Answer

MIS6211 W1 Discussion

Adaptability: Adaptability of data or a system is the ability of data/systems to be used under different conditions. A data analyst may use data for different purposes such as security test, reliability test and/or testing of an organizational competitive advantage.

Business and organizational policies: These includes rules and policies that direct a data analyst on how to gather data, store, and use it. A data analyst can organize data requirements based on who should be involved and to what extend in data collection, storage, and usage (First San Francisco Partners, 2017).

Change control demands:

Compatibility: Compatibility of data can be defined as the extent to which data can be accessed and perhaps with the use of different devices includes mobile phones and computers. A data analyst can determine as to whether different users using different devices can comfortable access and use the data.

Completeness: This assures that all necessary data has been secured that can meet current as well as future organizational information demands. A data analyst should identify errors collected, stored, and transmitted data to ensure completeness of data.

Consistency: This is the integrity of data when accessed by different devices or converted into different formats. A data analyst must ensure that data is modelled and organized in such a way that it cannot change when accessed with different devices or converted into different formats.

Currency: This refers to the monetary value assigned to data. A data analyst should ensure that data currency is kept more accurate and precise and much as possible.

Ease of use: Can be defined as the extent to which data or a system can be used without the need of technical support. A data analyst should ensure that a data system can be used with ease by all intended users.

Evolution: Refers to the process in which data undergoes transformation and changes in a given information system or through other systems (Techopedia, 2018). Evolution of data helps data analysts to promote machine learning process.  

Extensibility:  Extensibility of data includes taking considerations of future expansions and data uses. A data analyst should ensure that future considerations are included during data modeling and organizational process.

Functional features:  Functional features of a data system determine the freedom and extent of use by different users. A data analyst must ensure that data cannot be manipulated or added by unauthorized persons.

Maintainability/manageability: Maintainability/manageability of a data system refers to additional services apart from data gathering, storage and use of data required so as to keep a data system in a perfect condition. A data analyst should ensure that maintainability and manageability costs are a kept as low as possible.

Performance: Refers to the efficiency of a data system to satisfy the intended function. A data analyst must ensure that data is modelled and organized in the best way possible that ensure system performance.

Reliability: This is the ability of data to give similar outputs regardless of the number of trials. In this case, a data analyst should ensure that data in a system are modelled and organized in such as a way that it gives same output when tests are repeated several times.

Scalability: This can be defined as the ability of a process or system to maintain acceptable performance standards such as scope or workload increases. A data analyst use scalability of data systems to ascertain on whether existing systems can still maintain necessary level of performance when the scope and/or the workload is increased (Baiju, 2014).

Security: Security can be defined as a practice of data protection against unauthorized access and/or destruction. A data analyst should analyse lapses in data protection to promote continuous improvement in data security (Baiju, 2014).

Standards: Standards of data can be defined as documented agreement of format, representation, and definition of data (Experian, 2018). A data analyst should ensure data that is gathered, stored, and/or used satisfies the stipulated standards.

Support: Supporting data is defined as written information attached to offers, agreements, financial statements, proposals, and other documents to provide depth and backup of the discussed or agreed-upon items (BD Dictionary, 2018).  A data analyst can add supporting data when signing contracts or installing new information systems.

Testability: this is the fastness as well as the reliability of carrying out tests in a given data system (InfoQ, 2014). A data analyst should ensure that data is modelled and organized in way that allows for easy running of tests.

Ubiquity: Ubiquity of data is the state of data to be accessed anywhere and everywhere. Data analyst should ensure that data changes from different sources and users are synchronized in real time (Futures Center, 2018).