Knowledge and data have long been maintained as separate entities, but is it now time to formally integrate the two. Business intelligence, usually defined as the extraction of insights from structured data, has a long history. It incorporates such previous concepts as decision support, executive information systems, data warehousing, and data mining - but not knowledge management. Its primary focus has been technological; the business intelligence literature is replete with discussions of technologies for extraction, transformation and loading of data, for statistical analysis and for reporting results and formatting them in scorecards. Knowledge management, on the other hand, has a somewhat shorter history, with antecedents in corporate libraries, competitive intelligence, best practices sharing in corporate quality organizations, and knowledge transfer efforts. Its primary focus has been on the capture, sharing and distribution of unstructured textual and graphic information - as opposed to the structured, quantitative orientation of business intelligence. Knowledge management has also had a technological focus (particularly on web-based, repository, and collaborative technologies), but its adherents also place strong emphasis on the need for human and cultural interventions in order to make knowledge sharing work. Many in the knowledge management community have historically shied away from anything having to do with data and information, taking the more purist stance that knowledge management is concerned with knowledge not data or information. During knowledge management's early formative years, knowledge management gurus would define the hierarchy of data, information and knowledge discussing how data gets turned into information and information into knowledge. Knowledge management practitioners would be quick to point out that they worked with knowledge, not data or information. This would, of course, exempt them from having anything to do with business intelligence.
The most important similarity between BI and KM is that both ultimately deal with knowledge. The knowledge in a KM context is obvious and is generally derived directly from human beings. Knowledge in BI is derived from data, but it is certainly knowledge after the analysis has taken place. If a company concludes through its BI efforts, for example, that it can profit by putting its product on sale during the holiday season, that's a piece of knowledge that can be captured, stored, distributed and used like any other knowledge object. In this sense, business intelligence is the process of turning data into knowledge - and then managing that knowledge. In fact, the absence of such KM-oriented activities for certain types of BI-created knowledge (particularly in the area of market research) has been a problem for many companies. They create large numbers of findings from extensive data analysis, but often have no structure or process for capturing and reusing the knowledge over time.
BI and KM also both have strong human dimensions, though this has not often been discussed in the BI context. It's well-known that successful knowledge management relies heavily on people - people who create and share knowledge, people who manage knowledge, and people-related culture and behavior. With business intelligence, the primary focus is on technology and data. People come into play in BI in the form of analysts with expertise in quantitative methods and business problem-solving, executives who make fact-based decisions and establish cultures oriented to them within their organizations, and trusting relationships between analysts and decision-makers. From a technology standpoint, the underlying technologies to analyze data in a BI content, and to manage and distribute knowledge in a KM context, are largely different. The two have in common, however, the front-end technology for access and display of content. Today these are generally referred to as portals, and a portal can easily display data, data-derived knowledge, or human-derived knowledge.
