Each company has a lot of data that need to be stored and transformed in knowledge to be useful. 

Data is raw. It simply exists and has no significance beyond its existence (in and by itself). It can exist in any form, usable or not. It does not have meaning of itself. For example pieces of a puzzle represents data.

Information is data that has been given meaning by way of relational connection. This “meaning” can be useful, but does not have to be. For example a piece of a puzzle that has one corner can be one of the four corner of a 2D puzzle…but we don’t know exactly what corner.

Knowledge is the appropriate collection of information, such that its intent is to be useful. Knowledge is a deterministic process. To determine what piece is left upper corner we look at the color for example and if we have a puzzle with a landscape, we know that upper we can have blue and bottom we can have green and we can deduce the position of the pieces and solve the puzzle.

Understanding is an interpolative and probabilistic process. It is cognitive and analytical. It is the process by which we can take knowledge and synthesize new knowledge from the previously held knowledge. The difference between understanding and knowledge is the difference between “learning” and “memorizing”. People who have understanding can undertake useful actions because they can synthesize new knowledge, or in some cases, at least new information, from what is previously known (and understood). That is, understanding can build upon currently held information, knowledge and understanding itself. In computer parlance, AI systems possess understanding in the sense that they are able to synthesize new knowledge from previously stored information and knowledge.

Data is stored in databases to be used in decission making processes.