Different Types of Data
Anyone who has been paying attention to business developments in the past decade will tell you that modern-day knowledge, power, and success are all derived from data. This is true not only for industry giants but also for small-to-medium businesses (SMBs).
Whether you own or run a brick-and-mortar store, service business, manufacturing facility, small startup, or e-commerce company, data is the key to efficiency, profitability, and sustainable growth. More specifically, data’s application in Business Intelligence (BI).
BI is an all-in-one practice of utilizing and monetizing data for business through things like data mining, warehousing, data analytics, and Artificial Intelligence (AI) applications like machine learning.
Today, we will go down to the basics of data and discuss different types of data to give you a comprehensive understanding of what types of data can be used in BI.
But first…
What Is Data in BI?
Data is simply a set of values of subjects with respect to quantitative or qualitative variables. All data can be categorized into quantitative or qualitative data. The former includes numerical and categorical data, whereas the latter includes descriptions, maps, or diagrams.
Quantitative data is objective and deductive, whereas qualitative data is subjective, inductive, and helps explain the “why” and “how” behind the numbers. Data is sourced and collected as raw and unorganized facts typically processed through the data mining efforts of BI solutions.
Until data is organized, it is usually random, basic, and mostly useless. Data mining processes organize and structure data to be presented in a relevant context for businesses, making it extremely useful for data analytics.
This processed, organized data is known as information. This information can be further parsed through BI solutions and efficiently utilized in multiple business applications like production, marketing, sales, decision-making, risk assessment, customer trends, market predictions, and much more.
Different Types of Data
When we talk about different types of data, we refer to the different types of information or processed data. While there are many types of processed data, the main ones include:
● Primary Data
Primary data is an authentic and unique form of data, which businesses collect directly from a source, according to their business requirements. This source can be online, within the company, or from the markets. Businesses collect primary data for different purposes.
For example, data gathered for market research, from sales and marketing about customer behaviour, ascertaining customer demands, determining employee job satisfaction, and learning about the quality of service provided by an employee, are forms of primary data.
● Secondary Data
Data previously collected, documented, and used as primary data for another purpose is considered secondary. Often, this is data collected from the database or data warehouse of another business or big data company that sells data.
Secondary data is collected or organized by another business, person, or researcher for some other particular purpose but is being used by a different business. They could use it for the same purpose or a different purpose.
Gathering data or information for market trends, competitor research, or consumer behaviour are prime examples of secondary data. Similarly, historical data and other types of data from sources like online databases, articles, and social media platforms are also categorized as secondary data.
● Cross-Sectional Data
Cross-sectional data is a different type of data collected by observing multiple subjects. These subjects could include customers, competitors, or markets at a specific point of time (typically the same time), or without any regard to time differences.
It is organized data about the subjects at a single point in time or a single space point. Cross-sectional data is relatively limited because it cannot describe changes over time. This means businesses cannot derive cause and effect relationships of variables from this data.
Still, it is useful for reference purposes and certain data analytics purposes of Business Intelligence.
● Categorical Data
Categorical data is widely used in BI and its applications. They are variables that represent types of data that may be divided into different groups. For example, when going in-depth about customer attributes, categorical variables could include categories like age, sex, and literacy.
This type of data can be useful for targeted marketing and market research. Simply put, data that cannot be measured numerically is known as categorical data, which makes it different from most types of data because it is qualitative in nature.
Categorical data is often referred to as attributes because it consists of observations on a single characteristic. Good examples of categorical data in BI include income bracket, education level, age group, and employment status.
● Spatial Data
Spatial data is also known as geospatial data. This type of data is simply geographic information that identifies the location of boundaries and features on the ground, such as water bodies, communities, natural or constructed features, and more.
Spatial data can be mapped and presented as geographic locations on Earth, so it is usually stored as coordinates and topology. Businesses typically use this data type to identify demands, behaviours, and trends in physical locations or certain market regions.
Spatial data is often categorized as vector or scalar data. Each of these gives particular information regarding geographical or spatial locations.
This type of information is often organized and stored with a variable as metadata. It can also be used by businesses to locate end-user devices. However, this may be unethical or illegal, especially without user permissions.
Conclusion
The mentioned different types of data are just some key types, and there are many more, which we can discuss another time. For now, you hopefully have a better understanding of the different types of quantitative and qualitative data that can be used in Business Intelligence and how BI uses them for business growth.
BI solutions like data mining and data analytics are key for modern business growth, regardless of the business’ size, type, market, or location. BI and Artificial Intelligence can help your business use the mentioned different types of data in many beneficial ways.
If you want to learn more about the different types of data, or if you want to deploy BI solutions powered by AI technology for your business, please visit our website today.