I promised to follow an earlier article that looked at database management systems as a commodity technology with a similar assessment of business intelligence. In drafting the promised article, however, I realized that I couldn't limit my evaluation to the software side of BI.
BI is complex. It is simultaneously software, transformational work practices, and business information. Admittedly, I am going far beyond IE Editor in Chief Doug Henschen's take on BI, but consider: What value is reporting or OLAP or data mining — software — that doesn't tap all data that contributes to relevant business insights — information — that can help you restructure, realign, or optimize business operations — practices? To understand if BI is a commodity technology, we need to examine all three, complementary aspects of business intelligence: software, information, and practices.
Let's start with information, with BI sources and BI results.Is analytical information a commodity in the sense that you, given your business role, can access and analyze all relevant data to create the guidance you need? The answer is decidedly no.
BI has only recently come to grips with a number of major source-data challenges. Data quality, semantics (meaning), integration, and uncertainty top the list. I won't elaborate other than to guess that you're probably not even thinking about the uncertainty (and lineage) issue, about the utility of measuring and capturing accuracy and trust-worthiness and factoring it into calculations. This uncertainty question is similar to the data-quality question but they are not identical. Data quality most frequently involves how well the data is described rather than the properties of the data values themselves.
And BI has only recently started to accommodate data from "unstructured" sources and from streaming, distributed sources. Complex-event processing (CEP) and data-stream processing tackle the latter challenge, and on the "unstructured" side, we're doing alright with text-sourced data although ability to extract from and analyze images and audio is primitive.
With text analytics in particular, we're finally getting back to BI's origins, to BI's earliest definition as put forward by Hans Peter Luhn in his seminal 1958 IBM Journal paper, "A Business Intelligence System." Luhn's interest in analyzing scientific and technical literature was reflected in the goal he articulated for a BI system of "auto-abstracting and auto-encoding of documents and ... creating interest profiles for each of the 'action points' in an organization." The aim was decision support; the source was text rather than numbers. But numbers are "low-hanging fruit" compared to text, far easier to collect and analyze. Arguably we now have the routine numbers problem licked so we're finally extending BI to other than numbers, e.g., text per Luhn's original vision for BI systems, and to other than routine numbers, e.g., to data and event streams.
On the output side, BI's short-comings as a means of producing actionable information have contributed to the emergence of a new practice, enterprise decision management.
It is not yet practical to access and analyze all data to relevant to many business decision-making needs. Core, by-the-numbers BI is well understood, but BI has a long way to go before it will produce much-talked-about 360o, all-encompassing enterprise views.
Seth Grimes is an analytics strategist with Washington DC based Alta Plana Corporation. He consults on data management and analysis systems.BI is complex, simultaneously software, transformational work practices, and business information. Consider: What value is reporting or OLAP or data mining (software) that doesn't tap whatever data is relevant to produce business insights (information) that can help you restructure, realign, or optimize business operations (practices)? We need to examine all three, complementary aspects of business intelligence: software, information, and practices. Let's start with information, with BI sources and BI results.