We live in an increasingly digitized, data-driven society. The term “data” is often used, but have you ever stopped to ask what it really means? Sure, we can say “data is knowledge and information,” but there’s more to it than that.
This article explores the nature of data to help answer the question, “What is data?” We will also discuss what information is, the various uses and types of data, the data processing cycle, how data is analyzed, why it’s a wise idea to become a data scientist, and how a data science bootcamp can help you do so.
But since data is information, let’s step back and define information first.
What is Information?
We define information as organized or classified data containing meaningful user value. Information is also processed data used to make informed, actionable decisions. Processed data used in decision-making must meet specific criteria:
- Accuracy: The given information must be accurate
- Completeness: The information must be thorough and complete
- Timeliness: The information must be current and easily accessible whenever needed
So now that we’ve established a foundation, let’s build from there. What is data, then?
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What is Data?
Since the advent of computers, people have used the word “data” to describe transmitted and stored computer information. However, that’s not the only definition of data since there are many other types of data as well. So, what is data? Data can be numbers or letters written on solid media (e.g., paper) or bits and bytes stored inside the memories of electronic devices. It can even be just facts stored inside a person’s mind.
So, data is information like facts and numbers used to analyze things and make decisions, and computer data is information suitable for use by computers and related digital devices.
Now, what is a data type?
What is Data? Exploring Data Types
Data is typically broken down into two significant types, qualitative and quantitative, which are then split into four categories:
- Nominal
- Ordinal
- Discrete
- Continuous
Qualitative data, also called categorical data, is information that can’t be counted or measured numerically, such as images, audio, text, and symbols. This data type is sorted by category, hence the clever name.
Qualitative data is further broken down into nominal and ordinal data. Nominal data labels variables with no order or quantitative value, like marital status or hair color. Ordinal data has a number present in a given order, such as exam letter grades or ranking people’s finishing places in a competition.
Conversely, quantitative data can be expressed using numbers, which is why it’s often called “numerical data” and includes things like temperature, height, weight, and test scores.
Quantitative data gets broken down into discrete and continuous data. Discrete data features values that fall under whole numbers or integers and can’t be broken down further into fractions or decimal values, such as the number of people taking a class or phone numbers. Continuous data can be used in fractional numbers, such as a software app’s version number or a market share price.
And no discussion of data type is complete without mentioning big data, which is information rendered in petabytes or higher. Big Data is typically described using the 5 Vs: value, variety, volume, veracity and velocity.
Now that we’ve covered data types, let’s briefly look at what data is used for.
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The Uses of Data
In broad terms, data has five common uses:
- Making informed decisions
- Solving problems
- Gaining a better understanding of how an organization is performing and where it stands
- Process improvement
- Better understanding of customer/client behavior
What is Data? All About the Data Processing Cycle
Data processing is the reordering or restructuring of data by humans or machines to improve its usefulness and enhance the value of a specific purpose or function. Typical data processing consists of three basic steps: input, processing, and output. When used together, these steps make up the data processing cycle.
- Input. The input data is prepared for processing in a usable, convenient form that depends on the machine to conduct the processing.
- Processing. The input data’s form is converted into something more practical and valuable. For instance, timecard information calculates paychecks.
- Output. Finally, the processing results are brought together as output data, and its final form depends on what it’s being used for. So, using the previous example, the output data becomes the employees’ actual paychecks.
Data analysts and data scientists typically handle data processing chores.
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How Do You Analyze Data?
Data analysis depends on which data type we use, either qualitative or quantitative.
Data analysis and research using subjective information works better than numerical information because the data consists of words, pictures and objects, painting a fuller, more detailed picture than mere numbers can. However, extracting knowledge from this entangled data is a challenge, so it’s typically used for exploratory research and data analysis and usually relies on finding patterns in the qualitative data.
Although there are several ways to discover printed data patterns, word-based strategies are the most widely used global method for data research and analysis. Notably, the data analysis process used in qualitative research is overwhelmingly manual, with specialists typically reading accessible information and finding repetitive or often-utilized words.
For quantitative statistical research, employing descriptive analysis regularly gives solid numbers. However, the analysis is usually insufficient to justify the numbers. As a result, organizations that want to work in today’s extremely competitive world must have a robust capacity to examine complex research information, derive notable knowledge, and adjust to constantly changing market needs.
Quantitative data must first be prepared using these steps:
- Data Validation
- Data Editing
- Data Coding
Why Should You Become a Data Scientist?
As we said at the start, we live in an increasingly digitized, data-heavy world. Here are some solid reasons why you should consider a career as a data scientist.
- Security and fraud prevention are huge priorities. Data science detects fraud and quantifies risks. At the start, the financial sector used data science in this capacity, but it has spread to other areas.
- Healthcare is becoming heavily digitized. Data science is used, for example, in medical image analysis, drug development, genetics, and genomics. Lastly, it is extremely useful for helping to develop better virtual assistants for patients.
- More people are using Internet searches. All search engines use data science algorithms to show desired results.
- Data science is everywhere, so there’s job security. There are many other data science and artificial intelligence applications, including advanced image recognition, targeted advertising, recognition of speed, planning airline routes, gaming and augmented reality. So, data science is a secure career that will always be in demand.
- It pays well. According to Glassdoor.com, data scientists in the United States bring home an average yearly salary of $129,127.
So, what are the top data-related jobs?
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The Top Jobs in Data
Let’s look at the top five data-related IT careers.
- Business Intelligence Analyst. These analysts use data to help organizations make good decisions.
- Data Analytics Manager. Data analytics managers extract the most pertinent information from massive volumes of big data.
- Data Scientist. Data scientists use their analytical, statistical, and programmable skills to collect vast amounts of data and extract valuable insights.
- Database Administrator. Administrators set up databases, maintaining them and keeping them secure.
- Database Developer. Database developers improve databases and develop new applications.
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FAQ
Q: What is data in simple terms?
A: Data is information such as facts and numbers and is used to analyze things and make sound, informative decisions
Q: What are the types of data?
A: Data is split into qualitative and quantitative data. Qualitative data is further divided into nominal and ordinal, while qualitative data is divided into discrete and continuous data.
Q: Why do we use data?
A: Data scientists and analysts data use data for activities such as:
- Making informed decisions
- Solving problems
- Gaining a better understanding of how an organization is performing and where it stands
- Process improvement
- Better understanding of customer/client behavior
Q: How do we collect data?
A: These are the most common means of data collection:
- Artificial intelligence
- Data reports
- Direct observations
- Focus groups
- Interviews
- Online forms
- Secondary collection via documents, datasets and records
- Social media monitoring
- Surveys, quizzes and questionnaires
Q: What is the best method to collect data?
A: “Best” is a subjective term; in many instances, we must account for the organization’s needs and situation. However, professionals in data collection typically rely heavily on questionnaires and surveys above other methods.
You might also like to read:
Data Collection Methods: A Comprehensive View
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