A dataset is an organized collection of related data assembled for a particular purpose, such as analysis, research, reporting, or training an AI model. A dataset might contain rows and columns, text, images, video, audio, or a combination of different data types. Some datasets contain raw data, while others contain data that has been cleaned, validated, labeled, or transformed. What makes something a dataset is that the data has been collected and organized as a defined collection, not that every record has already been processed.
Structured vs. unstructured data
Datasets are usually built from one of three kinds of data, and the distinction matters for how the data gets collected, cleaned, and used.
Structured data lives in predefined fields, prices, SKUs, star ratings, dates, the kind of information that fits cleanly into rows and columns. It is the easiest type to work with, since its shape is known in advance.
Unstructured data has no predefined schema: raw text, images, video, and audio. Per the types of web data covered in What Is Web Data?, this is where most of the value in modern AI training actually sits. Image and video data alone already make up over 41 percent of the AI training dataset market by volume, per research cited in Large-Scale Data Collection: Complete Guide, even though it takes more work to collect and shape than structured data does.
Semi-structured data sits between the two, formats like JSON, HTML, and XML that have some organizational structure, tags, key-value pairs, but not the rigid fixed schema of a spreadsheet.
| Structured | Semi-structured | Unstructured | |
|---|---|---|---|
| Example | 50,000 laptop listings with price, SKU, and rating, pulled nightly from five marketplaces | A JSON checkout response from an ecommerce platform, with nested but labeled order and shipping fields | 2 million customer reviews and their accompanying photos, collected across a dozen retail sites for sentiment analysis |
| Shape known in advance | Yes, fully | Partially, tags exist but content varies | No |
| Common source | Database exports, structured page fields | API responses, scraped HTML | Review text, images, video |
What makes a dataset different from raw data or a database?
These three terms get used loosely, but they describe genuinely different things.
Raw data is whatever came out of collection before anything has been done to it, unprocessed, uncleaned, not necessarily even structured yet.
A dataset is raw data after it has been cleaned, validated, deduplicated, and organized for a specific purpose. It is static in the sense that a given version of it represents a fixed snapshot, not something that changes underneath you while you are using it.
A database is a live, queryable system built to store and retrieve data on an ongoing basis, and it is expected to change over time as records are added, updated, or removed. A dataset can be exported from a database, but the two are not interchangeable: a database is infrastructure, a dataset is a specific, fixed output.
| Raw Data | Dataset | Database | |
|---|---|---|---|
| State | Data in its original or unprocessed form | A structured, cleaned collection of related data | A system for storing, managing, and retrieving data |
| In one workflow | 80,000 scraped HTML pages from a retailer’s nightly crawl | Those pages cleaned and structured into a versioned CSV of 80,000 product rows | Those rows loaded into a system where new products get added and prices update continuously |
Raw data describes the state of data. A dataset describes a collection of data. A database describes a system used to store and manage data.
How does a dataset get built?
A dataset is created by bringing related data together into a defined collection. The data might be collected from websites, generated by an application, exported from a database, obtained through an API, recorded by sensors, created manually, or obtained from an existing provider. Depending on its purpose, the collected data may then be cleaned, validated, deduplicated, labeled, transformed, or organized into a particular structure.
For web data specifically, that general process follows a consistent chain. A web crawler discovers the pages that need to be visited. A web scraper accesses those pages and identifies the fields on them. Extraction pulls those fields out into raw form. Processing and preparation, covered in detail in What Is Web Data Extraction?, clean, normalize, and validate the result. What comes out the other end, stored and organized, is the dataset. This whole sequence, and how it applies specifically to web data, is covered in full in What Is Web Data?.
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Where do datasets come from?
A team can create a dataset itself or obtain an existing dataset from another source. Which approach makes sense depends on how specific, current, large, and difficult to collect the required data is.
Open datasets are made publicly available for research, education, benchmarking, or other permitted uses. They are commonly found on platforms like Kaggle and Hugging Face, though they are often limited in size, licensing, or how current they are.
Data marketplaces are platforms where organizations can discover datasets offered by different data providers. Marketplaces like AWS Data Exchange, Datarade, and Snowflake Marketplace aggregate offerings from many providers in one place, letting a team browse and license existing data rather than collect it themselves.
First-party data is data an organization creates or collects through its own products, operations, customers, research, or systems.
Custom data collection is data collected specifically for a particular requirement when an existing dataset does not provide what is needed, the crawl, scrape, extract, process chain described above, run against whatever source actually matters for the job.
The tradeoffs between these options, and when each one actually makes sense, are covered in depth in Large-Scale Data Collection: Complete Guide.
What is a dataset used for?
Datasets are used anywhere a defined collection of information needs to be analyzed, compared, shared, or used by another system. Common uses include:
- Data analysis and reporting: analyzing trends, relationships, and changes.
- AI and machine learning: training, evaluating, or fine-tuning models.
- Scientific research: recording observations and testing hypotheses.
- Business intelligence: analyzing customers, markets, products, or operations.
- Competitive and ecommerce intelligence: comparing prices, products, availability, and market activity.
- Application development: providing data used by software, APIs, search systems, and other applications.
Within a web data context specifically, the most common use is training and fine-tuning AI models, a use case covered in depth in AI Data Collection at Scale: The Definitive Guide, where the quality and structure of the underlying dataset directly shapes how well a model performs. Datasets also feed competitive analysis, tracking pricing, catalog, and positioning data over time, a use case covered in Ecommerce Intelligence: The Complete Guide.
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FAQs
- What is a dataset? A dataset is a structured, cleaned collection of data, typically the finished output of a collection and processing pipeline, used for analysis, reporting, or AI training.
- What is the difference between structured and unstructured data? Structured data fits into predefined fields, like a spreadsheet row. Unstructured data, text, images, video, has no predefined schema and generally requires more work to collect and prepare for use.
- What is a data marketplace? A data marketplace is a platform where multiple providers list datasets available to license or purchase, letting a team browse and compare existing data rather than collecting it from scratch.
- Can a dataset be used for AI training? Yes, this is one of the most common uses of a dataset. See AI Data Collection at Scale: The Definitive Guide for how training data specifically differs from a general-purpose dataset.
- Is a dataset the same as a database? No. A dataset is a fixed, structured snapshot of data built for a specific purpose. A database is a live, ongoing system built to store and retrieve data on an ongoing basis. A dataset can be exported from a database, but the two serve different roles.
