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Data and information are interconnected. Data is a collection of raw particulars and statistics. The meaningful, ordered and processed form of data is called information. Each student’s test score is an example of data while. The average score of a class or school is an example of information.
What is Data in Simple Words?
Data is a collection of raw particulars and statistics that have not yet been processed to get their accurate meaning. data is represented by alphabets (A-Z, a-z), digits (0-9) or special characters (+,-,/,*,<,>,=). It may consist of facts, characters, signs, and pictures also.
Types and Uses of Data
With the advancement in technology, data is available now in the form of text, video, and audio. Mostly this type of data is in unstructured form.
Term the Big Data is also considered a type of data that is used to explore the data that is in the petabyte range or higher. Big Data is also manipulated as 5Vs: variety, volume, value, veracity, and velocity.
Due to the horrible situation around the globe due to Covid-19, web-based eCommerce has extended at a large scale, business models based on Big Data have been developed, and they consider them as the most important asset. The term big data provides huge benefits to businessmen as it reduced costs, enhanced efficiency enhances sales, and much more.
Qualitative data is data that describes the info object features without specifying quantities or sizes. There are three sub-types of qualitative data:
- Nominal Data Type
- Boolean Data Type
- Ordinal Data Type
Nominal Data Type
Nominal data or Nominal Attribute data types mean that they’re associated with names and their values are nominal values or symbols.
Examples include the names of things or people. The nominal data isn’t subject to ranking and may represent specific categories or classifications For example: during a company’s sales database, some fields can have attributes like (marital status), for which the possible values are:
And also (a profession that the possible values can be)
Thus, these values are nominal values, so these characteristics (marital status, profession) are called nominal attributes.
However, sometimes an attribute could also be nominal and therefore the values in it contain numbers, but they’re treated as nominal values. An example of this is often the (phone number) or (postal code) field. Numbers in these cases are nominal values because they can’t be added, subtracted, or mathematically compared with one another.
Boolean Data Type
Boolean data or data types with a logical attribute are nominal data also, but their values are limited to 2 values or two states only. And they are often expressed numerically using the binary numeration system of numbers with two values (0,1), where the worth (zero) expresses The absence or non-fulfillment of the characteristic and the value (1) for its realization.
Ordinal Data Type
Ordinal data or data types with an ordinal attribute are data that will take values that have a selected arrangement between them in which order is meaningful. But without attention or maybe without the necessity to know the particular difference between the successive values during this arrangement.
For example, during a nutriment restaurant, several options are often used for the dimensions of the beverage that’s chosen with the meal. They take the subsequent value (small, medium, large), and these values make a transparent order showing the sequence of size from smallest to largest despite.
That we may find one among the variables or features that determine the customer’s opinion of a specific product, so it takes one among the subsequent values: (very bad, bad, good, very good)
All data of the nominal, logical, and ordinal type are qualitative data, that is, they describe the features of the info object without specifying its quantities or sizes.
Quantitative data or numeric data or attributes of quantitative or numeric data are sorts of data with measurable values, and that are often expressed in whole or real numbers. It also can be within the sort of Period measurement.
- Ratio Measurement
- Interval Measurement
Interval Measurement Data
Interval measurement data, during which the values are divided into equal intervals, and therefore the values of those periods have a big order. These values are often positive, negative, or maybe zero. They are often compared with one another and therefore the difference between them can be calculated.
For example, the units of measurement wont to measure temperatures are measured using a Celsius scale on different days of the week. Where a selected measurement is often obtained a day, and these values are often arranged in descending or ascending order to seek out the most well-liked or coldest days.
Relative Measurement Data
Ratio-Scaled data are the kinds of knowledge with a numerical characteristic, during which the worth of zero may be a real value, and that they are often compared together. and They are often arranged and perform calculations on them, and statistical values are calculated for them like mean, mode, etc. Examples of Relative measurement data (product price, age, income)
For example, If we’ve two products whose prices are respectively (100) and (50), then we will say that the price of the primary product is twice the worth of the second product
Information is meaningful, ordered, and processed form of data. It is extra important than data because decisions are made by using it. Data is utilized as input and the information is the output of this processing. This information can be exercised again in some other processing.
Similarities Between Data and Information
Data and information are interconnected and closely related to each other. Furthermore, Information cannot be compiled without data. Data is an unsystematic, unorganized and unrelated entity. While information is systematized, organized, and understandable. Data is independent but the information is dependent.
Data Processing Life Cycle
The data processing life cycle is the set of steps necessary to switch data into useful information. The main purpose of this processing is to generate actionable information. Stages of the data processing life cycle are collection, preparation, input, processing, output, and storage.
Stages of Data Processing Life Cycle
- Collection: The first step of data processing focuses on the quality of the data. The data must be defined and accurate.
- Preparation: Manipulating data into a form that is suitable for further analysis and processing.
- Input: This step shows that data is together and given to the computer for processing.
- Process: In this step, the computer processes raw facts to produce information.
- Output: This step represents that the information is sent to the user as output.
- Storage: It is the last step of the data processing lifecycle. It includes the information that is stock up on the computer for future use. This step is optional.
Relationship Between Data and Information
Data and information are interconnected and closely related to each other. Furthermore, Information cannot be compiled without data. It will be meaningful if data is collected from the right resources. Data is the raw facts and figures while Information is a processed and meaningful form of that data.
Difference Between Data and Information
Data and information are interconnected. The main difference between data and information is, data usually consist of raw facts or figures that have not been arranged, analyzed, and processed while information is arranged, analyzed, and processed form of raw facts or figures. To learn more about the difference between data and information, just click on the below button.
For example, marks of a student in dissimilar subjects are the data. To compute the total marks, the marks of different subjects are exploiting as data, and total marks are the information. Now, to estimate the average marks of the students, this information will be also processed once more. In this processing, the information is used as data and average marks will be the information.
Examples of Data and Information
Examples of Data
- When students get admission, to colleges or universities, they have to fill out an admission form. The form consists of raw facts i.e student’s name, father’s name, and address, etc. The idea of collecting this data is, to sustain the records of the students throughout their study period.
- During the census, governments gather the data of all citizens of a country. The government stores this data permanently, to make use of it for diverse purposes at different times.
- Different organizations carry out surveys to identify the opinions of the people about their products. In these surveys, people also convey their ideas and opinions about diverse issues. The organizations use these ideas and opinions as data for the enhancement of their products also.
Examples of Information
- If we desire to find out, a list of all students who exist in Lahore, we will apply some processing on this data, this processing will provide us the desired list. This list is a form of processed data and will be called information.
- The data stored in a census is used to produce a different kind of information. For example, the government can also use it to discover the total number of graduates in the country or literacy rate in the country and also use the information in vital decisions to advance the literacy rates.
- An organization can use the view of the people as data. Then process it to produce information about its concern. For example, it can know how many people like or dislike its product. The organization can use this information for the perfection of its product.