Introduction

1. Discuss the relationship between data, information, and knowledge. Support your discussion with at least three academically reviewed articles.
Scholars have broadly defined data. According to Echeverria et al. (2019), data is referred to as the recorded (stored and captured) signals and symbols readings.
• The symbols consist of words, i.e. verbal or text, diagrams, numbers and images or videos that are considered the building cells of the communication process.
• The signals consist of the sensory or the sensor readings of various things like touch, taste, smell and sound.

The main aim of the data is to store and record the situations or activities and attempt to record and capture the actual event and picture (Echeverria et al., 2019). Hence it can be concluded that data are historical and is used for illustrations and forecasting.

According to Schloss et al. (2018), information is the message that depicts a meaning or provides input for decision-making or for taking relevant action. Information can be collected from the historical background (reconstructed picture or processed data) or current background (communication). Hence, the main aim of information is to provide support in the process of decision making or to solve the challenges or the issues or help the business grab opportunities.

According to Kedzie and McKeown (2020), knowledge is referred to as the (1) recognition or cognition, (2) “capacity to act”, and (3) to understand that is being contained or resides in the mind or the brain. The main aim of knowledge is to support one with the best lives. In the business context, the main aim of knowledge is the creation or the increment of the value of the organization and all the stakeholders. So, it can be said that the overall aim of knowledge is value creation.

The relationship between information, data, and knowledge is integral. Data consists of unrefined, basic and unfiltered information. Data are considered the elements of the analysis. Information of factual nature is being used as the basis for the discussion in the context of analyzing data. The information is produced by a sensing organ or device that consists of irrelevant or valuable information and helps process the data that contains meaning. Information that are in the form of numerical can be transmitted digitally in the context of data interpretation. Data need to be organized to become information.

Information is referred to as refined data that is being evolved from the point that is useful for the analysis form. Information is considered as data with relevant context. Moreover, information is the reception or the communication of the knowledge or intelligence. The knowledge that is being obtained through the study of investigation or instruction is information. It consists of data and facts. There is always a quantitative measure for information. Information needs to be put in the form of context to become knowledge.

Knowledge generally resides within the mind of the user and enhances when insight and experiences are being applied with the information and the data. Knowledge is referred to as the information that contains meaning. Knowledge is the condition or the fact of recognizing something through the process of association or experience. It is the capacity of individual understanding of information. The all total of what is being known to us, the apex body of the truth, all the relevant information, and humanity’s principles, all constitute knowledge. Knowledge is generated by the flow of information, anchored by the holder’s commitment and beliefs.
Hence, it can be said that the relationship between information, data, and knowledge is integral. Data consists of unrefined, basic and unfiltered information that lead to knowledge.

2. Why do organizations have information deficiency problems? Suggest ways on how to overcome the information deficiency problem.
Information management refers to the use of IT tools and the methods used for processing, collecting, storing, and securing data from sources that tend to become inconsistent or fragmented. A contemporary business must handle a wide range of data, including unstructured and semi-structured content such as sound and video, in addition to structured data such as words and numbers (Alsafyani, 2020). Social media information, texts, photographs, videos, music, papers, address books, events, etc are included in the digital library. Information Management is responsible for maintaining, growing, and transferring an organization’s digital library contents across several platforms. Information Management, in particular, is concerned with how data is structured, stored, and secured, as well as the speed and simplicity with which it is gathered, analyzed, and reported.

In today’s world, one of the most valuable resources in an organization is information, or more specifically, knowledge. Organizational capabilities are built on specialized skills in integrating and sharing knowledge and information. However, the majority of knowledge dispersion initiatives and systems often fail. Even when there are no technological or IT issues, the primary purpose of sharing is not being achieved, and even the achievement of relative success is difficult. The distinguishing trait of this new millennium, often known as the Information Age, is progress and rapid development (Rubio-Aliagaet al., 2017). At all levels, organizations of the modern era are confronted with the main problem of operational stability. This new era may be compared as a phase for the businesses that harness the incredible capabilities of information and apply them to achieve a growing competitive advantage. Companies nowadays are confronted with such vast amounts of data, and the satisfaction of knowing that they can control and use it efficiently has become one of their primary concerns.

Changes in information and technology that businesses want and various management teams, shifting objectives, and increases or decreases in IT spending as they compete with other demands on an organization’s budget have all played a role throughout many decades (Essel et al. 2017). There are many other reasons like data silos (the information that is being trapped in the databases of the departments), loss of data or bypassing of data during the transit, all determine the efficacy of information management. Moreover, poorly structured user interfaces require effort from the users, formats that follow non-standardized information, and the changes that are fast-moving in the format of the information that is desired and being particularly of unstructured content, all require expensive investments.

The ways to overcome the information deficiency problem are as follows:
1. Establishment of a System of Distribution – Administrators should not spend an excessive amount of time acquiring knowledge when business is expanding (Börekçi, 2019). Management should hire junior staff to identify, gather, and communicate a lot of what the organization is looking for in terms of information. Businesses can then utilize this as information scouts and a type of clipping service to pre-read.
2. Administrators should be thoughtful at the time of sending information. The lack of organizational rules is often to blame for the overwhelming volume of data. Such rules might alternatively protect businesses from unnecessarily and excessively exposing the processes to knowledge that is incompatible with existing organizational knowledge base.
3. Constantly updating and reviewing – Periodically reviewing the documents is an integral part of efficient knowledge management. All the relevant information requires effective collection and organization and then proper utilization of the gathered information in a form that is useful for the whole organization. Another crucial element is having the correct knowledge and successfully sharing it appropriately (Khlystova et al., 2020). Organizations must keep up with changes in information and data to maintain their systems up to date with critical information at all times. Second, they should take advantage of contemporary data storage technologies.

References

Alsafyani, M. A. (2020). Computer-based solutions to support those with Colour Vision Deficiency to access day-to-day information. Retrieved July 11, 2021, from https://147.197.131.104/bitstream/handle/2299/23514/08196001%20ALSAFYANI%20Majed%20Final%20Version%20of%20PhD%20Submission.pdf?sequence=1&isAllowed=y
Al Shobaki, M. J., Abu-Naser, S. S., El Talla, S. A., & FarajAllah, A. (2018). The Intermediate Role of Knowledge and Information Management in the Relationship between Adopting the Strategy Criterion and Improving Overall Performance. International Journal of Academic Management Science Research, 2(12), 16-30.
Börekçi, N. Ö. (2019). Current information on vitamin D deficiency. The Journal of Turkish Family Physician, 10(1), 35-42.
Cunningham, C. D., & Egbert, J. (2019). Using Empirical Data to Investigate the Original Meaning of Emolument in the Constitution. Ga. St. UL Rev., 36, 465.
Echeverria, V., Martinez-Maldonado, R., & Buckingham Shum, S. (2019, May). Towards collaboration translucence: Giving meaning to multimodal group data. In Proceedings of the 2019 chi conference on human factors in computing systems (pp. 1-16).
Essel, H. B., Tachie-Menson, A., & Ahiaklo-Kuz, N. A. (2017). 21st Century Skill Set Deficiency in Ghanaian Basic Education: A Review of Basic Design and Technology, and Information and Communications Technology Syllabi. Indian Journal of Applied Research, 8(3), 248-250.
Kedzie, C., & McKeown, K. (2020, November). Controllable meaning representation to text generation: Linearization and data augmentation strategies. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 5160-5185).
Khlystova, E.V., Trufanova, G.K., & Ilinykh, N.E. (2020). Analysis of Conditions for Effective Use of Information and Computer Technologies When Organizing the Educational Environment of Students of Primary School Age with Neurodynamic Deficiency. Advances in Economics, Business and Management Research, 138, 875-879.
Kristoffersen, E., Aremu, O. O., Blomsma, F., Mikalef, P., & Li, J. (2019, September). Exploring the relationship between data science and circular economy: An enhanced CRISP-DM Process Model. In Conference on e-Business, e-Services and e-Society (pp. 177-189). Springer, Cham.
Rubio-Aliaga, Á., García-Cascales, M. S., Molina-García, Á., & Sánchez-Lozano, J. M. (2017). Geographic information system for optimization and integration of photovoltaic solar energy in agricultural areas with energy deficiency and water scarcity. In Project Management and Engineering Research (pp. 181-197). Springer, Cham.
Schloss, K.B., Gramazio, C.C., Silverman, A.T., Parker, M.L. and Wang, A.S., (2018). Mapping colour to meaning in colourmap data visualizations. IEEE transactions on visualization and computer graphics, 25(1), 810-819.
Saravanan, P. M. P. (2019). A Study on the Relationship between Social Intelligence and Environmental Hygiene and Values. A Study on Relationship between Social Intelligence and Environmental Hygiene and Values, 8(10), 1105-1109.