big data

USAGE SUMMARY

"big data" is correct and usable in written English.
It is used to refer to large data sets that can be used to uncover patterns and correlations. For example, "We used big data to identify trends in consumer behavior."

✓ Grammatically correct

News & Media

Academia

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Big data.

The Guardian - Tech

Surely not big data?

The Guardian

WHAT is "big data"?

The New York Times

It's big data".

The New York Times - Tech

You mentioned "big data".

The New York Times

Democratise big data.

Independent

Big data can help.

The New York Times

Big data has arrived.

Big Data Research

Big Data is everywhere.

Columbia University

"Move Over, Big Data!

Massachusetts Institute of Technology

"CPS Meets Big Data".

University of California, Berkeley

Expert writing Tips

Best practice

Always consider the ethical implications of using "big data", particularly concerning privacy and potential biases in the data.

Common error

Avoid claiming that "big data" automatically provides definitive answers. Insights derived from data analysis should be interpreted critically and validated with other sources.

Linguistic Context

Big data functions primarily as a noun phrase, representing a concept. It refers to datasets that are too large or complex to be processed by traditional data processing application software. According to Ludwig, it's a correct and usable phrase.

Expression frequency: Very common

Frequent in

News & Media

38%

Academia

30%

Science

20%

Less common in

Formal & Business

12%

Ludwig's WRAP-UP

In summary, "big data" is a commonly used noun phrase referring to extremely large and complex datasets. Ludwig AI confirms its grammatical correctness and usability. Its applications span across diverse fields including news, academia, and science, making it a versatile term. While "big data" is often used to describe opportunities, it's important to use the term with caution, avoiding overgeneralizations about the insights that can be derived from it. Alternatives include "large datasets" and "data analytics", depending on the specific context.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

large datasets

Focuses on the quantity of data involved, omitting the implication of complexity or processing challenges.

massive data

Highlights the sheer size of the data, similar to "large datasets" but with a stronger emphasis on scale.

extensive data

Emphasizes the breadth and scope of the data collected.

data analytics

Shifts the focus from the data itself to the analysis processes applied to it.

data mining

Highlights the process of extracting valuable insights from large data sets.

data science

Refers to the interdisciplinary field concerned with the collection, analysis, and interpretation of data.

predictive analytics

Focuses on using data to forecast future trends and behaviors.

information overload

Captures the challenge of dealing with an overwhelming amount of data, often hindering effective decision-making.

data deluge

Conveys the sense of being overwhelmed by a large and rapidly increasing volume of data.

bulk data

Implies a large volume of undifferentiated data.

FAQs

How is "big data" used in a sentence?

"Big data" is a noun phrase referring to extremely large data sets that are analyzed computationally to reveal patterns, trends, and associations. For example: "The company used "big data" to improve its marketing strategies."

What are some alternatives to using the phrase "big data"?

Depending on the context, you could use phrases such as "large datasets", "massive data", or "data analytics".

Is "big data" singular or plural?

"Big data" is generally treated as a singular noun, especially when referring to the concept or field. You would say "Big data is changing the world" rather than "Big data are changing the world."

What's the difference between "big data" and "data science"?

"Big data" refers to the large volumes of data, while "data science" is the interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured.