Many confuse knowledge management (KM) with business intelligence (BI). According to a survey by OTR consultancy, 60% of consultants did not understand the difference between the two. Gartner clarifies this by explaining business intelligence as a set of all technologies that gather and analyze data to improve decision making. In business intelligence, intelligence is often defined as the discovery and explanation of hidden, inherent, and decision-relevant contexts in large amounts of business and economic data.
Knowledge management is described as a systematic process of finding, selecting, organizing, distilling and presenting information in a way that improves an employee’s comprehension in a specific area of interest. Knowledge management helps an organization to gain insight and understanding from its own experience. Specific knowledge management activities help focus the organization on acquiring, storing and utilizing knowledge for such things as problem solving, dynamic learning, strategic planning and decision making.
Conceptually, it is easy to comprehend how knowledge can be thought of as an integral component of business intelligence and, hence, decision making. I argue that knowledge management and business intelligence, while differing, need to be considered together as necessarily integrated and mutually critical components in the management of intellectual capital.
Knowledge management has been defined with reference to collaboration, Content Management, organizational behavioral science and technologies. KM technologies incorporate those employed to create, store, retrieve, distribute and analyze structured and unstructured information. Most often, however, knowledge management technologies are thought of in terms of their ability to help process and organize textual information and data so as to enhance search capabilities and to garner meaning and assess relevance so as to help answer questions, realize new opportunities and solve current problems.
In most larger firms, there is a vast aggregation of documents and data, including business documents, forms, databases, spreadsheets, email, news and press articles, technical journals and reports, contracts, and web documents. Knowledge and Content Management applications and technologies are used to search, organize and extract value from these information sources and are the focus of significant research and development activities.
Business intelligence has focused on the similar purpose, but from a different vantage point. Business intelligence concerns itself with decision making using data warehousing and online analytical processing (OLAP) techniques. Data warehousing collects relevant data into a repository, where it is organized and validated so it can serve decision-making objectives. The various stores of the business data are extracted, transformed and loaded from the transactional systems into the data warehouse. An important part of this process is data cleansing where variations in data schemas and data values from disparate transactional systems are resolved. In the data warehouse, a multidimensional model can then be created which supports flexible drill down and roll-up analyses. Tools from various vendors provide end users with query capabilities and a front end to the data warehouse.