8 Types of Data That Inform Insights and Relationships

Let's start with quantitative data: information you can measure or count. Think of numerical data like shoe sizes, customer satisfaction ratings or the number of website visitors.

Quantitative value is great for statistical analysis because it supports descriptive statistics, calculating summary statistics and detecting relationships. Data analysts often use bar charts, line charts and frequency tables to visualize continuous data or discrete data for easier interpretation.

Quantitative data forms the backbone of analytics, enabling performance measurement and precise predictive modeling.

While quantitative data deals with numbers, qualitative data focuses on qualities and descriptions. It answers “what” or “why,” not “how many.” Customer behavior, thematic analysis and consumer preferences often fall into this category.

Analysts categorize data from interviews or surveys into themes, making it useful for enhancing customer experience or improving business processes. Qualitative and quantitative data together give a complete view when analyzing customer data or historical data.

Continuous data can take any value within a range. Imagine measuring the height of every tree in a forest; you could always be more precise.

In data science, continuous data supports advanced statistical methods like predictive modeling and machine learning algorithms. Analysts often visualize continuous data with line charts or histograms to identify patterns in a data set or interpret data distributions.

Discrete data has separate values that cannot be split further—like the number of products sold in a day.

Nominal and ordinal data often appear in customer datasets, and analysts typically use frequency tables and bar charts to summarize these categorical values.

Combining discrete and continuous data provides a more comprehensive view of information, enabling analysts to derive more meaningful insights.

Nominal data is a type of categorical data where values are labels rather than measurements. Examples include hair color, product type or country of origin. It's valuable for analyzing customer data and categorizing data for business analysts.

Privacy and security concerns are essential here because nominal and ordinal data often contain sensitive personal information.

Ordinal data is categorical but ranked. Think of customer satisfaction ratings from “very dissatisfied” to “very satisfied.”

While ordinal data shows order, the gaps between ranks aren’t consistent. This makes central tendency measurements and certain statistical methods possible but limits others.

Data classification that includes ordinal discrete and continuous types can drive more meaningful insights.

Interval data is numerical with equal spacing between values—like temperature readings—but lacks a true zero. This means you can calculate summary statistics and apply various statistical analysis techniques, but ratio comparisons won’t work.

Interval data plays a big role in implementing data driven initiatives and improving business processes through valuable insights.

Ratio data is like interval data but has a true zero, allowing full mathematical operations. Examples include weight, height or revenue.

Ratio data can be analyzed with the full range of statistical techniques—from basic descriptive measures to advanced predictive modeling—thanks to its true zero and complete quantitative properties.

Business analysts rely on such data to identify patterns and trends, interpret data distributions, and feed predictive analytics models that help enhance the customer experience.

We created this article in conjunction with AI technology, then made sure it was fact-checked and edited by a HowStuffWorks editor.