HISTORY OF TRADITIONAL DATA ANALYTICS
Traditional data analytics is a process of crunching numbers in order to derive insights or recommendations. Despite being extensively used in business, its effectiveness has been questioned in recent years.
First introduced in the 1960s, data analytics began to gain traction in the early 2000s as its applicability to business became crystal clear. According to a study by McCrory Group, 78% of top businesses employ analytics, with a projected $115 billion market by 2020. However, this growth has been met with some skepticism, as data analytics is often seen as a blunt instrument that fails to take into account the nuances of individual situations.
A commonly cited example is the over-reliance on correlation in data analytics. Correlation is when separate variables move in tandem, such as when a rise in crime rates is often accompanied by an increase in the use of security cameras. However, correlation does not imply causation, and it is easy to mistakenly assume that a correlation is a cause.
This susceptibility to error has led to critiques of data analytics as a “black box” that produces facile and often inaccurate conclusions. For instance, data analytics is often used to identify patterns in large data sets, but it is often difficult to explain the reasons for these patterns. As a result, data analytics often ends up drawingimplausible conclusions, such as the suggestion that a particular brand is more likely to attract consumers.
