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Hype
Hype
A hype occurs when something becomes extremely popular and everyone is talking about it. This happened when Big Data emerged around 2010. Newspapers, magazines, and research papers were full of it, and new businesses were built around the trend. Like most hypes, the Big Data wave eventually faded. It resurfaced briefly at the start of the AI hype, but today the Big Data hype is clearly over. AI has become a standalone phenomenon in the news, even though it still relies on Big Data in many cases.
Hype Is Created & Kept Alive
Hype Is Created & Kept Alive
A hype is usually sparked by the news and amplified by how the public responds to it. When everyone starts talking about a topic, it becomes a hype. Journalists continue producing follow‑up stories until a new topic takes over or there is simply nothing left to report. Alongside media coverage, people begin building businesses around the new trend, and public interest grows as more people want to learn about it. Eventually, however, the excitement fades as people lose interest or become tired of the subject.
Big Data Is Not Tech
Big Data Is Not Tech
Many people believe that Big Data is something only technical experts deal with, but this is not true. Technicians are just one part of the Big Data process and actually a relatively small part. Their main responsibility is storing the data and making it accessible. The real analysis is carried out by researchers and statisticians, or sometimes by software tools such as SPSS, R, or Excel. These tools can be used effectively as long as the person working with Big Data understands statistics and the project is not groundbreaking research or part of a large company with dedicated specialists for every stage of the research process.
What Was Hyped About Big Data?
What Was Hyped About Big Data?
White papers were published describing how Big Data could be used and what risks it carried. The term Big Data refers to a period in business and technology when the amount of consumer data grew explosively (Centric Consulting, 2024). Companies began storing all data simply because it was possible to store and measure it. Big Data was presented as the solution to every financial, medical, scientific, and social problem known to humankind. All it would take was a big pile of data and some way to process it all (Bellemare, 2023). Big Data did help solve certain issues, but as researchers tackled more complex problems, it became clear that data reliability and completeness were major obstacles that needed to be addressed first. As a result, Big Data shifted from being a grand promise to becoming the routine, (boring) everyday work of researchers, statisticians, journalists, medical professionals, marketers, and many others.
Big Data
Big Data
Big Data appears in archives, medical records, marketing, business, and many other places. Because these datasets are not stored in one central location, no one ever truly knows when the data is complete. New information may be discovered elsewhere and added later, while unreliable data may be removed. These characteristics are part of what define Big Data. In addition, Big Data is often described using the five V’s: Volume, Variety, Velocity, Value, and Veracity. Initially, Big Data was defined by only the first three, but over time two more characteristics were added to capture its full complexity.
Source
Source
Bellemare, A. (2023) Whatever Happened to Big Data? https://www.confluent.io/blog/what-happened-to-big-data/, bezocht op 24 juni 2026.
Capella, G.M. & Hidajattoellah, D. (2018) Big Data cursus Laudius.
Centric Consulting (2024) “Big Data” Gone Missing: What the Heck Happened to This Viral Business Trend? https://medium.com/centric-tech-views/big-data-gone-missing-what-the-heck-happened-to-this-viral-business-trend-f557671b881d, bezocht op 24 juni 2026.
Zee, B. van der & Zee, W. van der (2020) Succes met Big Data. Van Duren Media, Meppel.
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