Assignment 1

| (TCO A) The quality of information that gives assurance that is reasonably free of error and bias and is complete is

Identifies key facts in a range of data. Notices when data appear wrong or incomplete, or need verification. Distinguishes information that is not pertinent to a decision or

Verified accuracy and integrity of clinical data by performing validation checks written in SAS and data cleaning by examining data related errors and missing values.

Data completeness is when the business should ensure that their client’s information is altogether filled in so it is finished and they will likewise need to ensure that their client's information is right generally their clients may get charged for something that they didn't commit, in light of the fact that they have the wrong information for that individual

The audience would be the data owners, data managers and IT personnel who would be responsible for the data quality (data administrator, operations manager or database admin).

The choice of a particular source is related to the type of data or information needed; such factors as ease of access , ease of processing the source, cost, availability, quantity and quality of information will possibly impact on the selection (Wanderley

This webinar mainly deals with how to analyze, quantify and monitor data quality conditions and how to create dashboard, communicate results and what content do we provide to the dashboard and its functions like business rules metadata, rules library, decision points, repeat analysis and how to connect dashboard to the data.

Dirty Data: - Inaccurate, Inconsistent, incomplete and duplicate data comes in the category of dirty data. From the logic of Garbage in Garbage out, Dirty data as input will produce Dirty Data as output. Organizations usually realize that very late in the product life cycle that the data is dirty. Usually the reports generated by dirty data will amplify the errors further when the data is used and these errors will be impossible to debug. Organizations might lose a lot of revenue because of this problem. When this data is used by other systems, it impacts their data also. The impact of dirty data is in all departments of an enterprise from marketing to finance, human resources, customer relationships etc.

However, Labs and inspectors tend to be blamed because of the different levels of data quality, but the reality is that there are no clear definitions of what a complete, accurate and consistent data is. So, it is difficult to ask for clean data when the directives are not in place and / or well communicated. Furthermore, there is nobody at the Enterprise level that is accountable for all the portions of the data assuring data is of required quality. The next factor, the technology. In fact, labs and inspectors do not have the tools to be as efficient as possible.

Data integrity refers to the completeness, consistency, and accuracy of data. Complete, consistent, and accurate data should be Attributable, Legible, Contemporaneously recorded, Original or a true copy, and Accurate (ALCOA).

Must be a stand-alone data center – Such analytical filter outlier data points or subsets of data that may not be significant to the analysis. Making the process more consistent this analytical filter resulted in higher significance for regression equations.

Valid information is information that is correct and can be used for the purpose it has been gathered for without any discrepancies an example of valid information would be attendance reports you send to the office or they receive at the office, it is important this information is valid otherwise it could cause a student to be kicked out for low attendance even if they have been there every lesson.

Reliability - information that is presented is truthful, accurate, complete and capable of being verified

• Are there current standards or level for the data item that are needed? If not these need to be established.

Information quality is often a key dimension of end-user satisfaction instruments (Ives et al., 1983; Baroudi & Orlikowski, 1988; Doll et al., 1994). Information quality is often not distinguished as a unique construct to measure success but is measured as a component of user satisfaction. Fraser & Salter (1995) developed a generic scale of information quality, and others have developed their own scales using the literature that is relevant to the type of information system under study (Coombs et al., 2001; Wixom & Watson, 2001; Gable et al., 2003) whereby the domain basically depend a lot on the dimension of user satisfaction, how they perceive the information quality and whether it increases their job effectiveness.