The relationship between data and information is an interconnected one. Data is raw facts such as phone numbers or addresses, and information is the organization of these raw facts into a meaningful manner. The information may be well organized on a report or table and yet not always be meaningful to all people. There are different ways to arrange data to make it meaningful for different people. For example, one person might be satisfied with information that shows him or her towns in which their customers live in to help him or her determine where the largest volume of customers are. A different person might want that information expanded to include those customers street addresses as well so that they may determine a better shipping route. Both of these examples contain the same customers and similar data but the first person would have no use for the street addresses in his search and the second person would not have enough information to create a shipping route from just the towns.

The type of data used can also affect the information generated. You can gather a great deal of data on the needs of plants to grow but if you do not put the data in the table correctly or put inaccurate data in you will get information that is basically useless. That is the garbage in/ garbage out theory. It is also a good idea when creating a table to group the like characteristics together to make it easier to gather information from the data. Computers now make it easier to input data into tables in a meaningful manner to create information that might be useful to someone.

It is now easier to manipulate data and examine it in many different ways from many different points of view quickly. It is in man’s nature to gather data and group things together according to similar data to generate information that is useful for what they are doing. An example would be similar to one show in the study guide. An employer is looking for an employee that lives in the Trenton area and speaks Spanish. The older way to find this information was to look through each record and look for the pertinent data that they are looking for. Then they went to card readers, which was faster but still time consuming. The employer would input all the employee’s cards into a reader and set it to look for those with the right criteria. The card reader would then generate information from the data that the employer needs. The computer however is much more efficient. Provided that all information was put into the computer correctly, the employer can now ask the computer to search for this data and tell it to generate a report. The time is significantly shorter than waiting for the card reader to look through all though cards.
What is done with knowledge is a key concept people at all levels in an organization need to get comfortable with. I could not find a universally accepted definition of knowledge management. For instance some would argue, “The primary focus of knowledge management actions and thoughts should be directed toward accomplishing common goals.” If this is true, then information is generated in a form that enables people to attain a level of understanding. With understanding, people are better able to make sound and timely decisions. A cynical belief is knowledge management is just a euphemistic term that makes it legal to systematically take advantage of employee ideas. Others believe, “knowledge management is luring people together [physically or virtually] to share thoughts and ideas in order to produce a level of understanding needed to make decisions.” Still others believe “knowledge management involves managing cerebral activities (learning) within oneself to get some type of action.” In these examples, knowledge and understanding could be used interchangeably. However, the real concern is not knowledge or understanding, rather, the focus is on what is done with knowledge and understanding-the “action.” Throughout this paper, I’m going to assert that “what is done with knowledge” is the focus of knowledge management. Understanding how to influence “what is done with knowledge” in an organization can profoundly enhance productivity and is essential to leading and managing organizational change, maintaining an organization’s competitive advantage, and developing an organization’s future required operating capabilities.
Knowledge is defined as understanding gained by experience whereas intelligence is the ability to learn and understand. For a long time it was believe that there was only one kind of intelligence, logical intelligence-designed to “form concepts, solve problems, obtain information, reason, and perform other intellectual operations.” This belief was used as the basis for determining whether or not people were likely to succeed. Now we know that there are more separate and distinct forms of intelligences. Logical intelligence is one, other intelligences are: visual, verbal, creative, physical, and emotional. The point being made here is that because of the limited scope we put on describing intelligence in the past, people who were considered geniuses in logical intelligence were thought to be more likely to succeed than those that possessed other categories of intelligence-this belief proved to be a fallacy.
In economic terms, knowledge as an asset follows the law of “increasing returns.” This means that with each additional unit of knowledge we use, the more it proliferates. In contrast, traditional forms of assets follow the law of “diminishing returns.” This means that for each additional unit of asset used, the less it benefits the whole. This sort of confirms that today’s measurement systems are limited in their ability to account for knowledge embedded in human resources. It is difficult for organizational leaders to understand how an intangible asset such as “knowledge” can add value to an organization-unless they know what they’re trying to do with it. Recent history has shown that “huge investments in human capital and information technology are key tools of value creation that often does not show up on a balance sheet as positive values themselves.” Therefore, it is safe to conclude that quantitative measurements of knowledge as an element of national power is not likely in the near term. As you can see the relationship between data and information is very much interconnected and without data there could be no information.