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Cost Of Poorly Labeled Data

Poor data quality has a significant negative impact on companies. Not only are there financial costs, poor data quality also impacts organizational effectiveness, employee productivity and experience, and in the worst cases customer experience. IBM has estimated that poor data quality costs U.S. businesses $3 trillion per year.

Poor quality training data set projects back and divert focus from data scientists working on models. Poor quality training data reduces the results and value of ML projects, impacting organizational innovation. Most importantly, if left unchecked can impact the customer experience in the end product or service. Machine Learning is already a requirement for companies that want to compete and innovate. The cost of being left behind is too high. 

Download this complimentary white paper to learn what causes poor quality training data and how your business can invest in preventing it. Find out more about:

  1. What Constitutes “Garbage” Data?
  2. Common Causes of Low Quality Training Data
  3. The Costs of Prejudicial Bias
  4. How To Combat Systematic Value Distortion

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