The Rise of Big Data
Although term of Big Data has been used since the early 1990s, it is not known exactly who first used the term. John R. Mashey popularized the term during his years at Silicon Graphics, and many people dedicate that term to him.
The unique ability to reason enabled humankind to collect and process information, and civilization was built on this concept. In the third century BC, historians believed that the library of Alexandria contained all of human knowledge. Today, the amount of knowledge held by a single person is 320 times the size of the library of Alexandria.
In the last two decades, the volume and speed at which knowledge is produced has changed beyond human comprehension. The total amount of data in the world in 2013 was 4.4 zettabytes. It was announced by PWC that this reached 44 zettabytes in 2019. Considering that a zettabyte is a trillion gigabytes, that’s a awesome record.
Previously, only a quarter of all stored information in the world was digital. While we have a vast knowledge of paper and film, the amount of digital information is growing so fast that less than two percent of data today is non-digital.
After the digital revolution, the impact of big data is felt everywhere from business to science, from government to art. With information such as what we like, what we buy, when we buy, who we interact with, companies now conduct marketing activities, politicians organize election campaigns, and doctors treat diseases before they occur.
Why Is Big Data So Important?
Big data is not just about digitizing existing information. It’s about turning more of our lives into data in real time. That’s exactly what social media apps like Linkedin, Twitter, Facebook, Snapchat, and Instagram do: the real-time datafication to our lives!
But ultimately, in order for knowledge to be transformed into wisdom, data must be processed and presented in an understandable way and in the right dimensions for the right application context. The digital revolution and the age of information have created new business areas in this context and increased the importance of some not-so-popular professions. In recent years, companies have started to employ large numbers of Data Scientists or Analysts.
A Simple Example
Let’s think about it, let’s say that a person loves the color yellow but hates the color blue and feels thinner with vertical stripes, usually buys clothes for himself by shopping about every 26 days, and we know that 27 days have passed since his last purchase.
In addition to the big data term, these information, which are not the size of an atom, make us think that our chances of sales are very high when we present this person with a yellow vertical striped shirt advertisement today. In companies where dozens of data scientists and analysts are employed and where serious investments are made in data mining, this and much more information is collected and processed for millions of people, and advertisements specific to each are displayed in the light of this information. Try it, you will see it works.
A Real Example from the Big Data Industry
To take a different and real-life example, this is the big data-based microtargeting method that resulted in Trump’s election in the 2016 presidential election in the United States. Trump’s greatest achievement has been to follow a data-driven campaign process. The right advertising technique to the right target always works. Within the scope of Trump’s campaign strategy, microtargeting with Facebook ads was achieved through tactics such as seeing certain people with certain frequency in banner ads on websites, adding posts on interesting categories on Facebook. The advertisements generally created a positive perception of Trump but negative perception of Clinton. The budget allocated and spent on Facebook ads was three times more than Clinton spent on digital ads. This very well planned and implemented microtargeting method played a big role in Trump’s election.
Another example is from Netflix. This very popular media company says that the recommendation algorithm they use on the platform, which works based on customer data, influences 80% of all content watched, so they better influence and retain customers. Netflix saves a billion dollars a year thanks to this operation.
In the information age, zetabytes of data could hardly be expected to be useless. But with obvious consequences like the above, the big data industry has grown tremendously in just a few years. From $169 billion in 2018 to $274 billion in 2022.
Big data is not a fad or a trending hashtag that will die out soon. Since you started reading this article, over 10 million photos have been shared, over 200 million emails sent, millions of dollars in e-commerce transactions, 50 hours of YouTube videos uploaded, and millions of Google searches done.
Data technologies are becoming an industry standard in finance, commerce, insurance, healthcare and distribution. Welcoming big data technologies and solutions seems to be the key to optimization and forward growth.
The truth is that data-driven decisions tend to be better decisions. Leaders, they will either adopt this reality or be replaced by others. In every industry, companies that figure out how to combine domain expertise with data science will stand out from their competitors. At least the data tells us this is the surest bet.
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