Organizations increasingly rely on analytics and advanced data visualization techniques to deliver incremental business value. However, when their efforts are hampered by data quality issues, the credibility of their entire analytics strategy comes into question.

Because analytics traditionally is seen as a presentation of a broad landscape of data points, it is often assumed that data quality issues can be ignored since they would not impact broader trends. But should bad data be ignored to allow analytics to proceed? Or should they stall to enable data quality issues to be addressed?

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