MIS D2

Discussion 2

by Sheba Reddy Kota - Friday, May 8, 2020, 7:45 AM

 

When some unrefined, basic and unfiltered information gathered together, they are called as data. The data which are so much refined and is very much useful and helpful for some important analysis they are called information. When in certain data and information, human intuitions and experiences are added, they are called as knowledge (Li, Landström, Fast-Berglund, & Almström, 2019). Knowledge is specifically defined by how a human being is applying its sense of experience to imply the data and information he or she gathered. The above mentioned statements are the basic differences among data, information and knowledge (Adesina, Oyero, Adeyeye, & Yartey, 2020). But one thing is very clear from the above statements that each and every component related to data, information and knowledge has been connected to each other.

Data is the basic and preliminary idea of accumulating documents, if those documents are being associated more filtering, accuracy then they are taken as an information and finally if all the information anyone has gathered, applying them for some analysis purpose with the touch of their experience are called as knowledge, so in this way we can understand that how data, information and knowledge are interlinked with one another (Houck & Gamette, 2019). If data is not generated, then it is not possible to get any kind of information about some fact and if there is no proper information about any fact then it is also not possible to analyse any fact with the only help of human experience (Abu‐Musa, 2010). That is why data generation, converting them into an information and finally analysing the fact on the basis of those information, all of these are accumulated in a single process which can be termed as idea generation.

If data is not filtered properly then that unfiltered data is converted into an incomplete information which leads to improper analysing of any fact, and the problem generated for such purposes is termed as data information deficiency. There are several ways to get rid of this problems and they are as described (Adesina, Oyero, Adeyeye, & Yartey, 2020). If anyone is seeking any information, he or she contemplate that information in advance.Managers should identify any kind of information carries which are very vital.The intake capacity should be streamlined properly which is recognized as the issues of information overload. Everyone should be aware about information crutches (Houck & Gamette, 2019).A distribution system should be established for ensuring the prevention of information overload. Moreover, being thoughtful while sending data and streamlining the intake capacity are considered as the approaches for overcoming the information overload. 

 

References

Abu‐Musa, A. (2010). "Information security governance in Saudi organizations: an empirical study". Information Management & Computer Security, 4(18), 226-276.

Adesina, E., Oyero, O., Adeyeye, B., & Yartey, D. (2020). Data on information sources, knowledge and practice on hepatitis B virus in southwest Nigeria. Data in brief, 105507.

Houck, M., & Gamette, M. (2019). Data, Information, Knowledge, and Wisdom. A Partnership to Improve the Management of Forensic Service Organizations, 14.

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(1), 380-386.