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The DIKW Model for Knowledge Management and Data Value Extraction

Computer Science

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The DIKW Model for Knowledge Management and Data Value Extraction. 

Data comes in many different types. It can be quantitative, qualitative, structured, unstructured, semi-structured, or a mix of all of these. Data in its raw form alone is not very helpful in decision making or providing useful information. However, to apply structure to the data also requires a degree of bias when the choice of which data are most appropriate is made. 

Using the readings for this week in conjunction with the article presented in this discussion, consider that a healthcare clinic will collect demographic information about patients, as well as information about each patient’s visit. Quantitative and structured data will be entered in the record system about a patient’s vitals and medical history, but the physician will also enter qualitative and unstructured information as they describe a patient’s symptoms or what they are experiencing. What types of questions might be asked that could be applied to the data to inform the organization about the patients? What might the organization do with the data to inform future decisions such as purchasing supplies, medications, equipment, personnel, etc., in order to plan for possible future patients based on the data? What challenges might exist in working with unstructured data?