What Is a Data Bug?

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A data bug refers to an error or flaw in a dataset. If left unaddressed, data bugs significantly affect a model’s predictions or outcomes, which may be either favorable or unfavorable to the developer.

Data bugs are often hidden and hard to detect, taking into account the following:

  • Data Quality Issues: The quality of a dataset depends on five characteristics: volume, velocity, variety, veracity, and value. If it is too large, outdated, varied, inconsistent, or misaligned with the intended purpose, the data is of poor quality.
  • Data Bias: Elements in a dataset may be more heavily represented or weighted than others, making it biased towards a portion of the population. Bias does not accurately depict a model’s use case, leading to skewed outcomes, inaccuracies, and analytical errors.  
  • Data Selection Bias: Proper randomization of a dataset is not achieved when those who collected the data fail to include other samples. Doing so causes the dataset not to be equally balanced or objectively represented.
  • Data Cherry-Picking: An error in data collection known as cherry-picking occurs when only data that meets a specific set of criteria is included in a model to manipulate the outcome towards desired results.


How to Fix Data Bugs

Data bugs may remain undiscovered for a long time, leading to inaccurate results that cause enterprises to make faulty decisions and incur high expenses. Fortunately, there are two ways to address data bugs:

  • Improve Metadata: Since metadata is information about the data, users should focus on adequately labeling, defining, and categorizing raw and transformed data. This information should include descriptive metadata (details on data content and creation, validation, and transformation) and structural metadata (information on the technical design and specification of the data structure).
  • Iterate: Data bugs will affect a model regardless of how well or frequently they have been fixed. The only option left is to make changes to the data to improve quality and eliminate bugs. Versioning keeps track of revisions and ensures that accurate data is used.


Manage Data Bugs with Pachyderm

Real-world data is messy, and data bugs will be inevitable. But with iteration and continuous data quality improvement, they don’t have to bog down your machine learning projects. Keep track of fixes made to your datasets with Pachyderm and its best-in-class version control and data lineage features. Sign up for a free trial today to ease your team’s debugging issues.

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