Difference Between Analyzing and Evaluating (With Table)

Analyzing vs Evaluating

Analyzing and evaluating are two terms that go hand in hand. They are terms that are commonly used in research and data science field or any field that needs to understand and process the given data.

 Since they are often used together, it becomes difficult to properly differentiate between the two terms. So what is it that makes the two terms different?

Analyzing is breaking down and interpreting the given data. This is used to obtain the factors, effects and importance of the data. This usually requires longer thought processes since the data needs to be broken down for a further explanation.

Evaluating is the process that comes after analyzing. They provide the conclusion or the result of the research conducted on the data. Evaluating a data usually requires less thinking skills since it only determines the value and gives a short conclusion

The difference between Analyzing and Evaluating is that analyzing involves studying and understanding the data by breaking it down. But evaluating is the process that determines the importance and the value of the given data. The process of evaluating is done after analyzing the data since it is used to give the result or conclusion.


 

Comparison Table Between Analyzing and Evaluating

Parameters of ComparisonAnalyzingEvaluating
Data Studies and understands data Determines the importance and value of the data
Thinking process Longer and complex thinking process since the data needs to be broken downShorter thinking process since it only concludes
Concerns Concerned with the definitions and implications of the dataConcerned with the extent of the quality of the data
Association More associated with objectivityMore associated with subjectivity
Utilized Utilized in academic researchesUtilized to determine the pros and cons of the data
Result Not necessary to obtain a resultThe result is compulsory

 

What is Analyzing?

The word analyzing comes from the French word ‘analyse’ meaning ‘dissect’. It is also known to have a Greek origin. The process of analyzing is of six types based on the data received.

Analyzing is the process of explaining and developing the data by breaking them down into less complex data. Analyzing not only breaks down the data but also helps to form other data or details from the one broken down. It gives a wider perspective on all the collected data.

This process is the first step adopted by people in the field of research. Therefore, analyzing is a widely used process in the field of research and academics.

Since analyzing involves a lot of complex processes from the breaking down of data to the explanation of it, it involves a more elaborate thinking process. They are also very objective.

Analyzing
 

What is Evaluating?

The origin of the word evaluating is also different from analyzing. This is because the origin of evaluating is the French word ‘évaluer’ meaning to find the value of. 

So, one can say that evaluating is the process of finding the value of the given data. It also finds the feasibility. It gives the quality of the data, rather than finding new skills from it.

There are two types of evaluating process: formative and summative. Formative evaluating is when the data is assessed and the skill sets required for the data are obtained. Summative evaluating is determining or knowing of the goal set during analyzing of the data is achieved.

Since evaluating is a conclusive process, this is done after the data is analyzed. It is also associated with subjective thinking and therefore requires less thinking process as compared to analyzing.

Evaluating

Main Differences Between Analyzing and Evaluating

  • Though the two terms go hand in hand, the process involved with the data is different. Analyzing is the process in which the data is broken down for a further explanation. It helps in understanding the given data while evaluating a data means to give a value or to find the importance of the data.
  • Analyzing is normally only concerned with the definitions and implications of the data. This is not the case while evaluating since it is concerned with the quality of the data and the extent of it.
  • The thought process involved in both processes is also different. Since analyzing requires breaking down data, there is always the need for an elaborate thinking process, while this is not required for evaluating. This only concludes the power of the given data.
  • Since analyzing includes breaking down compound data, they are more objective than evaluating. Evaluating is usually only associated with subjective skills rather than objective.
  • The data found by analyzing is used in the research and academic fields. Evaluating is not often used in the academic research field, but rather in finding the pros and cons, or conclusion of the data.
  • Since analyzing only helps in explaining or developing the data further, there is no need for a result. But evaluating is the process that gives the conclusion to the data being analyzed, so having a conclusion is necessary.

 

Conclusion

Analyzing and evaluating are two processes that are used together. They are common terms used in the fields that study and find the importance of data. This has also resulted in not being able to differentiate between the two terms.

Analyzing is when the given data is studied, explained and broken down for further clarity. This process is widely used in the research field, along with academic fields that deal with the study of data like data science.

Evaluating is finding the importance or the power of the value. This process is done after analyzing for concluding the study on the data. Therefore, evaluating is a conclusive process.

Obtaining a result in analyzing is not necessary since this process only helps us understand the data better. But this is not the case for evaluating. Since the process is conclusive, obtaining a result is of utmost importance.

Both the processes are required to obtain complete information about a data and to be able to use the data for further researches or evaluation of other data.