Sample Statistics Dissertation Discussion Paper on Data, Information, and Knowledge

Data, Information and Knowledge

Data represents unrefined details and figures devoid of any further explanation and assessment (MacDonald, 2016). It is often static in nature and may denote a set of detached facts regarding an occurrence. Organizations at times have to choose the nature and quantity of data necessary to generate the required information. An example of data is: ‘the cost of crude oil is 100 dollars for each barrel’. Information is taken to be a collection of data (refined data) that results in the ease of making decisions. Unlike data, information must have some significance and purpose. Therefore, information could signify data that has been deduced to acquire implication for the user; for instance, ‘the cost of crude oil has increased from 100 dollars to 110 dollars for every barrel’. This offers meaning to the figures hence generating significance to any person that could be in need of understanding the trend of the prices of oil.

Knowledge is considered an aggregation of information, understanding, and opinion that could benefit a person or a group. It indicates human comprehension of an area under discussion, which has been obtained after thorough evaluation or experience. Knowledge is thus based on insight, evaluation, and suitable comprehension of the subject matter. Knowledge is obtained from information similar to the manner in which information is acquired from data (MacDonald, 2016). Knowledge may be taken as the conception of information founded on its apparent impact or significance to the issue. It may be deemed the incorporation of human perceptive practices that assists them to demonstrate consequential determinations, for example, ‘if the cost of crude oil for each barrel has increased by 10 dollars, there is a possibility that the price of diesel will go up by one dollar per liter.       

            Data becomes information the moment it gets wrapped with significance (MacDonald, 2016). Data is, therefore, required to generate information, which makes information more important when judged against data since it gains significance and sometimes a time frame. On the other hand, information becomes knowledge if it is combined with realization regarding its value. In this regard, knowledge encompasses things that are attained through understanding, study, consistency, connection, awareness, and conception. For example, the moment specific figures concerning the price of a commodity are received in an organization, they form the organization’s data, and could be stored for later reference. Such data only turns out to be information the moment they attain timeliness and relevance. Similarly, information stored in an organization becomes knowledge when extracted, evaluated, and combined with experience, valuation, and interpretation.       

Significant and accurate data analysis, statistics, is important in using the quantities of available business data as it strives to create meaning. Irrespective of whether the volume of available data is small, intricate, or massive, the most significant aspect of data analysis is the relevance of the data to the business that seeks to employ them. On this note, the most suitable analysis requires being made to the gathered data since it has to be issued in the correct form to allow the decision makers to interpret it accurately and with ease. If suitably applied, correct and meaningful data analysis may play a key role in offering solution to business challenges, maximizing performance, and generating precise forecasts regarding future occurrences. Meaningful and accurate data analysis operates as a filter with respect to obtaining significant ideas from enormous data-sets (Wagner, 2014).


MacDonald, L. (2016).The role of data in business. Chron. Retrieved from

Wagner, D. (2014). The importance of big data analytics in business. TechRadar. Retrieved from