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 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 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 ensure that data collection or extraction from source systems is as high quality as possible.
Data Security
We understand the importance of protecting sensitive information, including:
- Personally Identifiable Information (PII): Names, addresses, social security numbers
- Financial Information: Bank account details, credit card numbers
- Health Information: Medical records, doctor’s notes
- Authentication Information: Cryptographic keys, passwords, and secrets
- Intellectual Property (IP): Research data, trade secrets, and proprietary information
- Location Data: GPS coordinates and other sensitive location details
We protect sensitive data by employing industry-standard techniques including:
- Industry-standard authentication methods with multi-factor authentication (MFA)
- Granular, role-based access control (RBAC) to limit data access
- Encryption of data at rest and in transit (TLS/SSL, Public Key Infrastructure), column-level encryption for sensitive fields in databases as well as de-identification and masking of sensitive data as applicable.
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 ensure accurate, complete, conformant, and timely data is available for end-users. This includes documentation and testing of Extract, Transform, Load (ETL) business rules, report calculation rules, or other programmatic-driven changes to the data. It also includes traceability testing by entering data through source systems or creating dummy data sets and tracing the data through 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.