The Prospects and Risks of Big Data: The Advantages and Disadvantages

Every minute, worldwide, more than 204 million emails are sent. Every minute, Google records 2 million different queries on its search engine and Facebook, equally as many interactions on its platform. Every minute, $85,000 worth of orders are placed on Amazon.

Big data is a source of excitement as much as it is a source of worry and anxiety. What are the advantages and disadvantages of big data? What are the downsides to be mitigated in order to fully enjoy all its benefits? In this article, we’ll take a deep dive into big data and answer all these questions.

What’s Big Data: A Simple Definition

Who hasn’t heard of data today? Pieces of information, statistics, and text that we store on our computers and exchange over the internet. Now, more than ever, we’re producing more and more data, and this data has way more uses than you might first realize.

The development of the internet and social media is contributing to the explosion of data produced on the internet. New tools and algorithms are appearing to analyze this vast wealth of data.

Big data is the generic term used to refer to the algorithms, tools, and techniques used to understand and analyze the exceptional volume, the “big” volume of data produced on the Internet. Big data affects all fields: marketing, science, customer relations, transport, health, education, etc.

To fully understand the big data revolution, it is necessary to understand the stakes and risks. It is important to understand how it works to understand the advantages and disadvantages inherent to it. This is because big data, despite all its benefits, has been at the centre of one of the major issues of this century: confidentiality and privacy.

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Understand Big Data

Why are we no longer talking about data but have found a whole new term big data to distinguish this field? These five points below will help you distinguish big data from regular data:

  • Volume: big data only applies when there’s an exceptional volume of data.
  • Speed: big data applies when there’s reasonably fast data processing giving preliminary results often in real-time.
  • Variety: big data is varied data taking different forms. Thus, an image, a video, a tweet, and like are data. A simple trace left on a website after your visit, the cookies, etc. are also all data. Big data often involves analyzing more than one type of data in tandem.
  • Veracity: big data concerns itself heavily with the problem of the veracity of the data. Are they relevant, are they real?
  • Values: One of the drawbacks of big data is the problem of knowing what added value these data bring, what useful information can you extract from them? Sorting the data is essential. It is essential to carefully select the data to be analyzed and processed and select the right algorithm for the job.

Thankfully, there are excellent automated data collection algorithms that handle most of this for you, and you can employ them in your projects for maximum success.