Shifting Left from Quality Assurance to Quality Engineering for Data Programs: Mphasis’ Comprehensive Approach

Imagine a retailer managing customer data from multiple sources—point-of-sale systems, e-commerce platforms, and loyalty programs—all generating data in different formats. Without early-stage quality checks, discrepancies may slip through, unnoticed until customer insights are consolidated. At this point, identifying the source of errors becomes a labor-intensive process, draining both time and resources. This scenario is more common than you'd think. In fact, businesses lose an average of USD 12.9 million annually due to poor data quality, as reported by Gartner.

As data volumes surge, relying on traditional Quality Assurance (QA) methods becomes unsustainable. These reactive processes, typically applied at the end of the data pipeline, focus on detecting issues only after they've occurred. The result? Costly fixes, operational bottlenecks, and compromised decision-making. To overcome these challenges, organizations must shift left from conventional QA to Data Quality Engineering (DQE)—a proactive, integrated approach that ensures data quality from the outset.

What is Data Quality Engineering (DQE)?

At its core, Data Quality Engineering represents a fundamental shift from validation at the end of a data pipeline to continuous, automated quality checks throughout the entire process. By integrating testing and validation into every phase of data ingestion, transformation, and storage, DQE ensures that organizations can catch and correct issues as early as possible.

Mphasis’ DQE approach is a game-changer. With an emphasis on automation, it replaces manual checks with advanced tools that execute quality tests in real time, offering unprecedented speed and accuracy. The process ensures consistency, reliability, and high performance across all stages of data management.

For example, according to McKinsey, companies adopting end-to-end automation in data management have seen up to a 50 percent reduction in data errors. These efficiencies are crucial for data-heavy sectors such as financial services and telecoms, where large-scale data ingestion and transformation are daily activities.

The Current State of Data Quality in Organizations

Although Agile and DevOps have streamlined software development, similar advancements in data management have been slower to materialize. Data programs, hindered by the complexity of their environments, often struggle to keep up with the evolving demands for speed, accuracy, and scalability.

The lag is largely due to the unique nature of data systems. Unlike software development, where code testing can be performed in isolation, data quality depends on multiple interconnected systems working in harmony. A single data quality issue can ripple across various departments, negatively impacting everything from business intelligence to customer experience. Their impact on business outcomes, from delayed analytics to poor customer experiences.

Consider an organization preparing data for a machine learning project. The data must be accurate, clean, and consistent across different sources before it can be used to train AI models. In a fragmented environment, achieving this level of quality through traditional QA would require significant manual intervention, leading to delays and increased costs.

Mphasis’ DQE Solution: Redefining Data Quality Management

Mphasis’ DQE solution automates the entire data quality lifecycle, addressing the complexity of heterogeneous data environments with a comprehensive, integrated approach. By combining continuous validation with advanced automation, the Mphasis DQE solution empowers organizations to improve data accuracy, reduce errors, and enhance overall operational efficiency.

Key features of the Mphasis DQE solution include:

• Continuous Testing and Validation: Quality checks are embedded at every stage of the data pipeline, from ingestion to transformation. The continuous testing ensures that data quality issues are detected and addressed in real time, minimizing the risk of downstream errors.

• CI/CD Pipeline Integration: The DQE solution seamlessly integrates with CI/CD pipelines, automating data validation alongside regular code deployments. The alignment with DevOps processes ensures that data quality becomes an inherent part of the development workflow.

• Automated Data Validation: By leveraging machine learning and rule-based algorithms, the solution automates critical aspects of data validation and transformation, enabling organizations to scale their data quality efforts without increasing manual overhead.

• Cross-Platform Compatibility: Mphasis’ DQE solution is designed to operate across diverse technologies and platforms, ensuring data quality is maintained regardless of the system in use. This flexibility is essential in managing the complexities of modern data ecosystems.

Challenges in Adopting DQE and How Mphasis Overcomes Them

While the benefits of DQE are clear, the transition from traditional QA can pose challenges. Integrating new tools into existing infrastructures, ensuring scalability, and customizing solutions to meet specific needs are often top concerns. However, Mphasis addresses these issues with a tailored approach, offering flexible, scalable architectures that allow organizations to adapt the DQE solution to their unique environments.

Mphasis’ expertise in managing data heterogeneity further ensures that the DQE solution can handle the complexities of even the most fragmented ecosystems, providing organizations with a robust framework for managing data quality at scale.

The Future of Data Quality Engineering

As data ecosystems continue to evolve, so too will the role of Data Quality Engineering. Emerging technologies, including AI-driven quality checks and advanced analytics, will play a pivotal role in the future of DQE. These innovations will enhance the ability to detect and address data quality issues in real time, further improving the efficiency and accuracy of data programs. Organizations that embrace DQE today will be well-positioned to leverage these advancements as they emerge, ensuring they remain at the forefront of data-driven innovation.