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The types of data you need to know for analytics

Read on to find out more about the types of data you can use for analytics to help you derive valuable information for your business decision-making.

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Analytics refers to evaluating data to derive valuable information and insights. In the age of big data and technology, most businesses are leveraging analytics to make smart business decisions and gain a competitive advantage. Data analytics can help establish the reason for an occurrence, predict future events and suggest an appropriate course of action in case of an event using past and present data.

The type of data determines the preprocessing strategy or analysis to ensure it provides the right insights or results. Read on to find out more about the types of data you can use for analytics to help you derive valuable information for your business decision-making.

Two Main Types of Data

Data comes in two main categories, qualitative and quantitative. Qualitative data, also known as categorical data, refers to the type of data you can’t count or measure. Instead, you can use words and texts to present and describe the data instead of numerical values.

Since you can’t measure qualitative data using numbers, you can observe it and divide it into categories. For example, you can explain gender as being male or female or present the characteristics of an item by stating the color and shape. Examples of qualitative data include color, nationality, and blood type.

Quantitative data, on the other hand, refers to data that you can express in numerical form. It usually answers the question” how much” or “how many” and provides information about an item’s quantity. You can measure, group, and rank quantitative data, present it graphically and perform statistical analysis on the data.

Examples of quantitative data include weight, price of a commodity, product ratings, and discounts. Note that quantitative data can take an infinite number of values, which you can further break down into fractions.

Types of Qualitative Data

Qualitative data comes in two types, nominal data, and ordinal data.

1. Nominal Data

Nominal data is helpful in labeling variables that have no numerical value.  You can only differentiate nominal data using their names. It is impossible to sort or perform any mathematical operations on the data since you can’t order or rank them. For example, you cannot say the color blue is greater than the color red, and changing the order of the values has no impact on the meaning.

Nominal scales with only two values, such as male or female, are known as dichotomous. Examples of nominal data include hair color like black or blonde, nationality such as American or Canadian, and marital status, which can be married, single, or divorced.

2. Ordinal Data

Ordinal data is similar to nominal data, although it can be ordered and placed into ranks. However, you can only observe ordinal data and not measure it. Although you can assign numerical values such as first, second, and third to the data, adjacent categories have no relative distances between them.

You can use ordinal data to gauge satisfaction and happiness. For example, you can ask your client to rate their satisfaction after using a product on a scale of 1 to 10. Since you can order ordinal data, you can also arrange it by comparing the categories such as greater or lesser and higher or lower. Note that you cannot perform mathematical operations on the data as they only show sequence.

Some examples of ordinal data include the time of day (morning, noon, or evening) and letter grades like A, B, and C. The difference between nominal and ordinal data is that you can only classify nominal data, whereas you can order and classify ordinal data.

Types of Quantitative Data

Below are the two types of quantitative data; discrete data and continuous data.

  • Discrete Data

Discrete data is data that you count as a whole. It is indivisible into parts and usually involves integers. An example of discrete data is the number of people in an event, which can only be a whole number such as 50 or 100. You cannot divide discrete data as there cannot be 50.5 people.

Simply put, discrete data can only assume specific values. Other examples of discrete data include the number of clients who bought a product, the number of times someone clicked on a link, and the number of cameras on a smartphone.

  • Continuous Data

Continuous data is data that you measure and divide into logical smaller units. While you can’t count it, you can measure the data into precise units, and variables take any value between two numbers. For example, you can measure distances in kilometers, meters, and centimeters.

To better understand continuous data, you should ask yourself whether you can reduce the measurement point by half and remain logical. Examples of continuous data include height, weight, distance, time as well as speed.

All types of data are essential in data analytics. With the right data analytics solutions, you can find out the facts behind your data and derive valuable insights for making informed business decisions.

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