A dataset is a single collection of data records which are connected. It usually includes values, statistics, or images that were collected for a unified purpose. This data can be extracted and used by research teams, data analysts and other stakeholders to analyze correlations, gain insights, create predictive models, and otherwise draw meaningful conclusions about a given matter.
Datasets are used in many industries including healthcare, retail, energy, government, finance, and education. Even streaming services rely on datasets to know which movies to recommend to viewers.
Some other examples include:
Different types of datasets are used to report on different types of analysis.
Numerical: A numerical dataset contains numerical values that can be used for statistical analyses. Examples include census data, election polls, sales figures, financial records, etc.
Categorical: Data involving distinct categories or labels. For example: gender, industry type, or geographic region.
Bivariate: Contains pairs of variables that can be plotted for comparison and analysis. Examples include career choices vs. education, consumer spending vs. age, etc.
Multivariate: Contains three or more related types of data. This method is used to understand complex patterns and trends. For example, a manufacturing firm can use multivariate data to examine the effect of different production inputs on the quality of the end-product.
Correlation: Datasets that analyze the degree of relationship between two or more different variables to help predict outcomes. Correlations can be either positive, negative, or zero.
As open data efforts expand globally, quality datasets are becoming increasingly available in open or public domains. Various datasets are freely accessible, from government institutions like the United States Census Bureau, universities, and private companies. From data on air pollution levels to population trends—these public datasets are being used to inform business decisions, create new solutions, and even improve public policy.
Advancements in big data, particularly with the advent of cloud computing and data virtualization hold particular promise. Across industries, businesses are leaning into complex growing datasets to gain insights into consumer behavior, market movement, financial data, and more. Emerging technologies like AI and Machine Learning are being used to make sense of this data, making the insights derived from big data more powerful and paramount in today’s growing business world.
