Earlier people had opinions, now the credibility of what you say has to be supported by data. You have to start with it, continually measure it and work around it for big results. In other words, data is the raw material, it is the processor and it is the output as well. The dependency on it so much that you would lag behind in the race if you don’t invest in it. It has opened a whole new world of science and means of employment. Whether you are a data scientist, marketer, businessman, data analyst, researcher, or you are in any other profession, you need to play or experiment with raw or structured data. That’s why it’s important to handle and store it properly, without any error.

Let’s understand what it entails. There are two types of data: Qualitative and Quantitative data, which are further classified into four types of data: nominal, ordinal, discrete, and continuous.

Qualitative Data is data that can’t be measured or counted in the form of numbers. These data consist of audio, images, symbols, or text. It is further classified into two parts-

Nominal data- It is used to label variables without any order or quantitative value and you can’t do any numerical tasks. Examples-

· Colour of hair (Blonde, red, Brown, Black, etc.)

· Nationality (Indian, German, American)

Ordinal Data – It has natural ordering where a number is present in some kind of order by their position on the scale. These data are used for observations like customer satisfaction, happiness, etc., but you can’t do any arithmetic tasks on them. Examples-

· When companies ask for feedback, experience, or satisfaction on a scale of 1 to 10

· Letter grades in the exam (A, B, C, D, etc.)

Quantitative Data- It can be expressed in numerical values, which makes it countable and includes statistical data analysis. It answers the questions like, “how much,” “how many,” and “how often.” It is classified into two parts:

Discrete Data- It contains the values that fall under integers or whole numbers and can’t be broken into decimal or fraction values. These are represented mainly by a bar graph, number line, or frequency table. Examples-

· Total numbers of students present in a class

· Cost of a cell phone

Continuous Data- It can take any value within a range. It stores the fractional numbers to record different types of data such as temperature, height, width, time, speed, etc. Examples:

· Speed of a vehicle

· “Time-taken” to finish the work

· Wi-Fi Frequency

· Market share price

In essence, data is a collection of facts and figures to be used for a specific purpose such as a survey or analysis. When arranged in a systematic and organized form, it is called information. It is valuable because you can use it to make informed decisions, both personal and professional, The more relevant, high-quality data you have, the more likely you are to make good choices when it comes to marketing, sales, customer service, product development and many other areas of your business.

The most significant growth in the adoption of data analytics is seen with the advent of Big Data, Data Warehouses, and the Cloud. If stored and used properly, it has proved to be a very effective tool in the turnaround of products, brand image and the business, in general.

Data is information and information is power.

5 views0 comments