Integration and Data Quality
The extraction, transformation and loading (ETL) of data is the key to success in a BI system that let you manage the quality of the data properly.
We Ensure the data cleanliness and quality by using powerful ETL and Data Quality solutions.
Experts on integrating all data sources: transactional, unstructured, external ... creating consistent patterns in the organization.
Different or sparse data with heterogeneous formats in a common model suitable for the business.
Clean and updated data is the key to build and use a Business Intelligence system.
Integration benefits
The data integration benefits following quality techniques are:
- Get a higher confidence and value in the information
- Improve the efficiency in the business processes
- Cut Down costs
- Improve the client's satisfaction
- Make the right decisions
Best practices
Best practices we recommend for designing ETL processes:
Procedures
Centralization of procedures, so as to ensure coherence and consistency of the exchanged data from different sources.
Loading data
Implement processes loading information to avoid possible errors in the initial information.
Avoid redundancy of calculations
If there is data previously calculated in the operational databases, it should not be re-calculated in the extraction. This premise seeks to achieve two objectives:
1.- Improve process performance.
2.- Avoid possible inconsistencies between the results of operational systems and those obtained in the DW.
Data Quality
Setting "quality control" and validation points:
1.- Technical: ensuring the correct execution of processes.
2.- Functional: reports which allows you validating the data added.
Intermediate tables
Consider the possibility of using intermediate tables with the atomic level of information to be handled.
