Medical data is any health-related information associated with frequent or infrequent patient care or as a part of a program for the clinical trials. It entails electronic health records that comprise a digitalized version of a patient’s medical information. Data collected or stored in any system is often not equal. It can be analyzed and categorized based on its format, size, and the purpose that it serves. Medical data is usually collected and stored in dispensaries, clinics, hospitals, and research centers. Public health authorities also find great use in these data. Data is classified into three broad categories; structured, semi-structured, and unstructured data.
A data is said to be structured when it adheres to the requirements of a pre-defined data model, making it more straightforward and easier to analyze. Most of these conform to a tabular format, and the relationship between each set of measured parameters is presented in rows and columns. The commonest examples of structured data are the ones in the SQL databases and the Excel files (Liu, et al., 2019). These are often organized into rows and columns that can be sorted in an easily accessible model. Each parameter is separately accessible, and this makes this type of data compelling. It is considered as the most traditional form of data storage system since the ancient systems were able to store and access their data in a structured format.
Unstructured data is one that lacks a predefined model of data, or its mode of organization is pre-defined. This is regarded as a text-heavy type of data, usually containing numbers, dates, and other pieces of factual information (Abadi, Bajda-Pawlikowski, Abouzied & Silberschatz, 2016). When viewed, it may result in ambiguities or irregularities, making it difficult to understand, mainly when traditional programs are employed, compared to when structured databases are used. It includes video files, audios, or any other No-SQL database. The ability to store this form of data has grown over the past years with the discovery and development of new technology (Wang & Gu, 2018). The ability to analyze stored unstructured data is viewed to be relevant in the context of Big Data. A larger part of data in most organizations is unstructured. The ability to extract content and value from unstructured data serves as one of the major drivers that have propelled the growth of Big Data systems.
Some types of structured data do not conform to the form of structured data models that are associated with relational databases that are seen in structured data. These are known as semi-structured data. Although these lack the data tables, they contain tags and other important markers that can be used in separating semantic elements that can be used in enforcing hierarchies of records and fields in this data. This property makes most people describe it as a self-describing data structure (Abadi et al., 2016). Examples of semi-structured data include; XML. This category of data exists because it is easier to analyze compared to the unstructured data. It has been used to compress bigger data systems that can be read in a structured system, which is easier, compared to the unstructured data. This system, together with the structured data, is the most commonly used.
A spreadsheet is one of the common and widely used data systems. Its widespread use is based on its availability, accessibility, and ease in its use. Despite their use in data systems, they do not support basic functionality, including some specific data implementation commands. These include; ability to support self-documentation through the metadata system. They also cannot enforce specific data types or domains to ensure the consistency of data within a column. Data in a spreadsheet cannot define the relationship among different tables. Besides, it cannot guarantee the consistency of data systems across different related tables (Wang & Gu, 2018). These deficient functions make spreadsheets the most basic forms of data systems that are used accessed by any user.
A complete and functional database system comprises many components to make it fully functional. These components must define and regulate the process of data collection, storage, management, and use within the database environment (Eberlein & Driesen, 2018). Generally, the major components of a database include; software, hardware, people, procedures, and data. The hardware comprises of the networking components, the computer, and input or output devices. The software component, on the other hand, entails; application programs, operating systems, data management systems, and support utility programs.
People as the component of a database system comprise all the task force that is responsible for making the data system running and functional. These include the database administrator, analysts, system administrator, database designer, and the end-users. Each of these entities functions under different capacities to ensure that the system functions well. The data itself comprises the business rule, controls, and database (Eberlein & Driesen, 2018). Each of these components attains the complete functionality of a database system. A comprehensive database provides the functional and operational needs, together with the future planning of any given organization.
When calculating and determining the potential cost of a database system, it is essential to consider the components of a database and the financial demands of each separately. Therefore, when calculating the cost, one needs to consider the sophisticated software and hardware systems, sufficiently and competent personnel, licensure of the database system, and the associated regulatory compliance costs. The cost of each thus depends on the cost offered by the respective sellers or vendors and other service providers. It is, therefore, impossible to estimate the cost breakdown since these vary depending on different dynamics such as the quality intended to achieve and the components of the database system.
References
Abadi, D., Bajda-Pawlikowski, K., Abouzied, A., & Silberschatz, A. (2016). U.S. Patent No. 9,495,427. Washington, DC: U.S. Patent and Trademark Office.
Eberlein, P., & Driesen, V. (2018). U.S. Patent No. 10,120,886. Washington, DC: U.S. Patent and Trademark Office.
Liu, Z. H., Hammerschmidt, B. C., Krishnamurthy, S., Lu, Y., McMahon, D. J., Tata, S., & Arora, V. (2019). U.S. Patent Application No. 15/848,737.
Wang, J., & Gu, L. (2018). Teach MBA Data Science Using R. Issues in Information Systems, 19(2).
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