The Data Bug

Regarding defects in human-built systems, the term "bug" appears to have been coined by Thomas Edison in 1876 to describe problems in his systems. Bug has been defined as  "an unexpected defect, fault, flaw, or imperfection".

Like the "system" or "software" bug - the "data" bug is a defect, fault, flaw, or imperfection in data. Data bugs may be hidden and difficult to find - considering the following:

  • Data quality issues
  • Data veracity issues
  • Data bias issues
  • Data cherry-picking issues
  • Data selection bias issues

Further, humans are flawed and have both naked and hidden biases as well as other incentives to skew data to obtain a desired result, including:

  • Confirmation bias issues
  • Narrative fallacy issues
  • Cognitive bias issues

Data science results from data bugs may be extremely serious - they are sometimes impossible or very difficult to detect and may trigger errors that can cause a myriad of secondary effects, resulting in an illusion of reality and bad decisions.

Moreover, data bugs may remain undetected for long periods of time. Data has many secondary uses with low barriers to sharing, combining with other data sources and transformation or manipulation.

What is urgently needed is a new "meta-data reporting system" that labels, defines, rates and categorizes all new and transformed data (structured, raw unstructured and semi-structured). This goes beyond the traditional simple "meta-data" definitions. Meta-data is information about data - describing how and when and by whom a particular set of data was collected, and how the data is formatted. Descriptive meta-data is about the data content and the creation, validation and transformation of the data - as well as specific instances of data application. Structural meta-data provides information about the technical design and specification of data structures.

A new meta-data reporting system should include:

  • Creation and origins of data (sensor, author, computer system)
  • Data transformation history (integration with other data)
  • Data quality ratings
  • Data veracity ratings
  • Data validation ratings
  • Records of specific instances of data applications and results
  • Potential bias ratings
  • Potential data manipulation ratings

This detailed meta-data information should follow the data like a "chain of data evidence" - for future users of the data. This is especially useful after the data is sliced, diced and combined with other data sources.

Along the "chain of data evidence" future users can add reports detailing real or potentially hidden data bugs. Included in the data bug reports would be veracity reports, quality reports, defect reports, fault reports, problem reports, trouble reports, and other potential data evidentiary issues.