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.
Some researchers see knowledge management as an element of business intelligence. They argue that KM is internal-facing BI, sharing the intelligence amongst employees about how to effectively
perform the variety of functions required to make the organization go. Hence, knowledge is managed using many BI techniques. Others contend that a ”true” enterprise-wide knowledge
management solution cannot exist without a BI-based metadata repository. They believe that a metadata repository is the backbone of a KM solution. That is, the BI metadata repository implements a
technical solution that gathers, retains, analyzes and disseminates corporate ”knowledge” to generate a competitive advantage in the market. This intellectual capital (data, information and
knowledge) is seen as both technical and business-related.
Other researchers note that many people forget that the concepts of knowledge management and business intelligence are both rooted in pre-software business management theories and practices. They
claim that technology has served to cloud the definitions. Defining the role of technology in knowledge management and business intelligence – rather than defining technology as
knowledge management and business intelligence – is seen as a way to clarify their distinction.
The attraction of business intelligence is that it offers organizations quick and powerful tools to store, retrieve, model and analyze large amounts of information about their operations and, in
some cases, information from external sources. Vendors of these applications have helped other companies and organizations increase the value of the information that resides in their databases.
Using the analysis functions of business intelligence, firms can look at many aspects of their business operation and identify factors that are affecting its performance.
However, the Achilles’ heel of business intelligence software is its inability to integrate non-quantitative data into its data warehouses or relational databases, its modeling and analysis
applications, and its reporting functions. To examine and analyze an entire business and all of its processes, one cannot rely solely on numeric data. Indeed, estimates from various sources suggest
that up to 80% of business information is not quantitative or structured in a way that can be captured in a relational database. There is too much verbal or documented information that is
unstructured or semi-structured information and, hence, not well suited to the highly structured data requirements of a database application.
BI systems are becoming increasingly more critical to the daily operation of organizations. Data warehousing can be used to empower knowledge workers with information that allows them to make
decisions based on a solid foundation of fact. However, oftentimes only a fraction of the needed information exists on computers; the vast majority of a firm’s intellectual assets exist as
knowledge in the minds of its employees. Researchers now argue that what is needed is a new generation of knowledge-enabled systems that provide the infrastructure needed to capture, cleanse,
store, organize, leverage and disseminate not only data and information, but also information and knowledge that is less easy to codify. That is, systems should be designed that provide a
unified communications platform for sharing tacit and explicit knowledge derived from BI and KM systems. In these systems, data, documents, stories, videos, knowledge experts and decision
models can be identified, mapped and targeted to address new situations.
BI activities should provide knowledge improvement. This means that the effectiveness of business intelligence should measured based on how well it promotes and enhances knowledge, how well it
improves the mental model(s) and understanding of the decision maker(s), and how well it improves decision making and, hence, firm performance. Business intelligence should therefore be
viewed as an integral part of KM. This in no way diminishes the importance of BI activities. Rather, it simply places business intelligence into a larger organizational context – BI
is one of the many knowledge-based activities creating intellectual capital that can be exploited by a firm.
Recent articles by Richard Herschel