Data Reliability and Quality are keys to make the right decisions in decision support systems based on analytical models since problems, errors, shortcomings which result from data, yields false outcomes even if analyses are applied through the best method. Decisions made by these improper effects deteriorate all the company operations, and this failure costs higher than predicted for companies. Thus, data quality studies are the leading process to apply at the outset of every project and so significant to be in steady control.
When it comes to Data Quality study, it means a process that includes not only Information Technologies (IT) equipments but also all the businesses and their units and that needs to be appreciated and conceived end-to-end. Correction process for any wanting or incorrect data are at a higher cost than making correct data input by checking data quality in the first data entries. This awareness should be adopted particularly in data production points all across the company.
Data Quality studies in companies describe the processes that do not start and finish as any project but those which have continued and will do so. Therefore, it requires regular measurement, observing the improvement all along and ensuring new data formation under set standards.
To begin with, data are analyzed in data quality studies and its process. Then, a series of actions and processes is designated to enrich data quality. Among these are as follows;
- Data Cleansing
- Data De-duplication
- Data Enrichment
- Designing
and improving Data Collection Systems
- Data control
- Verification
- Cleansing
- Loading
- Establishing Data Management System
- Controlling Data Quality regularly