For any organization, data is an asset. However, for a successful business intelligence project it is imperative to maintain the quality of data. Your BI implementation partner would require strong data to help you generate breakthrough reports that can transform into actionable insights. Data quality can help you enhance your decisions making capabilities.
Data quality is a common issue in a Business Intelligence landscape.
But, how do we define data quality? Implementing a data quality strategy is not simple. Read this piece to discover some of the major characteristics that hold back to make up a data quality. Here are 10 pitfalls to avoid while implementing data quality strategy.
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