Guarantee of Data Warehouse quality is crucial from the early stages of project because of its strategic impor?

Yes, guarantee of data is very crucial from the very first day of the project to its completion. We should take care of it from very first day so we should not loose our data quality. If some one asks me how good company’s data, I can only know using only metrics. Data quality is a multi dimensional concept. We must deal with both subjective perceptions of the individuals involved with the data and the objective measurements based on the data set in question. Subjective data quality essments reflect the needs and experiences of stakeholders, the collectors, custodians, and consumers of data products. This was the approach adopted for uring the quality of products too. When performing objective essments, companies follow a set of principles to develop metrics specific to their needs, there is hard to have “one size fits all” approach. Refinements of these functional forms, such as addition of sensitivity parameters, can be easily incorporated. Often, the most difficult task is precisely defining a dimension, or the aspect of a dimension that relates to the company's specific application. Formulating the metric is straightforward once this task is complete. There are different types of data quality measurements and metrics. simple ratios measure desired outcomes to total outcomes. Managers prefer the ratio showing positive outcomes. Our data should be free of error. The free-of-error dimension represents data correctness. Our schema should be complete. There should be no entry missing in the data and this leads us to completeness. Data should be believable and credible and true. A working definition of the appropriate amount of data should reflect the data quantity being neither too little nor too much. A general metric that embeds this tradeoff is the minimum of two simple ratios: the ratio of the number of data units provided to the number of data units needed, and the ratio of the number of data units needed to the number of data units provided. It is very important that data quality management is undertaken as a continuous improvement process...not a one-shot deal! We can only get total data quality by establishing the data quality management environment between information system project managers and establishing conditions to encourage team work between functional and information system development professionals.