In early computing, the term Garbage In Garbage Out (GIGO) was popular and well known. It was meant to remind us that computers process all data without judgment. In other words, the quality of data processed by computers (or used to create data visualizations) is not guaranteed. If your data is wrong, your results will be wrong.

While what we just mentioned might be obvious, it may not be obvious that a data visualization you are reviewing was generated using data with poor quality and therefore is presenting an incorrect picture. Remember the visualization of the big dipper from Chapter 1, Introduction to Big Data Visualization? Imagine what it might look like when using incorrect data points:

Data visualizations will only show the value if the data used to create the visualizations has had its quality assured to the appropriate level through routine and regular review and evaluation, practices that, when using large volumes of data, can become extremely demanding...