If I ask what is the most valuable asset in the present age, the answer is nothing but ‘data’. For past few years, there have been an enormous and vast industries grown around the collection, analyzing and manipulation of data. Everything we do from waking up in the morning till we go to bed is being recorded by some application in one or the other way. Even with the revolution of wearable devices, our sleeps are not left unrecorded. Data collection companies try to record as much data as possible to understand the behavior of the consumers. There is a joke that amazon knows what you need better than you do. In this article, I will focus on the most basic and atomic object of this whole artificial intelligent industry; DATA.
So, data is everywhere. Now the concern is to make sense out of the raw data and that is information. Once we transform data into information, it opens the gates to extract the various facts out of that and make some fact based decisions for your organization. So the ultimate goal of the data is to empower the businesses to make fact based decisions to engage consumers.
Based on the value that data represents, it can be classified by a number of means.
Qualitative and Quantitative Data
Quantitative data is everything that can be measured and represented numerically. It represents some quantity that can be added, subtracted, multiplied, compared etc with any other value of the same type. This is the most important kind of data as it enables us to perform all the statistical analysis on that data. For ex. the population of a city, heights of students, salaries of employees, temperatures of last 3 days etc. all these are quantitative data as they all can be measured numerically.
Qualitative data as the name suggests gives the description of the data rather than actual numerical value. This kind of data helps more in observation rather than calculation. For ex. Rating of a movie( Good, Average, bad ), color of the top of all the visitors in the party, Gender of a person etc. all these data enables us to calculate some kind of pattern/behavior. For ex. gender of two persons can be added or subtracted, but we can decipher some other information from the gender of two persons.
Discrete and Continuous Data
We call our data is discrete when the data available is in steps. Range of discrete data is finite and few possible values are possible in that range.For ex. gears on the car, a car can have only few options for gears; it can range from 4 to 10 (although it can be out of that range but the point is to show that the range is finite with few possible values), gender of a person (Male/Female/Transgender/Unknown etc).
Continuous data can contain any value within a range. For ex. speed of a car, price of a motorcycle, salary of an employee etc. As it can be seen that there is no possible set of fixed values for these data, these can take any value; speed of a car can be 87.4 mph or 12.3333 mph or 34.2233 mph etc.
Measure Scale of data
Before discussing the measure scale, let’s focus on three points that are essential for measuring scale.
1) Order ( a set of data can be arranged in order )
2) Difference ( couple of data points can be differentiated numerically)
3) Absolute zero ( there is some possible value that shows the absence of any value)
Nominal Data: Discrete data which has no order, no difference and no absolute zero point is considered as nominal data. For ex. color of cars, gender of persons etc. These values can not be ordered. Suppose there are two cars; Red and Blue; we can not make any ordered set from these values. No difference can be calculated for two colors. There is no such value as no color of the car; there will be a color of the car. So these kind of data are nominal.
Ordinal Data: Discrete data which has no difference and no absolute zero but does have order. For ex. rating of a movie (Good, Average, Bad); Difference can’t be calculated for any two values. But we can arrange our data in an order from low to high (Bad, Average, Good) or high to low (Good, Average, Bad). So this kind of data is ordinal.
Interval Data: This is quantitative data. This data can be discrete or continuous and has the order and mechanism to calculate the difference between two data points. For ex. temperature of a city; We can calculate the difference between the temperature of last two days; But there is no such data point as absence of temperature; Zero degree is an actual data point not the absence of data.
Ratio Data: This is also quantitative data.This kind of data can be ordered, differentiated and even have an absolute zero value. This data has a definitive ration between each data and there is a point of origin which is considered as absolute zero point. For ex. Salary of person; Zero is the point of origin for this data. Another example is heights of students; height has again the origin point is zero; It can’t be negative, can be ordered and differentiated.
