage and the quick time-to-insight that underlies solid, fact-based decisions.
Analytic Fabric Defined
To be clear, we can describe primary characteristics of an application that recognizes and exploits the value of its data for the benefit of every user. By defining the successful traits of a “data-driven” application that truly delivers this intelligence inside, you can easily think of examples of these hard-working applications in action today.
Process Orientation - A data-driven application helps automate and improve a business process (defined as a collection of inter-related business activities that increase the value of a product / service or support its creation, distribution and management). Ideally, the application should help streamline a process (making it shorter and more deterministic), saving money and time as a result. Delivering analytic insight within the improved process system enables greater confidence in decision making and a ‘closed loop’ to help drive constant improvement.
Intelligent Events - Some of the most advanced data-driven applications can intelligently monitor other systems for business events (or triggers) that can cause a variety of downstream process adjustments to occur. Some of those adjustments require human thought and intervention (such as when a parts shortage requires a product design change) and some can be managed simply between systems (such as a low stock level triggering a standard re-order). The more sophisticated the process, the more likely that events within could be monitored and acted on intelligently, through the use of an analytic application.
Contextual Relevance - The best data-driven applications deliver a broad and complete set of information, all relevant to the decision-maker and task at hand. The key to delivering relevant information successfully is understanding the context of the decision maker. Some relevant information may come from within the application system and its internal data set – presented using tables, graphs, and charts that are appropriate for easy visualization and display. Other information might come from external sources, but add richer context and insight because it is important and complementary and supplied right alongside the internal data. In total, it is the clever blend of information types that enable better decisions and a great data-driven application delivers plenty of information.
Analytical Insight - Fundamentally, a data-driven application is supposed to deliver analytic insight – at the right place and time – so superior understanding of the business and better decisions result. Ideally, that analytic insight comes from a deep understanding of the business domain being analyzed and a complete set of tools made easily available that enhance this process, not hinder it. The key here is delivering simple, powerful access to analytic insight, not complicated power features that are used by few. When the dust settles, a data-driven application better get this part right, or it fails its first test.
While some business applications today include a portion of these traits, it’s notable that every application could benefit from being more thoroughly data-driven. In short, these traits help unlock new value from the insight that often lies dormant inside the system or application data.
Analytics should be a simple and powerful part of every application, built into the very fabric of the business system or process. There is no application outside this thesis, nor any software system that shouldn’t deliver intelligence from inside. The new year (2013) will surely bring greater understanding that information must find the knowledge worker, not the other way around. To this end, I expect data-driven applications and embeddable BI to become more popular than ever before.
What is the biggest value you expect to see from integrated reporting and analytics? Your thoughts and comments are appreciated.

Author: Brian Gentile
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