By Sweta Suman, Senior Engineer, Target in India
In today’s digital landscape, data is a core asset. From strategic planning to real-time operations, organisations rely on data to guide their decisions. But the true value of data depends on its quality. Inaccurate, incomplete, or inconsistent data can lead to flawed insights, poor decisions, and costly errors.
To support reliable decision-making, many organisations build certified datasets—trusted, high-quality data assets that meet strict standards for accuracy, consistency, and governance. The process of creating such datasets involves a structured, end-to-end approach where every step plays a vital role.
- Understanding Business Requirements
The first step in delivering high-quality data is understanding the business need. This involves close collaboration between data teams and business stakeholders to define the purpose of the dataset, what insights it should support, and how it will be used.
Capturing clear requirements ensures alignment from the start. It also helps define the expected data sources, structures, and quality standards. These expectations are often formalised into a specification that guides the development process.
- Data Discovery and Validation
Once the requirements are clear, data engineers begin exploring the source systems. This step involves validating that the source data matches the expectations, identifying any inconsistencies or anomalies, and confirming its suitability for transformation.
This early discovery phase is critical to avoid surprises later in the pipeline. It provides the foundation for how the data will be processed and ensures that known issues are addressed up front.
- Designing the Data Pipeline
With discovery complete, the next step is designing the data pipeline. The pipeline typically includes two stages: ingestion and processing.
Ingestion involves collecting raw data from various sources and loading it into a centralised staging area. This may include batch jobs or real-time streaming, depending on the use case.
Processing transforms the raw data into usable form. This includes applying business logic, validating schemas, filtering out errors, enriching records with additional context, and standardising formats. Each stage is designed with quality checks to catch issues as early as possible.
- Development and Testing
Pipeline development is handled using modern data engineering practices. Code is version-controlled, modular, and built to scale. Best practices ensure that transformations are efficient, error-resistant, and maintainable.
Testing is done across different environments to ensure the pipeline behaves as expected. This includes unit tests, data validation, and end-to-end checks to confirm that the final output meets business needs and data quality standards.
- Deployment and Automation
After successful testing, the pipeline is deployed to production using automated deployment tools and scheduling systems. Automation ensures consistency, minimises manual errors, and allows pipelines to run at predefined intervals or in response to events.
Automated orchestration also handles job dependencies, monitors task completion, and retries failed tasks when needed. This makes the entire process more reliable and scalable.
- Monitoring and Observability
Once the pipeline is live, ongoing monitoring is essential. Dashboards are set up to track data volume, pipeline performance, validation results, and data freshness.
Monitoring allows data teams to detect anomalies, performance issues, or delays before they affect end users. This visibility is key to maintaining trust in the data and ensuring that certified datasets continue to meet expectations over time.
- Data Governance and Compliance
Reliable data isn’t just about correctness—it also needs to be well-governed. Governance ensures data is secure, discoverable, and used responsibly.
This includes cataloging datasets for easy access, classifying data based on sensitivity, auditing access and changes, tracking data lineage, and enforcing retention and privacy policies. Governance builds accountability and supports compliance with organisational and legal standards.
- Certification and Approval
The final step is certification. Once the dataset meets all quality, governance, and performance standards, it is reviewed and approved by both technical and business teams.
Certified datasets are then added to a centralised registry or catalog, clearly marked and made available for enterprise-wide use. This certification process builds trust and ensures teams are working with consistent, validated data.
Conclusion
High-quality data doesn’t happen by accident. It requires intentional design, collaboration, and discipline across every stage of the data lifecycle. From understanding business needs to monitoring performance, each step plays a crucial role in delivering datasets that are accurate, reliable, and trusted.
In a data-driven world, certified datasets offer a strong foundation for confident decision-making—turning raw data into actionable, strategic insights.
