Knowledge Management And Business Intelligence

Many confuse knowledge management (KM) with business intelligence (BI). According to a study by OTR consultancy, 60% of consultants did not understand the difference between your two. Gartner clarifies this by explaining business cleverness as a couple of all systems that gather and evaluate data to improve decision making. In business intelligence, intelligence is thought as the discovery and explanation of concealed often, inherent, and decision-relevant contexts in huge amounts of business and economic data.

Knowledge management is referred to as a systematic process of finding, selecting, arranging, distilling and showing information in a way that boosts an employee’s understanding in a particular market. Knowledge management helps a business to get understanding and understanding from its own experience. Specific knowledge management activities help focus the business 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 regarded as an integral element of business cleverness and, hence, decision making. I claim that knowledge management and business intelligence, while differing, need to be considered jointly as always integrated and critical components in the management of intellectual capital mutually.

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Knowledge management has been described with reference to collaboration, content management, organizational behavioral research and systems. KM technologies incorporate those employed to produce, store, retrieve, distribute and analyze unstructured and structured information. Generally 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 reports and journals, contracts, and web documents.

Knowledge and content management applications and technology are used to search, organize and extract value from these given information resources 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 cleverness concerns itself with decision making using data warehousing and online analytical handling (OLAP) techniques. Data warehousing collects relevant data into a repository, where it is arranged and validated so it can serve decision-making goals. The various stores of the business data are extracted, packed and changed from the transactional systems into the data warehouse.

An important part of the 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 be created which supports flexible drill down and roll-up analyses then. Tools from various vendors provide customers with query features and a front end to the info warehouse.

Some experts see knowledge management as an component of business intelligence. They claim that KM is internal-facing BI, writing the intelligence amongst employees about how to effectively perform all of the functions required to make the business go. Hence, knowledge is managed using many BI techniques. Others contend that a ”true” enterprise-wide knowledge management solution cannot exist with out a BI-based metadata repository.

They believe that a metadata repository is the backbone of a KM solution. That’s, the BI metadata repository implements a specialized solution that gathers, retains, analyzes and disseminates corporate and business ”knowledge” to generate a competitive advantage on the market. This intellectual capital (data, information and knowledge) sometimes appears as both specialized and business-related. Other research workers note that many people neglect that the principles of knowledge management and business cleverness are both rooted in pre-software business management theories and procedures. They claim that technology has offered to cloud the definitions.

Defining the role of technology in knowledge management and business cleverness – rather than defining technology as knowledge management and business intelligence – is seen in an effort to clarify their variation. The attraction of business cleverness is that it includes organizations quick and powerful tools to store, get, model and evaluate huge amounts of information about their functions and, in some cases, information from exterior sources. Vendors of these applications have helped other companies and organizations boost the value of the info that resides in their databases.

Using the analysis functions of business intelligence, firms can look at many areas of their business operation and identify factors that are affecting its performance. However, the Achilles’ heel of business cleverness software is its inability to integrate non-quantitative data into its data warehouses or relational databases, its modeling and analysis applications, and its own reporting functions.