Big Data and Business Intelligence: Debunking the Myths

TL;DR

This paper critically examines big data in business, especially social media data, debunking myths about its size, completeness, and methodological implications, and clarifies its actual capabilities and limitations.

Contribution

It clarifies misconceptions about big data's role and quality in business intelligence, emphasizing the importance of understanding data variety, speed, and biases.

Findings

Big data's volume does not eliminate traditional methodological issues.

Social media data is often incomplete and biased.

Big data is not a fully unbiased or comprehensive decision-making source.

Abstract

Big data is one of the most discussed, and possibly least understood, terms in use in business today. Big data is said to offer not only unprecedented levels of business intelligence concerning the habits of consumers and rivals, but also to herald a revolution in the way in which business are organized and run. However, big data is not as straightforward as it might seem, particularly when it comes to the so-called dark data from social media. It is not simply the quantity of data that has changed, it is also the speed and the variety of formats with which it is delivered. This article sets out to look at big data and debunk some of the myths that surround it. It focuses on the role of data from social media in particular and highlights two common myths about big data. The first is that because a data set contains billions of items, traditional methodological issues no longer matter. The second is the belief that big data is both a complete and unbiased source of data upon which to base decisions.