Data analysis is an important aspect of running a successful company and Google analytics is one of the popular data analytics tools out there to track your website data. When used efficiently, Google analytics allows business owners to gain a better knowledge of their past performance and make better decisions about their future actions.
There are several Google analytics data types you will come across when using Google Analytics. Whether you are an analyst or a typical user, knowing these data types can help you make better analytics choices and understand what can and cannot be done with different data sets.
In this article, we will look at the various data types in Google analytics.
Google Analytics Data Types
An object’s data type determines what kinds of values it can have and what kinds of operations it can perform. The majority of data is collected and classified by Google Analytics. Before you start analyzing, it’s critical to go over the data and see what information is available. Also, keep in mind the specific question(s) you want to answer with the data being collected.
Data Types Used in Google Analytics
The data types in Google Analytics can be categorized into quantitative and qualitative data.
Quantitative data is any data whose value is expressed in numbers or counts. Each data set has a unique numerical value. Quantitative data, also known as numerical data, describe numeric variables in more depth. An example of numeric data is the number of conversions made in the last month, number of sessions, page views, bounces, and revenue.
Quantitative data can be classified into two; continuous or discrete. Continuous data is information that can take any value within a specified range, such as time, money, or temperature. Discrete data is data that takes on whole number values and can be counted like units sold or pageviews.
Quantitative Data Types/Format
- An Integer
Integer values are written as a series of digits with a plus or – sign before them. The integer values are automatically converted to decimal values when used where a decimal value is expected.
A decimal, float, or double number is a number with a decimal. The terminology varies a little depending on the programming language. But it’s referred to as a float in Google analytics. The term ‘float’ comes from the term ‘floating point,’ which refers to the ability to move the decimal point around.
Integer or real constants with name suffix are used for currency constants. The currency data type has a decimal data format. Because the decimal data format calculates in base 10, it avoids the round-off errors that can occur with binary calculations.
In Google Analytics, percentages are a separate data type. It’s a number that has a percent sign attached to it and is stored as a decimal divided by 100. It’s represented by a number divided by 100, so 100% is 1, 90% is 0.9, and so on.
- Time Data Type
Time data type is data collected at regular intervals over a long time. Days, hours, minutes, seconds, optional fractions of a second, and an optional time zone are all part of the TIME data type. An example is a number of daily sessions on your website over three months.
Qualitative data is data that approximates and characterizes. This data type isn’t numerical; rather it’s observed and recorded. It’s categorized based on the attributes and properties of a thing or a phenomenon. Qualitative data is also known as categorical data.
Examples of qualitative data in Google analytics include channels, sources, gender, country, and device types, etc.
One of the most common types of qualitative data is a string. A string is made up of one or more characters that can be letters, numbers, or other symbols. A string can be compared to text.
The alphanumeric data in Google analytics is represented by a string. A string can contain a wide range of characters, all of which are treated as text, even if the characters are numbers and spaces.
Examples of Google analytics string data includes; Hour Index, Day Index, Minute Index, Month Index, Week Index, Month Index, Week Index, Month Index, Week Index, Year Index, Year Index, Year Index, Year Index, Year Index, Year Index.
Boolean Data Format
The Boolean data type can only have two values: True or false. True is typically represented by a 1 and False by a 0. The Boolean type is the most common outcome of conditional statements, which are used to control program workflow. For example, it can say; if a condition is true, do this; if it is false, do something else.
What Types Of Google Analytics Data Type Does Your Business Need?
Knowing the various Google analytics data types and formats will help you know the type of analytics that applies to your business and your overall data analytics strategy. We also recommend answering the following questions to determine the right data mix for your company:
- How far has data analytics progressed in my organization?
- How deep do I want to go into data analytics?
- Is it obvious what the solutions to my business data analytics issues are?
- What is the distance between my current data insights and the insights I require?
These answers will aid you in deciding on a data analytics strategy. The strategy should, in theory, allow for the gradual implementation of various analytics data types, starting with the most basic and progressing to the most complex. The next step is to design a Google data analytics solution with the best technology stack and a detailed roadmap to successfully implement and launch it.