TDWI Checklist Report: Agile Data Quality

TDWI Checklist Report | Agile Data Quality: Speed, Flexibility, and Business Alignment for Improving Data

June 17, 2013

Data quality (DQ) has always been a moving target, because enterprise data represents real-world entities that naturally evolve over time, such as customers, products, partners, and employees. Data quality and data stewardship practices have a solid track record of providing solutions that adapt to entity evolution and incrementally counteract data’s natural degradation.

As if that weren’t challenging enough, data quality professionals are under renewed pressure to identify and provide quality improvements for new sources and types of data, as organizations deploy new applications, implement new customer or partner channels, explore big data, and tap into new sources such as machine data and social media. Like everyone else nowadays, data quality teams must “do more with less” and deliver solutions in weeks instead of months.

To keep pace with accelerating demands for data quality solutions, many data quality teams and tools have embraced practices drawn from agile development methods. The agile method for software development has been in use for more than 10 years, and its tenets are summarized in the Manifesto for Agile Software Development. Agile methods originally focused on the development of hand-coded procedural logic for operational and transactional applications. Agile data quality is where agile methods are applied to data quality projects and solutions.

This TDWI Checklist Report discusses how organizations can achieve greater agility with data quality projects through adjustments to data stewardship, business processes, and technical development methods. The report also looks at critical success factors for agile data quality, such as tool features, team structures, self service, data-driven documentation, and data services.

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