What is data?

There are many types of data, and understanding those types helps us see how information is created and used.

Structured data: Structured data fits neatly into tables or databases. It includes numbers in a spreadsheet, customer records, inventory lists, or financial transactions. Because it follows a consistent format, structured data is predictable, organized, and straightforward to analyze.

Semi-structured data: Semi-structured data has some organization but doesn’t follow a strict model. Emails, XML and JSON files, event logs, and sensor readings sit in this middle space. The structure helps guide interpretation, but the format remains flexible enough to capture a wide range of information.

Unstructured data: Unstructured data doesn’t fit into rows and columns. Text documents, images, videos, audio files, social media posts, and scanned PDFs are common examples. While it can be harder to process, unstructured data often carries rich context that reveals meaning once analyzed at scale.

Qualitative data: Qualitative data captures descriptions, observations, and experiences. It helps us understand the “why” behind behaviors through interviews, open-ended survey responses, research notes, or support conversations. This type of data adds depth and nuance to decision-making.

Quantitative data: Quantitative data focuses on numbers, counts, and measurements. Sales totals, website traffic, test scores, and temperature readings all fall into this category. Its consistency supports comparisons, calculations, and statistical analysis.

Together, these types of data create the foundation for nearly every digital process today, such as powering search engines and supporting AI-assisted systems that help us solve complex problems.

Data is created almost everywhere. It’s captured by people, devices, and systems as we go about daily life—streaming music, ordering food, or checking the weather. Each interaction generates information that can be collected, stored, and used to improve how things work.

Organizations gather data from many sources, including:

• Applications: Everyday apps capture usage patterns, preferences, and interactions that help improve experiences and services.
• Sensors and connected devices: Smart thermostats, wearables, industrial equipment, and other Internet of Things (IoT) devices generate continuous streams of real-time data.
• Transactions: Purchases, account activity, reservations, and financial exchanges create detailed records that reflect how people and systems operate.
• Surveys and forms: Responses collected from customers, employees, or research participants provide structured input and direct feedback.
• Logs and system activity: Websites, servers, and software systems collect event logs that show performance, errors, and operational trends.

Once collected, this information flows into systems built to organize and store it:

• Databases hold structured data in rows and columns for fast and reliable access.
• Data warehouses bring together information from across an organization to support reporting and analysis.
• Data lakes store massive volumes of raw, varied data—structured and unstructured—that teams can explore in many ways.
• Data lakehouses combine the features of a data lake and a data warehouse, allowing for the storage and analysis of both structured and unstructured data.

Storing data securely and efficiently makes it possible to find meaning in data, share knowledge, and create value. Whether kept on local servers or in the cloud, data storage makes it possible to turn individual moments of information into lasting insight.

Data touches nearly every part of modern life, shaping how people, organizations, and communities make decisions. Across industries, it turns information into action and helps us respond to change with confidence.

• Business: Data supports better customer experiences and more efficient operations. It highlights buying patterns, shows where processes slow down, and helps teams plan with clarity.
• Healthcare: Data strengthens diagnostics and care. It supports early detection, helps personalize treatment, and gives clinicians a clearer view of patient progress.
• Education: Data helps teachers and students understand learning patterns. It shows where support might be needed and helps schools design programs that meet evolving needs.
• Environment: Data tracks climate patterns, energy use, weather conditions, and natural resources. This insight helps communities plan responsibly and protect ecosystems.
• Manufacturing: Data drives predictive maintenance by monitoring equipment performance. It helps teams address issues before they disrupt production or impact safety.
• Retail: Data reveals customer behavior and preferences. It guides product planning, store layout decisions, digital experiences, and personalized recommendations.
• Finance: Data allows real-time fraud detection and supports risk reduction. It helps financial institutions identify and respond to unusual activity.

These examples show how data brings clarity to complex systems. Whether it’s optimizing a supply chain or spotting early signs of severe weather, data helps us understand what’s happening and act with purpose.

A thoughtful approach to data helps organizations build trust, improve insight quality, and create long-term value. These best practices support clearer decision-making and help teams get more from the information they already have.

• Start with clear goals. Define what you want to understand or improve before collecting or analyzing data. Clear intent helps guide the entire process.
• Prioritize data quality. Consistent, accurate, and well-maintained information leads to stronger insights.
• Use consistent standards. Common formats, naming conventions, and governance practices make data easier to organize, share, and interpret across teams.
• Break down silos. Bringing information together across systems creates a more complete view of what’s happening and supports better collaboration.
• Protect sensitive information. Strong security and privacy practices help safeguard data and maintain trust with customers, partners, and employees.
• Make data easy to find and interpret. Clear documentation, intuitive tools, and shared dashboards help people work with confidence.
• Plan for scale. As data grows, storage and processing needs expand. Modern tools and cloud-based platforms help handle increasing complexity.

Together, these practices create a foundation for responsible, effective data use—one that grows with your organization and supports future innovation.

Data continues to grow in importance as AI-assisted systems, generative AI, machine learning models, agentic tools, and connected devices become part of everyday life. These technologies rely on clear, well-managed information to recognize patterns, support decisions, and help people work with more confidence.

Organizations today generate a large and varied set of data. Increasingly, the focus has shifted toward making that information easier to access, understand, and use responsibly. Data democratization, with the assistance of generative AI or agentic tools, helps more people work with information directly, without needing deep technical expertise. Synthetic data is opening opportunities to train and test models when real data is limited or sensitive. And privacy-enhancing technologies are strengthening security and governance so teams can collaborate while protecting sensitive information.

When data is collected thoughtfully, managed securely, and used with purpose, data becomes a foundation for innovation that supports stronger decisions today and prepares organizations for what comes next.