In recent years many entrepreneurs, governments, and data scientists have been marveling over big data. Big data is large volume of traditional and digital data that can be analyzed to optimize business strategies, cut costs, and multiply revenue. Although numerous companies now have access to these voluminous data sets, many are unable to efficiently use the information to their own advantage. This article uncovers three main issues companies are currently facing with big data while offering solutions. The first issue is being that since companies have so much data at the tip of their fingers it is often difficult to precisely find the solution to a question, or put in another way it’s similar to “trying to find a needle in a haystack”. The second hurdle is that many corporations are implementing large-scale company-wide initiatives before they have had enough time to crunch the numbers and really understand what information they are interpreting. It is essential for businesses to work on smaller projects in order to practice interpreting these large sets of data and then build their way up to larger undertakings. In brief, the final roadblock the article mentions is that companies are having difficulty acquiring relevant data for their business and thus do not have anything to analyze.
While this article does in fact shine light on some of the complications businesses are dealing with, I believe it neglects to mention one vital fundamental component. Since big data is a combination of both structured and unstructured data, it presents a problematic obstacle that many analysts have never encountered before. Unstructured data is the most common form of data and it is also the most difficult to process because it is a compilation of unorganized information, rendering it foreign to most traditional databases. This is the underlying problem for many of these large companies, the traditional techniques and skills that statisticians have used for years are not sufficient enough to analyze the current data. In order to add value and make use of big data, statisticians must update their methods and algorithms to fulfill the potential opportunity of big data. However until statisticians modernize their processes, companies run the risk of making statistical errors on a very large scale, thus leading to detrimental business decisions.