Data Quality Engineering Services
QA is critical. With QA-as-a-service, you get data quality engineering services that ensure you continue to get quality data and meaningful insights. QA-as-a-service prevents headaches and risks caused by human error. As many experienced leaders know, it only takes one mistake to cost you millions to clean up. And when your users are working with complicated applications within a large organization or a company that operates in various regions — the chances of human error are significant.
This presents a huge struggle for companies that don’t have the in-house expertise to ensure their data is clean and readily accessible. GAP builds data-minded engineering teams who are experts in navigating these challenges. With GAP’s QA-as-a-service you’ll get QA expertise, data quality engineering and process consulting along with manual functional and regression testing to ensure that your systems and applications are always working with quality data.
- Improve data quality
- Increase accuracy of conclusions
- Minimize human error
Data Testing Types with GAP’s QA Teams
Depending on the specific focus and objectives of the testing effort, some relevant types of testing that the GAP’s QA teams perform include the following:Â
- Data Validation Testing involves verifying whether data conforms to predefined standards and rules, ensuring it is accurate, consistent and free from errors. Data validation testing often includes checks for data format, data types and permissible values.
- Data Integrity Testing is aimed at ensuring data remains intact and reliable throughout its lifecycle. It involves checks to prevent data corruption, unauthorized changes and data loss.
- Data Migration Testing is specifically focused on ensuring the accurate and complete transfer of data from one system or environment to another. It validates that data is transformed and moved correctly, maintaining its integrity and structure.
- Database Testing encompasses various types of testing, including data validation, data integrity, performance testing (for database operations), and testing of database-related functionality. It ensures the database — and the data stored within it — function correctly and efficiently.
Why Do You Need to Test Your Data?
Ensuring the utmost accuracy of data used within the system is instrumental in fostering a culture of precision, reliability, and trustworthiness in organizational decision-making and operational processes. With data accuracy testing, businesses can verify the authenticity and completeness of their data, confirming critical information remains free from discrepancies, inconsistencies or inaccuracies that could potentially skew analytical insights and jeopardize the validity of operational outcomes.
Uniformity of data across modules and components within a system serves as a linchpin for maintaining operational coherence and ensuring seamless functionality. By testing data consistency, organizations can guarantee critical data elements remain synchronized and aligned across diverse operational facets, preventing discrepancies, errors and operational bottlenecks that could potentially impede the system's overall performance.
Nowadays, threats continue to evolve and grow more sophisticated. The security of data plays a pivotal role in safeguarding an organization's critical assets and sensitive information. By conducting comprehensive data security testing, businesses can proactively identify, analyze and address potential vulnerabilities or weaknesses within their data infrastructure, bolstering their defense mechanisms and fortifying their resilience against a myriad of cyber threats and malicious attacks.
In an era defined by stringent data protection and privacy regulations, the validation of data against industry standards and regulatory requirements serves as the cornerstone of an organization's commitment to safeguarding sensitive information. By testing data to guarantee protection and privacy, businesses can navigate the intricate landscape of data compliance with confidence and precision, mitigating the risk of non-compliance penalties and legal ramifications.
By identifying and rectifying data issues early in the process, you create a seamless and reliable flow of information. This comprehensive approach to data quality enhancement assures the data generated and utilized within the organization is not only accurate, but also consistent and up-to-date.
With an effective data quality improvement strategy, organizations can establish robust safeguards to prevent data loss, corruption and unauthorized alterations. By implementing comprehensive data testing procedures, businesses can proactively detect vulnerabilities and potential points of failure within their data infrastructure, ensuring the resilience and integrity of their valuable information assets.
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