Access and Feeds

Data Quality: The Critical Factor for Analytics Success

By Dick Weisinger

Do you trust your data? It’s increasingly easy to tap data into data analytics, machine learning, and other AI tools, but if the data itself is flawed, out of date, or incomplete, the exercise can turn into a classic case of garbage in/garbage out.

A survey by Corinuim Intelligence in June 2021 found that 80 percent of senior executives had concerns about the quality of the data used in their projects and found that it was difficult to frequently update and clean data to maintain accuracy, consistency, and context.

Melody Chien, Senior Director Analyst at Gartner, said that “data quality is directly linked to the quality of decision making. Good quality data provides better leads, a better understanding of customers, and better customer relationships. Data quality is a competitive advantage that D&A leaders need to improve upon continuously.”

Abe Gong, CEO of Superconductive, said that “poor data quality and pipeline debt create organizational friction between stakeholders, with consequences like degraded confidence. Data quality is critical to facilitate the making of decisions with confidence across the organization, enabling a singular understanding of what that data means and what it’s being used for.”

What kinds of data quality problems are common?

  • Duplicate data – overlapping and duplicate data aggregated from multiple databases and applications can skew the result of analytics.
  • Inaccuracy – data entry errors and the gradual decay of data accuracy over time can result in a loss of data integrity.
  • Ambiguities – Spelling mistakes and data improperly merged from multiple sources are some examples of problems that could lead to flawed data.
  • Hidden Data – Data might be collected but may not be available to analytics.
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