With all of the recent developments in data storage and production, we now have access to an overwhelming variety of information abundant at an incredible speed, which is all only increasing by the second. Every moment, hundreds upon thousands of pages of information are created, adding novel information to a database already bursting at the seams. Big Data, however, is more than just a breakdown of this enormous amount of information. This notion in mind, a variety of myths have arisen as a result of data analytics’ growing prevalence across countless facets of society.
I previously explored a few of these misconceptions, but there are still several more that I feel should be addressed, as they are both unfounded and detrimental to the data analytics community at large.
Myth: “Big data will always accurately predict the future”
Parts of this myth are technically true — big data can aid in the prediction of future metrics and trends, but it is not, by any means, a foolproof crystal ball. In reality, data analytics cannot guarantee anything to be certain. This misconception, clouded as it may be, is not surprising; big data has become so advanced, and has become so sophisticated in mitigating error, that perhaps we have come expect constant pinpoint accuracy.
Myth: “You should archive all of your data”
While it may be helpful to hold onto certain data for future reference, it is both unnecessary and unconstructive to archive “every scrap of data that could ever be collected.” Data hoarding can be just as unhealthy as that of physical possessions; it will only make the location of important data harder to achieve. That said, it is important for data-based companies to prioritize the data they store for disaster management, customer analysis, and other key situations where quick and efficient data recovery may be imperative.
Myth: Every company is making the switch to big data
It is true that an increasing amount of businesses are switching to a data-oriented infrastructure, but many others have continued to function without big data altogether. For many of these entities, big data remains an ambition, an alluring shift in corporate paradigms that will likely see implementation in the foreseeable future; at the same time, however, they represent a mindset that is reluctant to rush into such a significant change without careful planning. This approach stands in stark contrast to an overarching fear associated with modern big data: inescapable obsolescence created by other companies’ quick moving ambition. Still, though, it is simply too early to generalize all businesses as immediate big data proponents.