Emerson Process Management
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Question 1) What decisions have to be made to reach a solution?
The managers of Emerson process management might be interested in implementing Data Quality management. The goal of DQM is to improve data quality for the sake of getting high quality data. The main attributes of measuring quality data includes Completeness, uniqueness, accuracy, Timeliness, consistency, representation. It’s very important for establishing the measurement of data quality management as it establishes a baseline for measuring the quality of data. The other fundamental components are of DQM, the key components provide reusable and high efficiency of data management (Wang, 2012).
Figure 1: Components of DQM
Question 2) What steps must be taken?
The data dealing has been a quality issue for Emerson Process Management of Austin. Problems are faced with unambiguously data description, formation syntax. These can be mitigated by using the following process. Possibilities for this data quality management can be considered by Quality checks before finally loading it into Data Warehouse Data Base (Weidema and Wesnaes, 2016). Data cleansing can be done during ETL process, Loading and tagging problematic data such as referential integrity or value domains analysis can be done. Feedback to the data suppliers about the test result for the data can be done to mitigate the risk of data error collection. Manual correction and data cleansing can also be done as the frequent problem that occurs are already known by the domain expert. A continuous contact between the central data warehouses and source systems is therefore useful. Data Quality Management (DQM) for Data-Warehouse-System is the best approach that can be followed to measure the quality of the data.
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