There’s no debating the value of data in the modern world. Forbes recently referred to ‘data as the new oil’ indicating it is now the most valuable asset to organisations globally. Therefore, when undertaking a systems change and associated data migration, the primary focus shouldn’t be on the technology – it should be on the data. And in particular, ensuring quality data so that it continues to deliver value into the future.
Data Quality, in the context of data migration, refers to the process of assessing, improving, and maintaining the accuracy, completeness, reliability, and consistency of data as it is transferred from one system to another.
The Importance of Data Quality in a Data Migration
Imagine you’re moving into a new house, and you’ve hired a moving company to transport your belongings. You trust them to handle your valuables with care and ensure everything arrives intact. Similarly, during data migration, your data is being moved from one system to another. The trust you place in this process is akin to trusting a moving company. Here’s why data quality is essential:
- Data is the Lifeblood: Data is the lifeblood of modern businesses. It influences decision-making, customer interactions, and operations. Poor-quality data can lead to disastrous consequences, from incorrect analytics to misguided business strategies.
- Cost-Efficiency: High-quality data reduces the chances of errors and re-work. Fixing data quality issues post-migration is often more time-consuming and costly than addressing them beforehand.
- Compliance: Many industries have strict regulatory requirements for data accuracy and security. Failing to maintain data quality during migration can lead to compliance issues and legal troubles.
- User Satisfaction: Data quality affects the end-users’ experience. If they encounter incorrect or missing information, it can erode trust and frustrate customers and employees alike.
- Operational Efficiency: Accurate data ensures that systems and processes work as intended. It minimises disruptions and keeps your operations running smoothly.
A Data Quality Rule: The Key to Success
To ensure data quality during a migration, it’s essential to establish a Data Quality rule. This rule incorporates both the processes that validate and enhance data quality and the processes that manage data quality errors. Here’s how you can implement a Data Quality rule effectively:
- Set Data Quality Standards: Clearly define what constitutes high-quality data in your organisation. This may vary depending on the type of data and its usage.
- Assess Current Data Quality: Before migration, assess the quality of your existing data. Identify areas that need improvement.
- Data Mapping and Transformation: Develop a clear plan for mapping and transforming data to match the target system’s requirements. Ensure that this process aligns with your data quality standards.
- Validation and Cleansing: Implement validation procedures to catch errors before they enter the target system, including checks for completeness, accuracy, and consistency.
- Data Volume: Handling large volumes of data can strain resources and systems, so the infrastructure and processes need to be in place to manage massive data sets.
- Error Handling: Establish a robust process for managing data quality errors. Define responsibilities and workflows for resolving issues promptly.
- Data Ownership: Assign data ownership to individuals or teams who are responsible for data quality throughout its lifecycle.
- Stakeholder Engagement: Engage stakeholders from both the technical and business sides of the project.
- Data Security: Prioritise data security at every stage of the migration. Encrypt sensitive data and ensure secure transfer protocols.
- Testing and Validation: Conduct thorough testing of the migration process to identify and rectify any data quality issues.
- Documentation and Monitoring: Document all data quality procedures and continuously monitor data quality in the new system. Regular audits can help maintain high standards.
Zero Unplanned Defect Migration
In a data migration there is one golden rule that applies – aim for a zero unplanned defect migration. This means that defects, or data quality issues, are acceptable only if they are planned for and tracked with full awareness of their impact on the target system. In other words, defects are okay as long as they are not surprises. This principle aligns with the idea that data needs to be at the correct level of quality at the right point and time.
The reason behind this rule is simple – unplanned defects can wreak havoc on your business operations. Think costly disruptions, compromised customer trust, and possible damage to brand reputation. Anticipating and mitigating defects is therefore a key part of your data quality strategy.
By anticipating and addressing data quality issues proactively, you can steer your data migration towards success. Embrace the principles of data quality, plan meticulously, engage stakeholders effectively, and navigate the seas of data migration with confidence. As data migration specialists we know all the steps to take, so get in touch and we can help you move towards a data-rich future.