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The concepts of data, information, and knowledge are widely interdependent. The well-known phrase “knowledge is power” significantly acknowledges the potential value which gives rise to insightful information which in the end becomes knowledge itself(Jake Geozilla, 2019). A set of data on the other hand gives rise to information. Without the existence of context or setting, data can mean absolutely nothing. For example, a random arrangement of numbers like 04071776may be insignificant. But given context, it could mean a date, month, and year. I say, for instance, this is 4th July 1776, it now makes sense. The United States day of independence. This, therefore, means that a raw, and random sequence of numbers has been transformed into meaningful information. The information derived from raw data can be assigned specific variables to come up with knowledge. This can be achieved by asking one important question of “how”. How the numbers are connected to mean something, and how the information got can be applied to suit or achieve a specific objective.
Data and information can be harnessed to find solutions to real-life issues. The impact a lot of knowledge into the process of decision-making. The value extracted from data and information can go a long way to benefiting quite a several industries word over. This can range from education, financial services, software developments, and space programs. The interrelation of the two concepts is not something that should be ignored. Their insight drives intelligent decisions and successful results which can lead to business success. On information and knowledge, once raw data has undergone processing to give birth to information, the same information will further undergo manipulation to become knowledge(Efraim Turban, 2018). The knowledge, therefore, enables one to acquire experience, and familiarise themselves with given areas and subjects. The availability of this knowledge enables people to make sound judgments and properly informed decisions about particular subjects.
No 2: Discussion of Concepts
The article presents three main concepts, which are data, information, and knowledge. The three concepts are interdependent in a way that the insight acquired in one will subsequently be used in the next concept. This paper, therefore, seeks to look into the definitions of the concepts, examples, and how they can be beneficial to IT professionals.
Data refers to an assortment of figures, numbers, and facts presented in unorganized or raw form. Data in itself is insignificant if no context is attached to it. It can just be a random set of numbers with no meaning. Consider this common example, 04071776.This is a number sequence that means nothing at first sight. If this number is looked at from the perspective of a day, month, and year, it will make a huge sense. That is 04th July 1776. This number is meaningful because it now gives information.
Information is clean and processed data. This data is no longer in its raw form, which means it can easily be measured, visualized, and analyzed to serve a specific purpose. Different operations are involved in the processing of data. Such operations include aggregation and validation. The utilization of the 5Ws and 1H can extract useful information from a set of data thus making it of more paramount importance.When we ask ourselves ‘how’, the information becomes knowledge.
The relevance of the information extracted from gathered data to our objectives, and goals is what constitutes knowledge. The understanding of how to apply the information acquired to the pursuit of achieving goals becomes knowledge itself. This knowledge is thus employed in making informed decisions in our daily lives.
In conclusion, IT professionalsshould apply the data, information, and knowledge to find solutions to daily life issues. The knowledge also aids in the decision-making process. Some of the industries that IT professionals can sensitize with these concepts are software development, space exploration, and education.
Shannon Kempe, Michael Bracket (2013, November 14). The Data-Information-Knowledge Cycle. Dataversity, PP.3-6.
Jake Geozilla , D. e. (2019, September 27). Information vs Data vs Knowledge. datarob.com, pp. 2-8.
Efraim Turban, C. P. (2018). Information Technology for Management: On-Demand Strategies for Performance, Growth, and Sustainability (11 ed.). Wiley, 2018.
The paper written by (Abulela & Harwell, 2020) focuses on data analysis when conducting significant studies within a company. The paper highlights components for basic researchers when and introduces four important methodologies and they are proper handling of ethical data, measurement of dependent variables, constructed measures for missing data and proper models for checking to see If there’s any data missing. These models can be used by companies which depend on working on confidential data and also data in larger quantities, as this research focuses on using models to protect the data,
The paper written by (Cafiso & di Graziano & Pappalardo, 2019) focuses on how a project can be designed to create an efficient system which can be applied towards innovation of different technologies which are used for information gathering process. This paper talks about different technologies for extraction of data, collection, integration and also publication of data. Depending on the source and format of the data used, this paper also talks about different application for different formats of data. As nowadays companies use different format of different types of application, this research paper can be really used for various application as it talks about different models and methodologies which can be used by companies as they work on confidential data and also data in larger quantities, as this research focuses on using models to protect the data.
The paper written by (Duan & Sun & Che & Cao & Li & Yang, 2019) focuses on how the growth of internet on many devices lead to emergence in data resources and also the data protection for the all the devices. The paper also talks about different applications on how these data can be protected within these many devices and these methods can be borrowed and applied in many companies.
Abulela, M. A. A., & Harwell, M. M. (2020). Data Analysis: Strengthening Inferences in Quantitative Education Studies Conducted by Novice Researchers. Educational Sciences: Theory and Practice, 20(1), 59–78.
Cafiso, S., di Graziano, A., & Pappalardo, G. (2019). A Collaborative System to Manage Information Sources Improving Transport Infrastructure Data Knowledge. Journal of Engineering & Technological Sciences, 51(6), 855–868. https://doi.org/10.5614/j.eng.technol.sci.2019.51….
Duan, Y., Sun, X., Che, H., Cao, C., Li, Z., & Yang, X. (2019). Modeling Data, Information and Knowledge for Security Protection of Hybrid IoT and Edge Resources. IEEE Access, Access, IEEE, 7, 99161–99176. https://doi.org/10.1109/ACCESS.2019.2931365