Limitations of Traditional Statistics

Recently in class we learned about the limitations of traditional statistics. While they can be useful in some aspects, there are some disadvantages that limit their uses, especially when compared with Big Data. Here are a few we learned about:

Volume

One of the main setbacks of traditional statistics is they cannot handle large data sets. When we consider the volume of Big Data, some applications cannot compare every single data point, therefore hindering the accuracy of the data. One example would be Microsoft, which uses traditional statistics in applications like Access and Excel. These are held back by their storage capacity. Even still, these applications struggle well before they reach their limit if the hardware being used is not powerful enough. These limitation result in data that cannot be analysed to their full potential.

Variety

Traditional statistics struggle to handle qualitative data. Furthermore, Big Data contains unstructured, semi-structured and structured data. While traditional statistics are well equipped to analyse numerical data, they struggle with unstructured data in particular and may find it difficult when approached by sentences, audio or images. This is not cool.

Velocity

Another limitation would be the velocity. When traditional statistics are used it can take time to complete, compile, and conclude. This means traditional statistics are unable to be used for real-time information like the stock market. The results created will not be useful because the original variables will have changed, meaning we cannot rely on the results.

References:

https://ilearn.fife.ac.uk/course/view.php?id=9751#section-6