How do you handle data quality issues when integrating or migrating data from different platforms or formats?

The idea of using various tools and techniques is a profound suggestion to consider having defined what the main objective of the project is. Having done a data cleansing exercise and integration of disparate system into an ERP of a large city,the challenge was enormous and varied. The challenges ranged from data formats, size of data bases, source data, ownership, location. Currency of data , completeness just to name a few.

In my experience, assessing data sources is crucial. It involves understanding the current state of data quality, identifying issues like inaccuracies, incomplete data, or format inconsistencies. I’ve used tools for data profiling and validation, ensuring that the data aligns with the required standards and formats. This step is fundamental in setting the stage for successful data integration or migration.

I've learned to assess is a great starting point, while a process perspective matters too. Integrating and migrating data are not the same. Integrating (e.g. dashboard development): -- If data quality lacks you don't want to get stuck. This means a parallel strategy: 1) focus on data stewardship to improve quality, 2) simultaneously iterate on data products that deliver insights. Then repeat. Migrating (e.g. from one SSOT to another): -- This can only be done right once. Therefor this requires adequate validation, dry-runs, and eventually migration. When old mixes with new and quality from either source lacks it might be very hard (if not impossible) to guarantee consistent data quality and its accuracy. Tools help, process matters.

Antes de iniciar cualquier proceso de integración o migración de datos, es fundamental realizar un análisis exhaustivo de la calidad de los datos en su origen. Esto no solo ayuda a identificar problemas como duplicados o inconsistencias, sino que también permite establecer un marco de referencia para las expectativas de calidad en el destino. La implementación de técnicas avanzadas de perfilado y limpieza de datos asegura que el proceso sea eficiente y minimiza errores posteriores. Además, documentar claramente las reglas y estándares de calidad es clave para mantener la integridad y confiabilidad de los datos a lo largo del tiempo.

At work, I’ve learned that selecting the appropriate method for data integration or migration significantly affects the project’s outcome. Methods like ETL or ELT are chosen based on data volume, complexity, and the specific needs of the business. It’s essential to weigh the pros and cons of each method to find the most suitable one, ensuring efficiency and data integrity.

Actually, this boils down to what you have and what you want to achieve. The dataprep strategy is simply an outcome of that. Therefore it's not so much of a goal as described, but a means to an end. To explicate: when data models aren't similar a lot of transformation is required (and if not more important: describing the applied rules, assumptions and trade-offs being made). This means, the extent to which (possibilities exists on) data integration and/or migration differ from one model to the other put a center of gravity on extraction, transforming and loading. By that, I'm a firm believer on ETL - whereas you first want to extract, then transform and eventually load. In which each step requires more or less effort to make it work.

Al seleccionar un método para la integración o migración de datos, es esencial considerar cómo cada enfoque se adapta a las características específicas del proyecto y los datos involucrados. La decisión debe basarse en una evaluación cuidadosa de factores como la complejidad del entorno, las necesidades de transformación de datos, y la facilidad de mantenimiento a largo plazo. Además, es fundamental ponderar cómo el método elegido puede influir en la velocidad de implementación y la capacidad de adaptarse a futuros cambios en los requisitos comerciales y tecnológicos.

Garantizar la calidad de los datos durante la integración o migración es crucial para asegurar que los resultados sean fiables y útiles. Implementar comprobaciones rigurosas, como reglas de transformación y validación, ayuda a identificar y corregir errores antes de que impacten en los sistemas finales. Utilizar herramientas para monitorear y medir la calidad de los datos, como paneles y alertas, no solo facilita la detección temprana de problemas, sino que también permite una respuesta ágil para mantener los estándares de calidad. Este enfoque proactivo asegura que la información migrada sea precisa y cumpla con las expectativas organizacionales.

Implementing rigorous data quality checks during the integration or migration process is something I’ve found helpful. This includes setting up rules for data transformation, validation, and reconciliation. Monitoring tools are used to track data quality continuously, allowing for immediate corrective actions if discrepancies are detected.

Designing and implementing data quality checks into large-scale data migration or integration projects is (fortunately) something that has become a bit easier over the last 5 years. There are incredible tools available on the market now that help organizations quickly and easily validate that the data arriving in its new destination matches the data that left its source location, and this is even possible for big data. Additionally, new applications have been brought to market which allow for a human-in-the-loop model for spot-checking the data to be migrated or integrated. These apps can be configured for different thresholds for quality, which gives organizations the ability to decide what level of risk they're willing to assume.

Post-integration or migration, I always emphasize testing and verification. This step ensures that the data transferred is accurate and consistent with the source data. Using data comparison tools and scripts for verification, we can confirm that the data meets the set quality standards and is fit for use in the new environment.

Managing data quality is an ongoing process. It’s crucial to have a structured framework for regular monitoring and improving data quality. This involves updating data rules and policies periodically and using performance metrics to gauge data quality over time. Continuous improvement in data quality management is vital for maintaining data integrity.

Every organization today runs regular antivirus checks...often nightly. This should be no different for the management of your data and its quality. New tools have been introduced to the market which allow orgs to continually monitor all of their structured and unstructured data in place, right where it resides. While understanding this data at scale seems complex or daunting, the ongoing management of its quality is trivial since, on balance, an organization's complete set of data does not significantly change daily. Instead, incremental changes to the data through the addition, modification or removal of content occurs all the time and can be monitored as a nightly or weekly delta using new tools available to all organizations.

Data integration and data migration are two different topics and the temptation to generalise them under one topic should be avoided Data migration refers to the mass migration of data from a system to another. E.g., when old systems are being replaced by new ones either as part of systems upgrades, transformation programs or business events such as acquisitions and mergers Data integration refers to either data integration between systems or data integration between systems of records and systems of insights

Information architecture and information flow. When adding value to the bottom line of your organization take cognizance of how information flows from one functional area to the next. This is an important aspect in optimizing your company data architecture and information architecture.

Additionally, it’s important to foster a culture of data quality awareness across the organization. Regular training and updates for team members involved in data handling can significantly enhance the overall quality of data management.

Gestionar la calidad de los datos al integrarlos o migrarlos desde diferentes plataformas es crucial para asegurar decisiones precisas y eficaces. Los datos limpios no solo facilitan la consistencia y la fiabilidad, sino que también reducen riesgos asociados con errores y duplicidades. Es esencial implementar procesos robustos de validación y normalización de datos antes de la integración, así como utilizar herramientas de limpieza automatizada que detecten y corrijan anomalías. Además, la colaboración entre equipos de TI y usuarios finales es vital para identificar y abordar las diferencias semánticas y estructurales entre fuentes de datos.