In Agosta's (2005) Article, Some Of The Trends In Data Quality Management

5766 words - 23 pages

Augosta Week 4 DQ 1University of PhoenixDBM500: Database ConceptsAugust 24, 2007In Agosta's (2005) article, some of the trends in data quality management include: improved meta data quality, data profiling, standardization, and scoreboard reporting.Agosta identifies there where there is data, there is metadata, and the same quality practices that apply to our data should be applied to the metadata.The ability to profile data and determine any defects or quality issues is also another trend which vendors are focusing on in the coming years.The standardization of tools, processes and workflows helps make the end-to-end process of data entry to reporting measurable against quality standards.Finally scoreboard reporting allows admisitrative views into the operations of databases, including data quality.Mike S.ReferencesAgosta, L. (2005, February). Trends in data quality. DM Review, 157(2), 34-36.Vendors need to pay attention to the trends in data quality management such as the growing amount of end-users using the database requiring high-availability databases; the growing data sources, various application integration requirements with integrated customer view, and the demand for more unique and complex queries.Meeting the business requirements is an operating principle that the organization's database must satisfy. For example, the database must be secure to protect the organization's assets, it must be able to meet all the organization's requests, it must be able to efficiently store and retrieve data, it must be very stable, and must pass rigorous testing before implemented into production.Developing a timely system and resulting in a cost effective and productive system are also important roles from business rules in database development. These roles allow the organization to maintain its competitive edge by maintaining its development budget while implementing or producing a very productive database.In his article, Agosta (2005) discusses data quality and gives an example of how bad data can cost a business money and display the need for improved data solutions. The example he gives is if mail and packages keep getting returned because of incorrect data, and get duplicated multiple times, then it becomes a waste of time and money creating operational inefficiencies. He then goes on to talk about meta data quality, and how the quality practices that are applied to data now should be given to meta data and that businesses will need to adhere to strict business rules and related meta data to achieve meta data quality. Agosta also acknowledges that data profiling is the first step and not a standalone process in improving the quality of information. It is an important step in examing data for different purposes. In another article, BI Scorecard: Reporting capabilities (2004), the one line that stands out is that reporting can be boring, and that analysis leads to action. By having imrpoved data quality, there is more of a likelihood of action and...

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