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What is Data?

Data

In general, data is a distinct piece of information that is gathered and translated for some purpose. If data is not formatted in a specific way, it does not valuable to computers or humans. Data can be available in terms of different forms, such as bits and bytes stored in electronic memory, numbers or text on pieces of paper, or facts stored in a person's mind. Since the invention of computers, people have used the word data to mean computer information, and this information is transmitted or stored. There are different kinds of data; such are as follows:

  • Sound
  • Video
  • Single character
  • Number (integer or floating-point)
  • Picture
  • Boolean (true or false)
  • Text (string)

In a computer's storage, data is stored in the form of a series of binary digits (bits) that contain the value 1 or 0. The information can be in terms of pictures, text documents, software programs, audio or video clips, or other kinds of data. The computer data may be stored in files and folders on the computer's storage, and processed by the computer's CPU, which utilizes logical operations to generate output (new data) form input data.

As the data is stored on the computer in binary form (zero or one), which can be processed, created, saved, and stored digitally. This allows data to be sent from one computer to another with the help of various media devices or a network connection. Furthermore, if you use data multiple times, it does not deteriorate over time or lose quality.

Examples of computer data

Types of Data

There are two types of data that are given below:

1. Qualitative Data: Qualitative data is information that represents some characteristics or attributes. It depicts descriptions that cannot be counted, measured, or easily expressed with the help of numbers. It can be collected from audio, text, and pictures. It is shared via data visualization tools, such as concept maps, clouds, infographics, timelines, and databases. For instance, collecting data on attributes such as honesty, intelligence, creativity, wisdom, and cleanliness about students of any class would be considered as a sample of qualitative data.

Typically, it has two types: ethnographic data and interpretive data. The collection of data for understanding how a group assigns context for an event, it is known as ethnographic data. The data, which is collected to understand the experience and feelings of an individual's personal about the event, it is known as interpretive data.

Methods of collecting qualitative data

Qualitative data is widely collected by asking open-ended questions, or through direct or indirect observation. Below is given common methodologies of collecting the Qualitative data:

  • Interviews
  • Focus groups
  • Case studies
  • Cultural, or ethnographic, research
  • Pulling from existing records
  • Participant observation
  • Open-ended survey questions
  • Fieldwork

Qualitative data analysis

Qualitative data can be analyzed through being either deductive or inductive approach. In the deductive technique, the analyst starts with a question and evaluate data subjectively in terms of the question. In the inductive technique, he or she simply evaluates the data to look for patterns as in this approach; the analyst has no agenda. Frequently, the inductive process is also known as grounded theory. Generally, an inductive technique takes more time as compared to the deductive technique.

Qualitative analysis tools

Qualitative data analysis depends on the analog and digital tools to organize, systematize, and analyze non-numeric data.

  • SWOT analysis: It is a framework that means strengths, weaknesses, opportunities, and threats analysis. It is used to identify and analyze the internal and external factors that can have an effect on the activity of a place, person, project, or product. The tool is beneficial to offer a snapshot to understand the qualitative dynamics that able to affect success.
  • Porter's five forces: It is a framework that is used to improve the SWOT analysis. It is developed by Harvard professor Michael E. Porter, which improves SWOT analysis with the help of identifying and analyzing the internal and external factors that able to effect success.

Furthermore, QDAS (qualitative data analysis software) helps to collect and analyze qualitative data in a short time. It includes features such as coding for sentiment analysis and text interpretation, transcription analysis, and recursive abstraction.

Pros and cons of qualitative data

The methods content and observational help qualitative data researchers to collect the correct data to the actual experience and also help to avoid the Hawthorne effect. Including the qualitative data in reporting helps to add color to the story with the help of carrying a generalized solution into a less abstract view through real examples from actual people.

To collect and analyze qualitative data can be very time-consuming. Therefore, the researchers of qualitative data use sampling in their analysis. It can be difficult to scale the result out to discover when small samples of data are used.

The analysts can use numerical calculations and mathematical formulas to analyze the quantifiable data, and it can be put directly into a database. Before the qualitative data statistically examined for patterns or meaning, it must be classified by descriptive parameters, such as physical or traits characteristics.

Although the analysts can easily analyze the quantitative data through any software tool like spreadsheets, the analysis of qualitative data depends on the researcher's how they have skills and experience, which helps to create parameters from a small sampling, and larger data set can be examined.

2. Quantitative Data: These types of data can be measured but not simply observed. The data can be numerically represented and used for statistical analysis and mathematical calculations. For example, these mathematical derivations can be used in real-life decisions. Also, the number of students participate in different games from a class; the mathematical calculation gives an estimate of how many students are playing in which sport.

This data is any quantifiable information that is used to answer questions such as How much?" "How often?" "How many?". These data can be conveniently evaluated by using mathematical techniques and also can be verified. Usually, quantitative data is collected for statistical analysis sent across to a particular section of a population with the help of surveys, questionnaires, or polls. Furthermore, quantitative data helps to measure several parameters controllable as it includes mathematical derivations.

Types of Quantitative Data

There are various types of quantitative data; such are as follows:

  • Measurement of physical objects: It is commonly used to calculate the measurement of any physical thing, for instance, assigned each cubicle to the newly joined employees in any organization is carefully measured by the HR executive.
  • Counter: It is used to count equally with entities. For example, the calculation of a particular application of how many people have downloaded it from the App Store.
  • Sensory calculation: It is a mechanism to sense naturally the measured parameters that help to create a constant source of information. For example, electromagnetic information is converted to a string of numerical data through a digital camera.
  • Quantification of qualitative entities: It helps to identify numbers to qualitative information. For example, you are asking to share the likelihood of recommendation on a scale of 0-10 with respondents of an online survey.
  • Projection of data: It can be used for future projection of data with the help of mathematical analysis tools and algorithms. For instance, a marketer, after launching a new product with a thorough analysis, predicts growth in production.

The methods of collection the quantitative data

The main two types of quantitative data collection methods are given below:

  1. Surveys
    Surveys were traditionally conducted with the help of paper-based methods and have gradually evolved into online mediums. Collecting the closed-ended questions form a major part of these surveys is more appropriate in the collection of quantitative data. The survey contains answer options for a particular question. Also, surveys are unified to collect feedback from an audience. Surveys are classified into different category on the basis of the time involved in completing surveys:
    • Longitudinal Studies: In this, a market researcher conducts surveys from a specific time period to another as it is a type of observational research. When the primary objective is to collect and analyze a pattern in data, this survey is often implemented.
    • Cross-sectional studies: In this, a market researcher conducts surveys at a particular time period. It helps to understand a particular subject from the sample at a certain time period by implementing a questionnaire.
    There are some principles given below to administer a survey to collect quantitative data:
    • Use of Different Question Types: Closed-ended questions have to be used in a survey to collect quantitative data. These questions can be a combination of several types of questions as well as multiple-choice questions like rating scale questions, semantic differential scale questions, and more. It helps to collect data, which can be understood and analyzed.
    • Fundamental Levels of Measurement: Collection of the quantitative data, four measurement scales, ordinal, nominal, interval, and ratio scales, are fundamental for creating a multiple-choice question in a survey. These four fundamentals are most important as no multiple-choice questions can be created without the fundamentals.
    • Survey Distribution and Survey Data Collection: To collect quantitative data, it is also the other important principle of the survey process. There are various ways of survey distribution for collecting data, some common methods are Email, SMS survey, QR code, Embed survey in a website, QuestionPro app, etc.
  2. One-on-one Interviews
    It was also a traditional method to collect quantitative data. Although it was conducted face-to-face, it has been moved to telephonic and online platforms. A marketer can collect extensive data from the participants with the help of interviews. Quantitative interviews are extremely and play an important role in collecting information. There are three important sections that help to gather quantitative data through interviews. These major sections are given below:
    • Face-to-Face Interviews: In addition to the already asked survey questions, an interviewer can prepare a list of important interview questions. Thus, interviewers will be capable of providing complete details about the topic under discussion. Also, an interviewer will get help to collect more details about the topic by managing to bond with the interviewee on a personal level, through which the responses also improve.
    • Computer-Assisted Personal Interview: In this method, the interviewers are able to enter the collected data directly into the computer or any other similar device. It is also called a one-on-one interview technique, which technique helps to reduce the processing time and provides benefits to interviewers as they do not require to carry a hardcopy of questionnaires and only enter the answers on the laptop.
    • Online/Telephonic Interviews: Although, telephone-based interviews are not a modern technique. These types of interviews have also moved to online mediums like Zoom or Skype, which provides the option to online interviews over the network. Online interview is beneficial that helps to overcome the issue of distance between interviewer and interviewee and save their time.
    • However, the interview is only a phone call in case of telephonic interviews.

Analysis methods of quantitative data

Although the collection of data is a crucial part of the research process, it also needs to analyze for making it understandable. So, there are several methods to analyze quantitative data that have collected in surveys. These methods are given below:

  • Cross-tabulation: It is the most preferred and widely used method for quantitative data analysis. To evaluate an effective result between different data-sets in the research study, it uses a basic tabular form. It contains data that have some connection with each other.
  • Trend analysis: It provides the option to check out the quantitative data if it has been gathered over a long period of time. It also helps to gather feedback about data changes over time.
  • MaxDiff analysis: It helps to gauss the customer preferences for purchase and determine what rank of parameters is higher as compared to others in this process. This method is also known as the "best-worst" method as it is time-consuming. Furthermore, this method can be used interchangeably, and it is much easier to implement.
  • Conjoint analysis: It is most similar to the MaxDiff analysis method that helps to analyze parameters to make a better decision. This method has the ability to gather and analyze advanced metrics that offer the parameters that rank the most important, including in-depth insight into purchasing decisions.
  • Gap analysis: It is another type of quantitative data analysis method that uses a side-by-side matrix to describe data, which provides a way to measure the difference between actual performance and expected performance. The data analysis by this method helps to describe the things that need to be complete this gap and also helps to measure gaps in performance.
  • SWOT analysis: It is a framework that means strengths, weaknesses, opportunities, and threats analysis. It has the ability to identify SWOT of an organization or product or service. Also, it helps to create effective business strategies and offers a complete picture of the competition.
  • TURF analysis: It evaluates the total market reach of a product or service or a mix of both, which stands for total unduplicated reach and frequency analysis. This method is helpful in understanding the avenues and the frequency in any organization.
  • Text analysis: In this method, intelligent tools work on easily understandable data. They make more quantify or fashion qualitative and open-ended data of this data. This method is helpful in the case when the collected data is unstructured and needs to convert into a structural way that makes it understandable.

Examples of Quantitative Data

Some examples of quantitative data are given below that can help to easily understand which types of data are known as quantitative data.

  • I updated my cellphone six times in a quarter.
  • My uncle lost 20 kg last year.
  • The latest mobile application is downloaded by 83 people.
  • My son grew up by 2 inches last year.
  • 600 employees attended the meeting.
  • 44% of people like online shopping rather than going to the mall.

Advantages of Quantitative Data

Conduct in-depth research: It is highly possible that the research will be detailed, as quantitative data can be statistically analyzed.

Minimum bias: There are many examples in research if personal bias is involved, it generates incorrect results. The numerical nature of quantitative data reduces the personal bias that helps to lead correct data.

Disadvantages of Quantitative Data

Some of the disadvantages of quantitative data are as follows:

Depends on question types: Collection of the quantitative data, the result is dependent on the types of questions. While collecting quantitative data, the researcher's objective of research and knowledge of questions are most important.

Restricted information: On the basis of the collected data, it can be more difficult for researchers to make decisions as quantitative data is not descriptive.

Differences between Quantitative and Qualitative Data

Quantitative Data Qualitative Data
The data that deals with quantities, numbers, or values known as quantitative data. The data that deals with the quality known as qualitative data.
These data are measurable and associated with numbers. Generally, these data are not measurable and associated with details.
This data is implemented on numerical data. It is implemented when data can be separated into well-defined groups.
These types of collected data can be analyzed statistically. These types of collected data can be observed and not evaluated.
This type of data is generated by statistics and subsequently analyzed. Mostly, this type of data is gained through observation.
Its approach objective and research methodology conclusive. Its approach subjective and research methodology exploratory.
It contains structure data. It contains unstructured data.
The methods of collecting these data are quantitative surveys, interviews, experiments, etc. This data is collected through qualitative surveys, documental revision, and more.
It is more reliable and objective data as it uses statistics to add credence or credibility. It is less objective and reliable as compared to quantitative data.
It includes a wide number of representative samples. It includes a smaller number of non-representative samples.
This data is expressed in numerical form. Its nature is descriptive instead of numerical.
The measure quantities of quantitative data are size, length, amount, price, and more. It often uses adjectives and other descriptive words for referring data on color, texture, appearance, including other qualities.
Examples of quantitative data are weight, height, time, price, temperature, and more. Examples of qualitative data are appearance, beauty, flavors, scents, etc.

How a computer process data into information?

A computer uses following four functions to process data into information by using software and hardware.

  1. Input
    First, the data must receive input before a computer starts to process anything. For instance, to enter input into the computer have to type on the keyboard.
  2. Process
    A computer uses a program to process the data into information, which data has received through input. The program may organize, calculate, or manipulate the data to create understandable information.
  3. Output
    It is displayed as output to the user after the data is processed into information. For example, when you use the Windows Calculator, the program displays the information on your monitor screen.
  4. Storage
    Finally, the created information is stored on the computer for future retrieval. It uses storage media like hard disk, floppy disk, etc.

What is the difference between data and information?

Parameters Data Information
Description There are two variables qualitative and quantitative that helps to develop ideas or conclusions. It is a collection of data that contains valuable meaning and news.
Format It is in the form of letters, numbers, or a collection of characters. It is in the form of Ideas and inferences.
Represented in The data can be represented in a graph, tabular data, structured data tree, and more. It can be represented based on the given data, language, ideas, etc.
Feature The data alone doesn't have any valuable meaning. It is raw and a single unit. It is a collection of data and a product that has a logical meaning.
Interrelation It is related to the collected information. It is related to the information that is processed.
Meaning It includes row data that does not have any specific purpose. It does have logical meaning, which has assigned by interpreting data.
Contains It is unprocessed raw factors. It is processed in a meaningful way.
Dependence The data does not depend on information. A piece of information depends on Data.
Support for Decision making As it does not have any specific purpose, hence cannot be used for decision making. It provides useful information, hence widely used for decision making.
Measuring unit The data is measured in bytes and bits. The information is measured in meaningful units such as quantity, time, and more.
Knowledge level Data is low-level knowledge. Information is the second level of knowledge.
Characteristic It cannot be sold to the public as it is the property of an organization. Information can be sold to the public.
Usefulness The data may not be useful as it is collected by the researcher. Information is easily available to the researcher for use; hence it is valuable and useful.

How the data can be organized into information.

Example of Data

In the below example, the data is a collection of random words and numbers, separated by commas.

Example of Information

In the above information example, the data has organized, formatted, and interpreted that have a clear meaning. It contains the address information of a person.


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