The paper "Managing Information in the Enterprise" is a perfect example of a term paper on business. a. Data quality refers to whether a company’ s data accurately reflects the real-world constructs that they are supposed to represent. Highly accurate data, then, meets this definition of data quality. However, this definition of data quality is limited to the extent that it mentions only consistency between the data and the real world. Accordingly, while the agreement with the external world is a necessary condition for data quality, it is not a sufficient condition.
A second necessary condition for data quality seems to be an internal agreement between the data. Data consistency between different applications within an IT ERP is an essential quality of data quality; together with external validity, this internal consistency forms the necessary and sufficient conditions of a definition for data validity. b. The difficulty of measuring improvements to data quality stems naturally from the difficulty of measuring data quality itself. “ Despite the widespread awareness of the high price of low-quality data, initiatives to improve data quality are not necessarily easy to measure in classic budgeting logic” (Rizy, Feil, Sniderman, & Hall, 2010, p.
5). The trouble seems to arise when one realizes that data is not directly translatable into dollar costs and profits. The first step in measuring data quality is to breaking down discrete business processes in terms of outputs and inputs. For processes like supplier onboarding, the manager can begin to compare how problems with data quality negatively affect the effectiveness and efficiency of that process. Once a manager determines the inputs for a process, one can begin to get a sense of how much an ideal process (with perfect information) would differ from the actual process, which gives some quantifiable measurement for data quality as it actually exists in an ERP system.
c. In a larger firm, business knowledge is decentralized. It is important for a manager to be informed and updated by his staff on operational problems. “ Without deep collaboration between IT and line-of-business managers, it can be hard for organizations to be on the same page in terms of information management projects and their value to the enterprise” (Rizy, Feil, Sniderman, & Hall, 2010, p.
10). Without collaboration between elements within the organization, it is difficult for any manager to put his finger on data quality within the firm. Accordingly, a manager should be cognizant of the threats that poor data quality poses to enterprise planning and anticipating problems that arise from the IT side. Data quality audits, in which both the manager or someone from another department ensures internal and external consistency of the data could be a useful solution for detecting problems before they begin to affect the bottom line.
d. Sourcing problems in data quality is a difficult task. “ Line-of-business executives and IT frequently disagree about the source of data-related problems and the potential solutions” (Rizy, Feil, Sniderman, & Hall, 2010, p. 2). Cutting through the disagreement, a manager must work with both sides in order to find some agreement on where data problems arise. While IT takes a technical approach, executives tend to ascribe inconsistency to human error. These disagreements could prove costly in projects to clean up data, which is why a manager must become a mediator: urging both sides to consider the organization as a whole, rather than their own specific issues (p.
8). Since knowledge in an organization is dispersed, the source of the problem will only be found by asking experts on the ground, making it necessary to solve disagreements before addressing data issues. e. Feelings of ownership are good when dealing with data quality. The reason is that, like any other factor of production, data quality is an instrument in creating a good or service; a lower quality instrument will lead to a lower quality product.
When individuals feel ownership of their tools, they tend to take better care of those tools. When IT and executives take better care of data, internal and external validity increases, making for better uses and management of data quality (Rizy, Feil, Sniderman, & Hall, 2010, p. 13).