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Information Technology

Information Technology

Adaptability: Adaptability is when the computer system can modify and transform its efficiency (Neubert et al., 2018). For example, if the data analysts have an enormous amount of data, then the system should be able to change according to the data requirements and produce valuable information for use for the intended purpose.

Business and organizational policies: These are the rules of conduct established by organizations to outline the responsibilities of the employers and the employees as well as how they handle information technology. Therefore, the analyst helps the organization to design suitable policies to enhance the security of valuable information.

Change control demands: This systematic approach involves the management of the changes made on the computer system (Neubert et al., 2018). Therefore, it ensures that the organization avoids making unnecessary changes and documenting the change process. Data analysts can use the documented changes to retrieve valuable information for a specific use.

Compatibility: Compatibility is when two systems work together without alteration. Data analysts can use compatible software applications with similar data formats to open document files for use in the organization.

Completeness: Completeness is the ability of the software application to perform the intended role (Neubert et al., 2018). Data analysts must ensure they use software that has its specification as completeness to organize extensive data into meaningful information.

Consistency: Consistency is the ability of the database to provide valid results depending on the defined rules (Handayani et al., 2017). Therefore, it helps data analysts to differentiate valid and invalid data for the specified use.

Currency: Currency is the monetary value that has been assigned data, and it helps the data analyst to determine the financial value of the data to an organization. The value of the data also helps the data analysis to prioritize the data and avail it when needed by various users.

Ease of use: The ease of use is the extent to which specific users can use a product and attain the intended objective (Handayani et al., 2017). Therefore, data analysts ensure they use software applications that enhance not only efficiency but also the effectiveness and satisfaction level of the users of information derived from the available data.

Evolution: Evolution is the radical transformation of various aspects of technology to provide more opportunities for the availability of information. Data analysts can use the different approaches of collecting information about the company and analyze them to enhance efficiency.

Extensibility: The software systems can provide an organization with the opportunity for growth. Data analysts must recommend the installation of software systems that allow the organization to store as much data as possible as it experiences growth.

Functional features: The functional features entail the purpose of the software application to not only support data collection but also enhance the stability of the organization by using the collected data (Adjallah & Wang, 2017). Data analyst checks if the installed applications will serve its intended role for the organization to avail of relevant information.

Maintainability/manageability: It is the measure taken to ensure the ease of use, competency, and the speed at which the software system functions (Adjallah & Wang, 2017). It helps data analysts to discover areas of significant changes and modify them according to the required need.

Performance: Performance entails the speed and effectiveness of a computer system. Therefore, data analysts can take advantage of high performing computers to analyze data and to produce desirable results.

Reliability: Reliability is the ability of the computer and its related components to perform consistently (Adjallah & Wang, 2017). Data analysts assess the specifications of the computer system to determine its reliability, thereby providing analyzing and providing valid information for various uses.

Scalability: Scalability is when a software or network system can accommodate increasing demand (Yang et al., 2017). Data analysts incorporate this aspect in the analysis of data for the effective management of data.

Security: Security comprises of strategies that have been implemented to prevent cyberattacks. Data analysts must help the organization to design and implement appropriate strategies to reduce incidences of loss of data, thereby ensuring the information is available any time.

Standards: Standards are specifications for software and hardware to enhance compatibility. Therefore, data analyst uses compatible products for data collection and analysis to provide valid and reliable information.

Support: Support is the professional assistance received by the organizations on technology products (Yang et al., 2017). Data analysts require the help of software and application developers to use appropriate data collection techniques for a positive outcome.

Testability: It is the extent to which the software system supports the testing of an anticipated outcome. It helps data analysts to determine if they can easily find errors in analyzed data.

Ubiquity: Ubiquity is the presence of software engineering at any time using any device in any format (Yang et al., 2017). Data analysts can take advantage of and efficiently analyze data to produce reliable information for various users.


Adjallah, K. H., & Wang, Z. (2017). Optimizing the Reliability, Maintainability, and Safety Data Collection Process Through Lifecycle. In 2017 Annual Reliability and Maintainability Symposium (RAMS) (pp. 1-6). IEEE.

Handayani, P. W., Hidayanto, A. N., Pinem, A. A., Hapsari, I. C., Sandhyaduhita, P. I., & Budi, I. (2017). Acceptance Model of a Hospital Information System. International Journal of Medical Informatics99, 11-28.

Neubert, G., Ouzrout, Y., & Bouras, A. (2018). Collaboration and Integration Through Information Technologies in Supply Chains. arXiv preprint arXiv:1811.01688.

Yang, A., Troup, M., & Ho, J. W. (2017). Scalability and Validation of Big Data Bioinformatics Software. Computational and Structural Biotechnology Journal15, 379-386.

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