Describe the knowledge management systems.

Businesses do not run on data but they run on information and their knowledge on how to put that information to use successfully. The transformation of data into knowledge is accomplished through a process that starts with data collection from various sources. This data is stored in a database where it can be preprocessed and stored in a data warehouse. To discover knowledge the processed data may go through a transformation that makes them ready for analysis. The analysis is done with data mining tools which look for patterns and intelligent systems which support data interpretation. The results of all these activities are generated knowledge. Such knowledge can be presented using different tools of presentation and can either be stored in a knowledge base or presented to the user. This process of converting data to knowledge is known as data life cycle.

In an organization data can be internal i.e. it can be stored in the transaction databases or personal data or external environment which can be commercial database or even satellite. This data in whichever source has to be collected through methods such as observations, surveys, time studies, contributions from experts and so on. Regardless of how they are collected, they must be validated in order to ensure that the information and knowledge that is obtained from them will be relevant and dependable. The validation will be aimed at removing problems in data such as errors, delays, improper data and improper organization or unavailability. If wrong data is collected then the information or knowledge to be created will be faulty hence data control must be put else the result will be GI GO (garbage in/garbage out) situation.

Data that has been preprocessed and stored in a data warehouse can be accessible for analysis and representation. A data warehouse is a single depository place for keeping all types of databases. It enables data to be accessed quickly as they are located in one place and the users of data can access such data easily and frequently. Data warehouses are organized to allow for the storage of metadata.

Metadata also known as a data mart is a replicated subset of the data warehouse and it is dedicated to a functional or regional area, for example, a company may keep data marts for different functions such as human resources, marketing, engineering and so on. Such data marts and data warehouses support analytical processing which is done in order to discover trends in data which is the basis of trusting and knowledge creation.

The process of extracting useful knowledge from volumes of data is known as knowledge discovery in databases or just knowledge discovery

This process starts with identifying which data to consider in the data ware then processing this data to be ready for analysis. The objective is to identify valid, novel potentially useful and ultimately understandable patterns in data. In order to get the patterns, the knowledge discovery process can use any of the following three:
- Massive data collection
- Powerful multiprocessor computers
- The data mining algorithms

Data mining is searching for valuable business information in large databases. It can follow techniques such as case based reasoning where historic cases can be used to recognize patterns.
It can also follow neural computing. This is a machine learning approach by which historical data can be examined for pattern co-ordination or it can follow intelligent agents which in modern times uses internet to discover the right information in the internet or from the internet based databases or it can use association analysis which is in most cases expressions of statistical rules among items. In massive data collection knowledge discovery provides with huge volumes of data from where it can be believed that from those large volumes of data from where it can be believed that from those large volumes, knowledge can be discovered using any of the techniques that can be available to the user.

By use of powerful computers, knowledge based systems can be applied to look for trends in data which can be then applied to various uses that the user is interested. Once knowledge has been discovered, it has to be presented. If presentation has to be effective, then visualization technologies have to be used to communicate such knowledge to the users. Technologies such as digital images, geographical information systems, graphical user interfaces, multidimensional tables and graphs, virtual reality and animation make the knowledge presentation more attractive and understandable to users. When this presentation is done among the employees of an organization, it is said that an organization learns. This is critical because it enables an organization to survive and to sustain competitive advantage over its competitors.

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Kavungya answered the question on May 10, 2019 at 11:17