Make better decisions with these four types of data
Data has always been important, but it is now increasingly used to guide decisions. There are many benefits to data-driven decision-making, including improved customer experiences, greater efficiencies, and improved product development processes.
Key types of data
There are two key types of data that can be used for decision making: qualitative, and quantitative.
Qualitative data
Qualitative data is data that is descriptive and cannot be measured in numbers. There are two categories within this: nominal and ordinal.
Nominal data is data that is used to label variables that do not have any numeric value. Examples include gender, nationality, religion, and marital status.
Ordinal data involves ranking in a non-numeric sense. Examples include level of education, satisfaction ratings, and military ranks.
Qualitative data often highlights reasons and motivations for particular beliefs and behaviours.
Quantitative data
Quantitative data is numeric and can be measured using statistical analysis. It is helpful for establishing patterns, trends, and correlations. There are two categories: discrete and continuous.
Discrete data is data that is countable, finite, non-continuous, and categorical. Values are whole numbers. Examples include the number of pages in a book, the number of children in a classroom, or the types of animals in a zoo.
Continuous data can take any value within a given range. Examples of this type of data include age, weight, height, income, and temperature.
Data analysis specialists
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Data quality
Data quality is extremely important, as the decisions that are guided by data can only be as good as the data itself. High-quality data should be accurate, complete, unique, consistent, timely, and valid.
Data for decisions
Many organisations tend to use only one type of data, but this can mean important information is not included and lead to poor decisions. It is important to incorporate as much data as possible into the decision-making process.