What is Data ?

Data is a word we hear everywhere nowadays. In general, data is a collection of facts, information, and statistics and this can be in various forms such as numbers, text, sound, images, or any other format.

In this article, we will learn about What is Data, the Types of Data, Importance of Data, and the features of data.

What is Data?

According to the Oxford “Data is distinct pieces of information, usually formatted in a special way”. Data can be measured, collected, reported, and analyzed, whereupon it is often visualized using graphs, images, or other analysis tools. Raw data (“unprocessed data”) may be a collection of numbers or characters before it’s been “cleaned” and corrected by researchers. It must be corrected so that we can remove outliers, instruments, or data entry errors. Data processing commonly occurs in stages, and therefore the “processed data” from one stage could also be considered the “raw data” of subsequent stages. Field data is data that’s collected in an uncontrolled “in situ” environment. Experimental data is the data that is generated within the observation of scientific investigations. Data can be generated by:

• Humans
• Machines
• Human-Machine combines.

It can often generated anywhere where any information is generated and stored in structured or unstructured formats.

What is Information ?

Information is data that has been processed , organized, or structured in a way that makes it meaningful, valuable and useful. It is data that has been given context , relevance and purpose. It gives knowledge, understanding and insights that can be used for decision-making , problem-solving, communication and various other purposes.

Why data is important ?

• Data helps in make better decisions.
• Data helps in solve problems by finding the reason for underperformance.
• Data helps one to evaluate the performance.
• Data helps one improve processes.
• Data helps one understand consumers and the market.

Categories of Data

Data can be catogeries into two main parts –

• Structured Data: This type of data is organized data into specific format, making it easy to search , analyze and process. Structured data is found in a relational databases that includes information like numbers, data and categories.
• UnStructured Data: Unstructured data does not conform to a specific structure or format. It may include some text documents , images, videos, and other data that is not easily organized or analyzed without additional processing.

Types of Data

Generally data can be classified into two parts:

1. Categorial Data: In categorical data we see the data which have a defined category, for example:
   • Marital Status
   • Political Party
   • Eye colour
2. Numerical Data: Numerical data can further be classified into two categories:
   • Discrete Data: Discrete data contains the data which have discrete numerical values for example Number of Children, Defects per Hour etc.
   • Continuous Data: Continuous data contains the data which have continuous numerical values for example Weight, Voltage etc.
3. Nominal Scale: A nominal scale classifies data into several distinct categories in which no ranking criteria is implied. For example Gender, Marital Status.
4. Ordinary Scale: An ordinal scale classifies data into distinct categories during which ranking is implied For example:
   • Faculty rank : Professor, Associate Professor, Assistant Professor
   • Students grade : A, B, C, D.E.F
5. Interval scale: An interval scale may be an ordered scale during which the difference between measurements is a meaningful quantity but the measurements don’t have a true zero point. For example:
   • Temperature in Fahrenheit and Celsius.
   • Years
6. Ratio scale: A ratio scale may be an ordered scale during which the difference between the measurements is a meaningful quantity and therefore the measurements have a true zero point. Hence, we can perform arithmetic operations on real scale data. For example : Weight, Age, Salary etc.

Conclusion

Data becomes valuable when it is processed, analyzed, and interpreted to extract meaningful insights or information. This process involves various techniques and tools, such as data mining , data analytics, and machine learning.