Many organizations ignore the long term data quality solutions in order to achieve short term goals but this leads to bad consequences. Consequences of not addressing dirty data in the system leads to adverse conditions which may include attrition rates of the customers or big losses in market share. Many analysts have warned about the severe consequences like business failures if the data quality issues are not addressed properly in the businesses. Therefore a constant data quality enhancement process has to be carried in the organization and all the steps of improvement have to be carried out. Here are the major steps in data quality improvement which every data oriented business has to perform.
Uncovering data defects through data profiling is the first step needed in quality improvement and it is the process of analyzing the data for correctness, uniqueness, reasonability and completeness. It was once a tedious task but now data profiling tools have made this task easier and automatic for you. You can also leverage the functions of data mining tool you use to cover the aspects of data profiling. By performing the process of profiling you can well understand how much data dirt is there in your system.
Data Cleansing and Data Quality Improvement
After you get an insight on how much data dirt is present in your system, you can start with the data cleansing method. Cleansing of data is a much labor intensive and time consuming process but it has to be done as much as you can. The best solution is to prioritize data elements as critical, important and insignificant and then start concentrating on cleansing methods. You can now keep an eye on the constraints and focus on the critical and important areas for cleaning the data first. You don’t need to cleanse the data all at once but you can take it as an ongoing process which is required always. Data cleansing solutions today are able to handle all the common data quality improvement problems and it is recommended to use some best tools associated to data cleansing.
Preventing Data Defects
This the next best challenge in data quality improvement process. The data dirt has to be prevented from entering the system from every possible point of entry. The root causes of data defects have to identified which may include these possible causes.
- Defective logic of programs
- Not understanding enough about data element
- No domain definitions
- No process for verifying the data
- Poor training for data entry
- Insufficient time for data entry
- No incentive if the quality of data entry is good
- No process for reconciliation
Data defect prevention should cover all the instances of data entering into the system. With proper training and validation rules created for data entry, it can be possible to allow only high quality data in the system. The data importing and migration processes should be of high quality so that the data entered does not lack in data quality aspects.