Is Big Data Capable Of Predicting The Next Best-Selling Book And Author: An Analysis

The applications of data in various fields are becoming more relevant than ever. From sports to music, the applications of data and technology are finding new uses, applications, and results. If you have seen the Brad Pitt baseball movie, ‘Moneyball’, you would know exactly what I am talking about.

Many argue that the applications of big data cannot apply to creative fields like music or writing. However, experts who are getting big data analytics certification think otherwise. Books, people point out cannot be judged according to big data. In other words, whether a specific book will become successful is a creative function that is dependent on experience, expertise, and audience reception.

Not quite so! It appears that scientists and data practitioners have come up with a method to analyze and understand whether a book can become a best-seller or not.

How does the Big Data Algorithm work?

In the last few years, experts like Jodie Archer and Matthew Jockers have analyzed the last 30 years of New York Times bestsellers. They have then applied scientifically and tech-developed algorithms to look at various trends, patterns, and learnings to figure out what is common in all these books. The results are striking.

Bestseller-Ometer, the algorithm has data fed into it from more than 20000 books and novels. For publishers, this comes as a welcome advancement. They do not need to take a risk call on every manuscript that passes through their doors.

They can easily just run the software and figure out which novel is going to work and which is not going to sell. If you are looking at percentages of success, the makers are confident of 80%! That is a huge percentage to take into consideration at any given point in time.

Is this a Reality that is going to change the Publishing World forever?

To be honest, the application of the data is not the first in terms of the idea. This was first assessed and put into work in the jewelry industry. Inkitt, a German-based start-up was the first to look at the various inputs and reading perceptions of the readers and come up with an algorithm that assesses what is liked and what is not.

The algorithm takes into account, the following factors-

  • What is the rate of repetition of the pages that the readers go over?
  • How long do they dwell on a specific section?
  • Are there any thematic points that are considered more?
  • Where are the books purchased and read from?

Once all these inputs are fed into the software, the algorithm runs its course and gives out suggestions, and shows definite patterns.

The Human Factor- Can this be Completely Discounted?

Both Jockers and Archer feel that even though the algorithm is great, it is not 100% thanks to the human factor. You might ask what is this factor?

This factor is called the human intimacy factor. It refers to the emotional and intimate bond that forms between a book’s characters and the reader. Think about Harry Potter novels and how they have become a household name for everyone of this generation.

It also means that new genres, one that has still not been discovered are subject to the human emotional connection and chance. An algorithm can only assess data that is in front of it.

If there is something completely new and unique, the algorithm will not be able to give a cohesive reading of the same. In such cases, editors need to depend on their experience to understand whether the said work can be successful or not.

The Bottom Line:

Big data analytics is changing the creative and arts industry in a major way. As more research and developments take place, it is only a matter of time before we see more full-proof connections emerge in this industry.

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Ariana Smith

I enjoy writing and I write quality guest posts on topics of my interest and passion. I have been doing this since my college days. My special interests are in health, fitness, food and following the latest trends in these areas. I am an editor at Content Rally.

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