The Seven Steps to Data Quality

October 5, 2023
The Seven Steps to Data Quality

It’s no secret that the world today is driven by data.

In every business decision, strategy formulation, and even in day-to-day operations, data plays a significant role. However, the utility of data is only as good as its quality and observability. Struggling with where to start your data quality and data observability efforts?

Here's a comprehensive seven-step guide to help:

Data Source Validation

The foundation of data quality is the source. Before you even start pulling data into your system, make sure the sources are reliable and well-documented. In the realm of DataFactory, this means using connectors to link to trusted data sources, whether they're cloud-based, on-premises, or even files.

Data Ingestion & Cleansing

Data ingestion is like the gatekeeper of data quality. As you ingest data, it's important to filter out any obvious inconsistencies or errors. The DataFactory offers a user-friendly drag-and-drop interface to set up initial cleansing steps in your data pipeline. This way, you can ensure the data entering your system is already of high quality.

Data Transformation & Enrichment

Data transformation and enrichment are where your data takes shape. The raw, cleaned data needs to be transformed into a usable format and enriched by combining multiple data sources. With DataFactory, you can build robust pipelines that include transformation steps, making sure the output aligns with your business needs.

Data Profiling & Anomaly Detection

Think of data profiling as getting to know your data better. It's about understanding its structure, content, and quality. Alongside, anomaly detection helps to identify any outliers or unusual data points. DataTrust offers machine learning capabilities to detect anomalies and auto-generate business rules for data profiling. This way, you can ensure your data not only meets the quality standards but also remains consistent.

Data Validation & Reconciliation

Validation and reconciliation are like the final checkpoints in your data quality journey. Validate the data against predefined rules and reconcile any discrepancies. DataTrust enables you to leverage data validation and reconciliation tools to ensure data integrity. By reconciling any discrepancies, you can ensure your data is correct, consistent, and reliable.

Data Governance & Cataloging

Data governance isn't just about managing data; it's about managing data in a way that's efficient and makes the data useful. Implement governance policies and catalog your data so it's easily findable and accessible. DataMarket offers a “shopping-style" interface to catalog and democratize data products, complete with ratings, reviews, and metadata. This way, you can make your data easily accessible and understandable for everyone in the organization.

Ongoing Monitoring & Auditing

The journey to data quality doesn't end with validation and reconciliation. It's important to continuously monitor data quality and conduct periodic audits to ensure ongoing accuracy. With DataTrust, you can keep an eye on data observability metrics and set up automated alerts for any quality issues. This way, you can ensure your data quality efforts are always up-to-date and effective.

Embarking on the journey to data quality and observability might seem daunting, but with the right steps and tools, it can be a smooth and rewarding process. Book a consultation to learn how RightData makes data quality a reality.