Proactive Approach For Improved Data Quality In Data Warehousing

Since data warehousing is used as a facilitator for tactical decision making, the value of the high quality of the inherent data has increased many folds. Data quality issues are much like software quality problems. They can undermine the job at any given stage.

This being my first article in history, is more of a loud believing compared to a definitive group of steps. If you are looking for the best data quality platform then visit

data quality

Data Collection procedure:

Many businesses depend on the ETL tools out there on the market to make their unstructured data prepared for OLAP. These tools could be more effective when the data coming from the day to day used systems is having valid contents. So the information quality checks need to be implemented right from the data collection procedure.

As an example, we see that just in case there is a feedback set where users write ad-hoc feedback to your open-ended questions. To guarantee valid feedbacks are registered, techniques ranging from parsing feedback text for a number of keywords to elaborate text mining algorithms are employed. More effective methods of data quality checking may offload data quality load out of the following stages of the DW projects.

According to me, there are several separate characteristics of looking at data collection. One way to check at it is implicit data collection and explicit data collection. As an example, data accumulated at the machine, proxy or client degree for tracking user's browsing behaviour might have to be treated separately while preparing it to get mining when compared with data collected through data entry forms.