Avoid these common pitfalls while implementing a data quality strategy
Data quality is an asset to build a knowledge base. Organizations use it to perform a variety of critical data quality tasks, including correction, enrichment, standardization and de-duplication of your data. For building business intelligence and analytics environments, it’s imperative to maintain high data integrity. Your business intelligence implementation partner requires strong data to help you generate breakthrough reports that can be transformed into actionable insights. Data quality is critical to your decision-making capabilities.
Yet data quality management is a common issue in a BI landscape and implementing a data quality strategy is not simple. In this Industry Perspective, you can explore some of the major characteristics that make up data quality and discover the 10 pitfalls to avoid while implementing a data quality strategy.
- Is your organization overlooking the processes while managing data within the applications?
- Are you using metadata and a business glossary for complete business terms?
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