Data Quality and Reconciliation

Data Quality

Effective data use and analytics programs start with collecting high-quality data that can be used with trust for decision making. High quality data is accurate, reliable, precise, complete, collected in a timely manner, and is recorded in a consistent format and confidential way. As part of any data collection and data-use initiative, Project Balance will first assess your data-capture tools and data extraction processes as well as to conduct an audit of the quality of the existing data. Based on your current processes, we will recommend updates to processes and data security to assure that data collection or extraction from source systems is as high quality as possible.

Reconciliation and Testing

A database or data warehouse is only as good as the data it stores. As part of the design and development of a data warehouse, Project Balance investigates ways to assure accurate, complete, conformant and timely data is available for end-users. This includes documentation and testing of Extract, Transform and Load (ETL) business rules, report calculations rules or other programmatic driven changes to the data. It also includes traceability testing by entering data through sources systems or creating dummy data sets and tracing the data through to reports or visualizations. As part of the ETL process, we identify and hold back records that contain exceptions and only let clean data into the warehouse.

How Can We Help?

This site is protected by reCAPTCHA and the
Google Privacy Policy and Terms of Service apply.