Big data refers to any data collection that is too large for traditional methods to process or analyze.
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Big data refers to data collections that are extremely large, complex, and fast-growing — so large, in fact, that traditional data processing software cannot manage them. These collections may contain both structured and unstructured data. While there is no widely accepted, technically precise definition of "big data," the term is commonly used for massive data collections that expand rapidly.
Digital storage capacity has increased exponentially since the development of the first computers. Data can be saved at a massive scale, and retrieved within seconds. Cloud computing has made data storage virtually unlimited. These developments have together made the advent of big data possible. Additionally, with widespread Internet usage, data from user activity, web-hosted content, and Internet of Things (IoT) devices can be logged and analyzed in order to make predictions or train advanced artificial intelligence (AI) models.
Big data can come from publicly available sources, or it can be proprietary. Examples of big data include:
Common uses for big data include:
Even though there is no firm agreement on what constitutes "big data" exactly, the term is usually applied to a data collection that meets the general criteria of volume, velocity, and variety:
Together, these attributes are known as "the three V's."
AI refers to the ability of computers to perform cognitive tasks, such as generating text or creating recommendations. In some ways, big data and AI have a symbiotic relationship:
Massive data sets make effective AI possible, enabling more accurate and comprehensive training for advanced algorithms. Large curated and labeled data sets can be used to train machine learning models; deep learning models are able to process raw unlabeled data, but require correspondingly more compute power.
For example, the large language model (LLM) ChatGPT was trained on millions of documents. The inputs it receives from users help further train it to produce human-sounding responses. As another example, social media platforms use machine learning algorithms to curate content for their users. With millions of users viewing and liking posts, they have a lot of data on what people want to see, and can use that data to curate a news feed or "For You" page based on user behavior.
Conversely, AI's fast processing and ability to make associations means it can be used to analyze huge data sets that no human or traditional data querying software could process on their own. Streaming providers like Netflix use proprietary algorithms based on past viewing behavior in order to make predictions about what kinds of shows or movies viewers will most enjoy.
Information overload: Just as an overly cluttered room makes it difficult to find the item one needs, such large databases can, ironically, make it difficult to find usable and relevant data.
Data analysis: Typically, the more data one has, the more accurate conclusions one can draw. But drawing conclusions from massive data sets can be a challenge, since traditional software struggles to process such large amounts (and big data vastly exceeds unaided human capacity for analysis).
Data retrieval: Retrieving data can be expensive, especially if the data is stored in the cloud. Object storage is low-maintenance and nearly unlimited, making it ideal for big data sets. But object storage providers often charge egress fees for retrieving the stored data.
Ensuring data accuracy: Inaccurate or untrustworthy data causes predictive models and machine learning algorithms trained on that data to produce incorrect results. However, checking large, fast-growing volumes of data for accuracy is difficult to do in real-time.
Privacy and regulatory concerns: Big data collections may contain data that regulatory frameworks like the General Data Protection Regulation (GDPR) consider to be personal data. Even if a data set does not currently contain such data, new frameworks may expand the definition of personal information so that already-stored data falls under it. An organization may not be aware that their data sets contain this data — but if they do, then they are subject to fines and penalties if their data is accessed or used improperly. Additionally, if a database contains personal information, the database owner faces increased liability in case of a data breach.
Cloudflare for AI is a suite of products and features to help developers build on AI anywhere. Cloudflare R2 is object storage with no egress fees to enable developers to easily store training data. Vectorize translates data into embeddings for training and refining machine learning models. And Cloudflare offers a global network of NVIDIA GPUs for running generative AI tasks. Learn about all of Cloudflare's solutions for AI development.