The journal seeks to transform data analysis into a competent tool for scientific research and engineering applications, and to distinguish it from mere data processing. Unlike data processing, which relies on established procedures and parameters, data analysis encompasses in-depth study in order to extract physical understanding. A further distinction the journal makes is the need to modify data analysis methodology (thus, “adaptive”) to accommodate the complexity of scientific phenomena.
from the interdisciplinary scientific journal Advances in Adaptive Data Analysis
As my regular readers will know, I have extensively covered the evolution of intelligence utilization in the procurement process from the static realms of ERP-centric financial applications through to the adaptive agent-based modeling platforms upon which emerging solutions are based.
The basis for this enthusiasm or passion is tied to my years of leading the development of a government funded research initiative in which advanced algorithms were used to ensure “best value” decision-making on the front lines. In short, the process was used to identify, gather and apply real-world, real-time data to purchasing decisions as they were happening.
When the research began in 1998, the idea of “automated” real-time analysis wasn’t even on the radar screen. Even in 2006, the concept of spend intelligence was roundly criticized in the mainstream, as demonstrated by the following comments from a leading supply chain blogger:
“I believe the phrase “spend intelligence” to be misleading. To me, it sounds like a new take or sub-segment of business intelligence software applications which offer analytics and dashboard capabilities and sit on top of existing systems of record. The problem is that spend visibility and analytics is much more complex, requiring data cleansing, rationalization, classification and other efforts which go far beyond what is needed to gain insight into basic HR, financials, IT and other internal information, which fall cleanly in to the BI camp.”
In all fairness to the blogger referenced above, he was not alone in his position. Nor was it an unreasonable assessment I would write in an August 11th, 2009 post, given the fact that “this dismissive view of spend intelligence is representative of the limitations associated with traditional equation-based ERP applications in which the cycle to extract, let alone analyze meaningful data was onerous.”
“In other words,” I would add, “unless you understood the basic differences between an equation-based versus an agent-based approach to solution development you would quite logically make the same assumption,” as your focus at the time would be on “IT or ERP-centric applications, and the limited framework within which they operated.”
This is an important distinction in that even though models such as Software as a Service (SaaS), and the new spend intelligence and analytics platforms are often referred to as breakthrough, innovative technologies, in reality they are not new. The foundations for these programs are sound and have been proven over many years. The difference is the current growing recognition and acceptance of these solutions in the mainstream as viable and reliable alternatives to the traditional, and largely static applications of the past.
What this means is that the market is catching-up with the adaptive intelligence solutions offered by organizations such as Rosslyn Analytics. A truth that is reflected by recent news including the January 12th, 2010 announcement that Rosslyn had partnered with COA Solutions. Under the agreement, the UK’s leading supplier of integrated management and information systems (COA) will provide its financial management system (FMS) customers with Rosslyn’s RA.Pid integrated web-based spend analytics service.
“This agreement,” Rosslyn Analytics CEO Charles Clarke stated, “is recognition that RA.Pid Enterprise is the perfect platform for partners to build their SaaS spend analytics solutions using proven technology and innovative reporting capabilities.”
RA.Pid, which the company calls “the world’s first free spend analysis service” is a SaaS cloud platform that “automatically transforms data into actionable, on-demand intelligence in minutes,” which of course brings us back full circle to the beginning of today’s post.
Whether in the cloud (re Internet), or SaaS-based both of which are of course key technical and model elements of emerging solutions, how it works is not as important as what it delivers. The ability to transform data into actionable, on-demand intelligence in minutes was the primary goal of the research that started in 1998. It’s “real-time” manifestation in the form of the practical solutions offered by Rosslyn Analytics today, clearly indicates that we have come a long way in terms of adaptive intelligence capability and utilization.
As it turns out, so too has the attitudes and understanding of the market, which has progressed considerably from pundit opinions that the “use and definition of the phrase, spend intelligence,” which some contended was simply “an attempt to shoot some Botox into a segment of the Spend Management market,”or the suggestion that the term Spend Intelligence “is misleading” are now faint voices in an emerging SaaS world.