What is a dataset?

Organizations today collect large amounts of data from various sources, including customer interactions, financial transactions, IoT devices and social media platforms.

To unlock the business value of all this data, it must often be organized into datasets: organized collections that make information accessible for analysis and application.

Different types of datasets store data in various ways. For instance, structured datasets often arrange data points in tables with defined rows and columns. Unstructured datasets can contain varied formats such as text files, images and audio.

While not all datasets involve structured data, they always have some general structure to them, whether defined schemas or loosely organized syntax in semistructured data formats such as JSON or XML.

Examples of datasets include:

• Customer service datasets tracking support interactions and resolutions.
• Manufacturing datasets monitoring equipment performance metrics.
• Sales datasets analyzing transaction patterns and consumer behavior.
• Marketing datasets measuring campaign effectiveness and engagement.

Organizations often use and maintain multiple datasets to support various business initiatives, including data analysis and business intelligence (BI).

Big data, in particular, relies on massive, complex datasets to deliver value. When properly collected, managed and analyzed using big data analytics, these datasets can help uncover new insights and enable data-driven decision-making.

In recent years, the rise of artificial intelligence (AI) and machine learning have further increased the focus on datasets. Organizations need extensive, well-organized training data to develop accurate machine learning models and refine predictive algorithms.

According to Gartner, 61% of organizations report having to evolve or rethink their data and analytics operating model because of the impact of AI technologies.¹

Though the term "dataset" is often used broadly, certain qualities determine whether a collection of data constitutes a dataset. Generally, datasets have 3 fundamental characteristics: variables, schemas and metadata.

• Variables represent the specific attributes or characteristics being studied within the dataset. For example, in a sales dataset, variables might include product ID, price and purchase date. Variables often serve as inputs for machine learning algorithms and statistical analysis.

• Schemas define a dataset’s structure, including the relationships and syntax between its variables. For example, a tabular dataset’s schema might outline the dataset’s formats and column headers, such as "date," "amount" and "category." A JSON schema might describe nested data structures such as customer profiles with attributes such as "name," "email" and an array of "order history" objects.

• Metadata or data about data, provides essential context about the dataset, including details about its origin, purpose and usage guidelines. This information helps ensure that datasets remain interpretable and integrate effectively with other systems.

Not all collections of data qualify as datasets. Random accumulations of unrelated data points typically don't constitute a dataset without some proper organization and structure to enable meaningful analysis.

Similarly, while application programming interfaces (APIs), databases and spreadsheets can interact with or contain datasets, they are not necessarily datasets themselves.

APIs allow applications to communicate with each other, which sometimes involves accessing and exchanging datasets. Databases and spreadsheets are containers for information, which can include datasets.

Organizations generally work with 3 main types of datasets, typically classified based on the type of data they handle:

• Structured datasets
• Unstructured datasets
• Semistructured datasets

Organizations often use multiple types of datasets in combination to support comprehensive data analytics strategies. For example, a retail business might analyze structured sales data alongside unstructured customer reviews and semistructured web analytics to get better insights into customer behavior and preferences.

Structured datasets organize information in predefined formats, typically tables with clearly defined rows and columns. These datasets are foundational to many critical business processes, such as customer relationship management (CRM) and inventory management.

Because structured datasets follow consistent schemas, they enable fast querying and reliable analysis. This makes them ideal for business intelligence tools and reporting systems that require precise, quantifiable data.

Common examples of structured datasets include:

• Financial records organized in Excel spreadsheets with defined fields for dates, amounts and categories.
• Customer databases with standardized formats for contact information and purchase history.
• Inventory systems tracking product quantities, locations and movement.
• Sensor data streams providing uniform metrics for equipment monitoring and predictive maintenance.

Unstructured datasets contain information that doesn't conform to traditional data models or rigid schemas. While these datasets require more sophisticated processing tools, they often contain rich insights that structured data formats cannot capture.

Organizations rely on unstructured datasets to power artificial intelligence and machine learning models. These datasets provide the diverse, real-world data needed to train AI models and develop more advanced analytics capabilities.

Common examples of unstructured datasets include:

• Text documents, such as emails, reports and web pages.
• Images and videos used to train machine learning models.
• Audio recordings from real-world applications.
• Chat logs and customer service transcripts.

Semistructured datasets bridge the gap between structured and unstructured data. While they don't follow rigid schemas, they incorporate defined syntax or markers to help organize information in flexible yet parseable formats.

This hybrid approach makes semistructured datasets valuable for modern data integration projects and applications that need to handle diverse data types while maintaining some organizational structure.

Common examples of semistructured datasets include: 

• JSON, HTML and XML files used in web applications and APIs.
• Log files containing both formatted fields and free-form text.
• Public datasets combining multiple data formats for broader accessibility.

Organizations collect data from multiple sources to build datasets that support various business initiatives. Data sources can directly determine both the quality and utility of datasets.

Some common data sources include:

• Data repositories
• Databases
• Application programming interfaces (APIs)
• Public data platforms

Data repositories are centralized stores of data. Proprietary data repositories often house sensitive or business-critical data such as customer records, financial transactions or operational metrics that provide competitive advantages.

Other data repositories are publicly available. For example, a platform such as GitHub hosts open source datasets alongside code. Researchers and organizations can use these public datasets to collaborate openly on machine learning models and data science projects.

Databases are digital data repositories optimized for securely storing and easily retrieving data as needed.

A database can contain a single dataset or multiple datasets. Users can quickly extract relevant data points by running database queries that use specialized languages such as structured query language (SQL).

APIs connect software applications so they can communicate. Data consumers can use APIs to capture data in real time from connected sources, such as web services and digital platforms, and funnel it to other apps and repositories for use.

Data scientists often build automated data collection pipelines by using languages such as Python, which offers robust libraries for API integration and data processing. For example, a retail analytics system might use these automated pipelines to continuously gather customer purchase data and inventory levels from e-commerce stores and inventory management systems.

Sites such as Data.gov and city-level open data initiatives such as New York City Open Data provide free access to datasets that include healthcare, transportation and environmental metrics. Researchers can use these datasets to study everything from transportation patterns to public health trends.

From powering artificial intelligence to enabling data-driven insights, datasets are foundational to several key business and technological initiatives.

Some of the most common applications of datasets include:

• Artificial intelligence (AI) and machine learning (ML)
• Data analysis and insights
• Business intelligence (BI)

Artificial intelligence (AI) has the potential to be a critical differentiator for many organizations.

According to the IBM Institute for Business Value, 72% of top-performing CEOs believe that their competitive advantage depends on having the most advanced generative AI (gen AI). These cutting-edge AI systems rely on vast datasets, both labeled and unlabeled to train models effectively.

With comprehensive training data, organizations can develop AI systems that perform complex tasks such as:

• Natural language processing (NLP): NLP models rely on English and multilingual datasets to grasp human language and power applications such as large language models (LLMs), chatbots, translation services and text analysis tools. For example, a customer service chatbot can use NLP to analyze datasets of past support conversations to learn how to respond to common questions.

• Computer vision: Using labeled image datasets, AI can learn to recognize objects, faces and visual patterns. Computer vision helps drive innovation in autonomous vehicles, medical imaging analysis and more. For example, AI systems in healthcare can analyze datasets of medical scans to detect early signs of disease with high accuracy.

• Predictive analytics: Predictive analytics relies on structured datasets to train models to forecast real-world outcomes, such as housing prices and consumer demand. These regression models analyze historical data patterns to make accurate predictions, such as analyzing years of sales data to predict seasonal demand and optimize inventory levels.

• Research: AI systems can process vast research datasets to uncover new insights and accelerate innovation. For example, pharmaceutical companies can use AI to analyze molecular datasets and identify promising new drug candidates more quickly than traditional methods.

Data scientists and analysts use datasets to extract valuable insights and drive discovery across disciplines. As organizations collect more data than ever, data analysis has become crucial for testing hypotheses, identifying trends and uncovering relationships that inform strategic decisions.

Some common ways datasets aid data analysis include:

• Pattern recognition: Advanced analysis of large aggregates of datasets can reveal hidden trends, correlations and anomalies that organizations can use to identify opportunities and mitigate risks. For instance, retail companies might uncover purchasing trends during holiday seasons by analyzing transaction data.

• Data visualization: Visualization tools transform complex datasets into clear and actionable insights by using charts, graphs and dashboards to make data more accessible. For example, a company might use interactive dashboards to display trends in sales and revenue, helping executives quickly grasp performance metrics and make informed decisions.

• Statistical analysis: Using rigorous statistical methods, data scientists can transform raw datasets into quantifiable insights that help measure significance and validate findings. For example, financial analysts might calculate key metrics from datasets to assess market performance.

• Hypothesis testing: Data scientists can use experimental datasets to validate theories and evaluate potential solutions, providing evidence-based support for business and research decisions. For example, a pharmaceutical company might analyze clinical trial datasets to determine the efficacy of a new drug.

Organizations use business intelligence (BI) to uncover insights in datasets and drive real-time decision-making.

BI tools can help analyze various types of data to identify trends, monitor performance and uncover new opportunities. Some applications include:

• Real-time monitoring: With metrics datasets and key performance indicators (KPIs), organizations can get continuous visibility into operational efficiency and system performance. For example, logistics companies use real-time monitoring during peak holiday seasons to track delivery times and quickly address delays.

• Customer behavior analysis: Transaction and engagement datasets can help reveal purchasing patterns and customer preferences. Organizations can then use these insights to develop targeted marketing strategies and improve customer experiences across touchpoints.

• Time series analysis: With the help of sequential and historical datasets, organizations can better track performance trends and patterns over time. For example, energy providers analyze time series data to predict and prepare for peak electricity demand, improving grid reliability and customer service.

• Supply chain optimization: Integrated datasets can help organizations streamline logistics and supplier management. For instance, retailers can analyze inventory levels, shipping data and supplier performance metrics to optimize restocking schedules and reduce transportation costs.

Handling large and complex datasets for any initiative can introduce several challenges and considerations. Some of the most salient include:

• Data quality: Maintaining data integrity and quality in datasets is critical. Otherwise, incomplete or inaccurate data can lead to misleading results. For instance, a new dataset with inconsistent formats across columns can disrupt workflows and skew analysis. Validation techniques such as standardizing formats and removing duplicates can help ensure accuracy and consistency as datasets scale.

• Interoperability and data integration: Integrating datasets from different sources or formats can present challenges, such as merging CSV files with JSON data. Creating a unified schema or standardizing data formats can help address these challenges and align data structures to help ensure system compatibility.

• Ethics and bias: Datasets containing personally identifiable information (PII) or biased data raise ethical and privacy concerns. For example, AI models trained on biased datasets can result in discriminatory outcomes, such as unfair hiring practices. Evaluating training data for biases and implementing privacy measures such as data anonymization can help mitigate potential issues.

• Dataset management: Growing data volumes and expanding use cases make dataset management increasingly complex. Prioritizing strong processes for dataset creation, maintenance and governance can help ensure data quality and accessibility while maintaining compliance with data privacy laws and regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).