Read and respond to two of classmates’ posts

Read and respond to at least two (2) of your classmates’ posts 150 words minimum. In your response to your classmates, consider comparing your articles to those of your classmates. Research from academic articles must be included in your responses to classmates’ posts. Below are additional suggestions on how to respond to your classmates’ discussions:

· Ask a probing question, substantiated with additional background information, evidence or research.

· Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives.

· Offer and support an alternative perspective using readings from the classroom or from your own research.

· Validate an idea with your own experience and additional research.

· Make a suggestion based on additional evidence drawn from readings or after synthesizing multiple postings.

· Expand on your colleagues’ postings by providing additional insights or contrasting perspectives based on readings and evidence.

by Indra Kumar Siripurapu

Relationship between data, information and knowledge

Data is the unrefined version, whereas information is considered to be refined and organized and knowledge is considered to be the value of the information. The development in IT industry has lead to huge streaming data which is analyzed and used for the advantage of the organization. This increases the quantity of data, knowledge and the information, but the utilization of all these to full extent has been lacking. The relationship between information, data and knowledge is still not evident till date. There has been several models which depict the relationship between information, data and knowledge. The Value-Chain model which shows the formation of knowledge from information which is processed from data. Whereas the materialization model is a reversed model compared to the value chain model which shows the derivation of data from information and knowledge. Another model called interactive model exists where information is derived from both data and knowledge. The value-chain model is most recognized compared to the rest. Overall, there has been several depictions regarding the relationship between data, information and knowledge but there has been controversies regarding the conclusion of the relationship.

Reasons for Information Deficiency

One of the main reasons for Information deficiency in an organization is the due to the lack of strong data storage methods and the fluctuating information. As the technology is changing, the information obtained is also not constant and unpredictable. The traditional data storage methods have been designed has certain limitations and there may be issues with the data format. Another reason of Information deficiency can be loss of information due to any technical difficulties. Loss of small amount of information can change the graph of the organization. Another reason would be lack of understanding of the use of the information. Without having the knowledge of the information , the information cannot be used properly for the organization benefit.

Ways to overcome information deficiency

One of the ways to overcome Information deficiency is to maintain a good data storage. Upgrading the data storage with additional features would help in prevention of loss of data or the limitations on storage of information can be avoided. A Good management of the information is required in order to avoid information deficiency.

References

(Kettinger & Li, 2010). The infological equation extended: towards conceptual clarity in the relationship between data, information and knowledge, https://www.proquest.com/docview/742649696/fulltext/97D6146717E148AAPQ/1?accountid=9864

(Dan Li, 2019). Human-Centred Dissemination of Data, Information and Knowledge in Industry 4.0, https://www.sciencedirect.com/science/article/pii/S2212827119309084#keys0001

Pejova, Z. (1996). Information Management Problems and Challenges in Transition Economies. Retrieved from, https://asistdl.onlinelibrary.wiley.com/doi/full/10.1002/bult.21

by Xiaocang Li

Before we know the relationship between data, information and knowledge, firstly, we need to understand their definition. Date could a the unanalyzed, raw, unorganized, unrelated materials is used to derive information after analyzation. Basically, data is facts that we observe, including symbols, images, numbers and characters. These original data is collected and ready for analysis to establish the information. Data left alone isn’t informative and could be relatively meaningless. However, it gains a purpose to analyze and form information. After receiving data, we can process, analyze, and organize those data to a different set. The data is becoming useful for people after these procedures above. Once data is processed and gains relevance, it will become information that is certain, useful and reliable (Santos, Piechnicki, Loures, & Santos, 2017). We could say that information is a kind of prepared data that has been aggregated, organized and processed into a more human-friendly format that provides more context. Information is often delivered in the form of data reports and dashboards. Knowledge means the awareness and familiarity of a person, idea, events, issues, ways of doing things, which is received by perceiving, learning, or discovering. A combination of information and experience leads to knowledge that has the potential to draw inferences and develop insights.

Currently, the business world has been changing fast. The information system needs to be updated in time to adjust the innovation and rapid change of situation. Organizations of this age are faced with the major challenge of dynamic stability at all different levels. It could be said that this new age is like a stage for organizations that have been able to take control of the amazing capabilities of knowledge and use them to gain progressive, competitive advantage (Paghaleh, Shafiezadeh, & Mohammadi, 2011). Now, most of the companies meet different dimensions of dynamism and uncertainty. Therefore, the organization needs a more efficient system to analyze the data and information. For example, managers need to understand a company’s culture better and user’s specific requirements. Otherwise, it needs to improve communication between different departments.

Reference

Li, D., Landström, A., Fast-Berglund, Å., & Almström, P. (2019). Human-Centred Dissemination of Data, Information and Knowledge in Industry 4.0. Procedia CIRP, 84, 380–386. https://doi.org/10.1016/j.procir.2019.04.261

Dos Santos, C. F., Piechnicki, F., de Freitas Rocha Loures, E., & Santos, E. A. P. (2017). Mapping the Conceptual Relationship among Data Analysis, Knowledge Generation and Decision-making in Industrial Processes. Procedia Manufacturing, 11, 1751–1758. https://doi.org/10.1016/j.promfg.2017.07.305

Paghaleh, M. J., Shafiezadeh, E., & Mohammadi, M. (2011). Information technology and its deficiencies in sharing organizational knowledge. International journal of business and social science, 2(8), 192-198.

Directions for Response to Discussion post
Students will respond to the Discussion Question in full APA format
•Significant posts are at least 150 words and require some information from the text, academically reviewed papers, some significant commentary that requires knowledge of the subject, a web link to an article or other source in order to be accepted.
If your posts is less than the minimum word count, points will be deducted from your grade.
•Provide appropriate APA in-text citations and references.

Coping and pasting an article or copying the information and changing a word or two is not a significant post. If you do this, you will receive a 0 for the assignment—no questions asked.
Absolutely no Wikipedia, Blogs, Internet Articles, Homework Help, Books or other Chron/Investopedia etc. Journal Articles sourced from the Campbellsville University Library should be your research source.

For APA formatting use APA Manual or www.purdueowl.com

Data/Information/Knowledge

1.  Discuss the relationship between data, information, and knowledge. Support your discussion with at least 3 academically reviewed articles.

2.  Why do organization have information deficiency problem? Suggest ways organizations can overcome information deficiency problems.

Even though there are various definitions about data, information, and knowledge, there is still no clear picture of the differentiation between these definitions. Few researches show that information is a flow of data while others define information as a message or form of communication (Cooper, 2017). On the other hand, data is used in maintaining records of activities. Thus, information is the source which consists of the past and present data. Furthermore, knowledge is defined as the aspect that exists in our brains which helps in decision making (Fred, Y. Y., 2017). Thus, information is said to be produced after data is processed. Information thereafter becomes knowledge after being absorbed by a person. Also, it is observed that, during the documentation of knowledge, it becomes information. On the other hand, when information is captured, it becomes data (Tang, V., Yanine, F., & Valenzuela, L., 2016). Therefore, the highest aspect in the hierarchy is knowledge, followed by information, then data.

For an organization to run successfully, it has to integrate information systems effectively. Therefore, having the correct information and using it in the right manner is an important aspect for the success of an organization. Today, organizations are seen to still suffer from information deficiency. This is attributed to the poor storage methods and unpredictable nature of information which are crucial for the future. It is usually difficult to single out which information is important to the organization and which one is not. This leads to information deficiency. In order to solve this issue, the organization should consider integrating information changes and data. This is important in updating their systems with the necessary information which can be used (Stefanović, 2016). Also, companies are encouraged to use of modern data storage methods.

References

Cooper, P. (2017). Data, information, knowledge and wisdom. Anaesthesia & Intensive Care Medicine, 18(1), 55-56. doi:10.1016/j.mpaic.2016.10.006

Fred, Y. Y. (2017). Measuring knowledge: A quantitative approach to knowledge theory. In. Scientific Metrics: Towards Analytical and Quantitative Sciences (pp. 155-162). Springer, Singapore.

Stefanović. (2016). A comparison of the Analytic Hierarchy Process and the Analysis and Synthesis of Parameters under Information Deficiency method for assessing the sustainability of waste management scenarios. Journal of cleaner production, 130, 155-165.

Tang, V., Yanine, F., & Valenzuela, L. (2016). Data, information, knowledge and intelligence. International Journal of Innovation Science.