Data, information, and knowledge - only one of these comes from a database.
Think about a simple data-driven scenario with five independent elements, each having five attributes, and a one-to-one relationship with the others. One might create a simple spreadsheet with five rows and five columns with the independent elements on the vertical axis and the attributes on the horizontal axis. When the number of data elements is low, spreadsheets are adequate. However, with increased processing power and cheap data storage, the database offered analysts powerful ways of storing ever increasing amounts of data in dynamic data architectures, as well building workflow around the data for a variety of purposes - the most important of which has been the transformation of data to information which could be used to make real-world decisions. However, information derived from data is neither dynamic nor automatically applied. In order for data-driven information to be useful to human beings, knowledge is required in order to apply the information to decision trees, problem solving or scenario planning.
Most electronic systems generate data of some form. Even the lowly web server produces a multitude of data elements which the webmaster may leverage to analyze web traffic in order to optimize the website. Data remains data until it is organized, classified, cleansed, normalized and structured in meaningful ways. It is in the analysis of the data that conclusions may be drawn. At that point, the data may be said to have generated information: "The data leads me to conclude that as x increases, y decreases," for example. However, the conclusions may be valid or invalid since they have not been tested in real world situations. Once ...
This solution describes the difference between data, information and knowledge; as well as summarizing and comparing various types of database management systems.