What is Data Analysis? Methods, Phases, and Process

Extracting meaningful data from a massive set of unstructured information using analytical and logical reasoning is referred to as data analysis. In today’s era, data analysis plays a vital role in decision-making processes in the domain of businesses, social sciences, science, etc. Generally, people make any decision based on the meaningful data available. So, data analysis is commonly used in many businesses and companies that work as the best solution for many challenges. 

What is Data Analysis?

Only transforming the unorganized or unstructured data into an understandable form is not the definition of data analysis. The actual definition of data analysis is the process of gathering information, verifying it, cleansing it, transforming it into relevant facts, and modeling it into a specific data model. 

The very first step in data analysis is to collect information or raw data. Later, you need to verify whether the collected data is useful to produce a particular solution. Next, the cleansing of data implies removing incorrect data or correcting inaccurate data. The process of data transformation means transforming the structure of data into another format. The last step in data analysis is data modeling. Data modeling is a method of creating the data model for the transformed data using specific techniques and rules. 

Generally, the data analysis process is categorized into two kinds:

  1. Qualitative
  2. Quantitative

In Qualitative data analysis, all non-numeric data is analyzed or inspected. On the other hand, Quantitative data analysis implies analyzing or measuring the data, and results are expressed in numbers. 

Need for Data Analysis

Data analysis has been used widely since the olden days. It is very beneficial for businesses and organizations. If the data analysis is not carried out, the unstructured data remains in a pile. In industries, data analysis helps organizations to stand potential in the market. Additionally, it also enables businesses to find business risks and avoid them with ease. The data analysis process enables organizations to focus more on customers’ needs and generates satisfactory products. 

Principles of Data Analysis

Here are some essential data analysis principles that you must consider before carrying out the data analysis process. 

  1. Be clear with your approach: One of the critical data analysis principles is you must be very transparent about why you are collecting the data? What is the purpose of performing data analysis using the gathered data? Are there any resources available relevant to the collected data? Is there any tool used to process the data? Based on these components, you should gather the data. 
  2. Source of data: Another important principle in the data analysis process is the source of the raw data. You need to know how your data is produced. If you are performing a data analysis process for your company, make sure the location of the data you are retrieving. 
  3. Other related information: When you get the data source, find other relevant and relatable information that may help you carry out the entire analysis process.
  4. The facet of your data: Before you perform the data analysis process, you must ensure that you study every aspect of your data. Look at the data in every possible dimension. 
  5. During the data analysis process, you must consider all possible questions stakeholders may ask. Considering those questions, perform the data analysis process accurately. 
  6. When you complete the process, make sure to communicate with clients or stakeholders for accuracy. 

The entire data analysis process is a questioning and answering process between the data analyst and stakeholders. A data analyst has to answer the questions of stakeholders accurately.  

Methods of Data Analysis

We have observed the principles of the data analysis process. Now, we shall focus on the methods of data analysis. In the above section, we have discussed two data analysis methods, Quantitative and Qualitative. Apart from these two methods, there are other data analysis methods, as well. Let us discuss each of these data analysis techniques in detail.

  1. Quantitative Analysis:

As its name suggests, the quantitative analysis is performed in terms of numeric data. The significant objective of the quantitative data analysis is to measure the frequencies and differences of variables. Such differences ad frequencies can be computed using the following methods:

  1. Mean: It calculates the average of set values. 
  2. Median: It implies the value present at the midpoint of any data set. 
  3. Mode: This quantitative analysis method determines the element that has a higher frequency in the data set. 
  4. Frequency: It determines the number of times a particular element occurs in the data set. 
  5. Range: This quantitative analysis method examines the difference between the lowest and highest values present in the data set.
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Now, let us discuss how quantitative data is analyzed. The following are some steps to perform analyzing quantitative data:

  • The collection of data is a very primary measure in any analysis process. After this, the step of data validation is carried out. In this step, analysts identify whether the raw data follows the standard rules. Data collected is collected from multiple respondents.  Analysts verify whether the data was gathered with a particular procedure and ensures the completeness of data. 
  • There is a case where large data sets may contain numerous errors. This measure is data editing, which ensures that there are no errors present in the data set. Various checks n data are performed to remove mistakes. 
  • As mentioned above, the data is collected from multiple respondents; these data are assigned a value called data coding. 
  1. Qualitative Analysis:

Qualitative data analysis is the opposite of the quantitative analysis. It does not depend on numbers; instead, it focuses on letters, images, words, symbols, and texts. Qualitative analysis is divided into five different categories, as follows:

  1. Content analysis: In this type of research, verbal or behavioral data is categorized into a tabular format.
  1. Narrative analysis: We are familiar with narrating stories. The narrative analysis implies describing the primary data by multiple respondents. When multiple respondents convey the non-numeric data, we get different views and experiences about the data. 
  1. Discourse analysis: This kind of qualitative analysis includes working on written text or oral talks. 
  1. Framework analysis: This analysis process involves multiple stages, like coding, familiarization, mapping, charting, and interpretation of the qualitative data.
  1. Grounded theory: Every single fact of data is formulated individually. After all the facts are prepared, analysts determine whether all cases contribute to the qualitative data. 
data analysis

Before you execute qualitative analysis, you need to consider some specific points. These points are listed below:

  • The first step to consider before you start analyzing qualitative data is you must get familiar with the raw data. Ensure that you read the information multiple times. Try to find out the pattern of the data. 
  • Before analyzing any data, it is evident that you are performing analysis to find a solution to a particular problem. When you read the data several times, have a glance at the analysis process’s objectives. Make sure whether the data contains relevant information required to answer the questions. 
  • The next step is coding. Coding is useful in assigning labels to data. Analysts identify the behavior of the data and assign codes to it. 
  • Once the coding is done, analysts identify specific patterns and themes and look for the expected answers. 
  1. Statistical Analysis:

Another method used for performing data analysis is statistical data. In this method, the previous data or shreds of evidence are mapped into dashboards. Based on the earlier information, statistical data analysis is performed. The statistical data analysis is categorized into two types based on the amount of data analyzed. They are 

  1. Descriptive analysis: This type of statistical data analysis analyzes the complete set of data. It calculates mean and deviation for continuous data, and for categorical data, it computes percentage and frequency. 
  2. Inferential analysis: In this type of statistical data analysis, a specific sample of data from the entire data is analyzed. When you select different instances of available information, you get multiple conclusions for the same data. 

Analysts conduct statistical data analysis in the following actions:

  • Researchers represent the entire raw data into a pie chart or any other graphical data representation method. 
  • Once you represent data in the graphical form, find locations of data using mean or median methods. 
  • Next, analysts go for finding out whether the data is spread out or clustered. They use the most common method of Standard Deviation that represents the variation or dispersion of the data set. 
  • Later, researchers need to predict the outcome based on the previous data results. 
  • The last step is Hypothesis Testing. This testing tells whether the data is entirely accepted or not. 
  1. Data Mining (Text Analysis):

Data mining (Text Analysis) is one of the crucial methods of data analysis. It is also referred to as text analysis. Generally, several data mining tools are available that determine patterns in massive data. The data mining analysis is carried out in sequential steps, like collecting the raw data, storing it on a server or cloud, accessing data for organizing, sorting the data, and producing it in the form of a graph or chart.  

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In the data mining process, the collected or raw data is stored in a data warehouse. This type of data analysis method enables businesses to enhance their marketing techniques and sales. More importantly, it assists firms in detecting fraud and spams. 

Let us know how analysts carry the data mining or text analysis process. 

  • Data Cleaning: This process implies cleaning data and removing unwanted or incomplete data from the data set. Additionally, analysts fill the data which is missing and eliminate noisy or error data. 
  • Data Integration: All the resources of data, like files, databases, cubes, etc. are combined for the analysis process. Doing this will enhance the accuracy and performance of the analysis process. 
  • Data Reduction: The entire data is reduced or optimized by maintaining its integrity. Several methods, like Decision trees, Naive Bayes, etc. are used for the data reduction process.
  • Data Transformation: The original data is converted into the derided format required for the data analysis process. 
  • Data mining: This step uses classification and clustering techniques to determine data patterns from the large data set. 
  • Pattern Evaluation: The patterns identified in the data mining process are evaluated to make it understandable by ordinary users. 
  • Knowledge Representation: In this measure, the filtered data is represented in reports, tables, etc. 
  1. Predictive Analysis:

Predictive Analysis refers to making predictions using the existing data about what will happen in the future. In predictive analysis, analysts determine or predict future data results based on current or previous data results. But performing predictive analysis requires a vast amount of data. The more the amount of data available, the higher the accuracy of the prediction. 

For example, consider that every year the company increases its revenue by 20%. So, we can predict that the company expands its revenue by 20% this year as well. The predictive analysis can be used in assessing risk, forecasting sales, or qualifying leads. 

Researchers execute predictive data analysis in seven precise steps. We shall see each of these measures in brief:

  • Before analysts perform predictive data analysis, they first define the project’s objective for performing predictive analysis. They consider certain aspects, like what will be the outcomes? What data sets should be used? Does the result of the study meet business requirements?
  • The next step analysts follow, they gather relevant information required for the project analysis. 
  • In the third measure, researchers carry out the actual data analysis process by verifying, cleaning, and transforming the gathered data. 
  • Later, the analyzed data is represented in the statistical form and undergoes Hypothesis Testing. 
  • Researchers then develop the respective data model to predict future results. 
  • Lastly, they deploy the data model into a decision-making process. 
  1. Diagnostic Analysis:

Another method of data analysis is the diagnostic analysis. In this type of data analysis methods, the cause of any business problem can be identified. The diagnostic analysis is also called root cause analysis. In other words, this analysis expresses the behavior pattern of the data. Analysts can use data mining, drill down, drill through, and data discovery techniques. 

For example, suppose the leads of the company increased in a particular month. The diagnostic analysis determines while marketing sales contributed the most in increasing the leads. 

  1. Perspective Analysis:

The perspective analysis is dependent on predictive analysis. In the predictive analysis, you get a result prediction based on the previous data results. This type of analysis includes perspective from all the above-listed analysis. So, it is supposed to be one of the best analysis methods than descriptive and predictive analysis. 

Phases of Data Analysis 

All the above techniques are the kinds of data analysis methods used widely in the business domain to ensure the particular project’s accuracy and efficiency. Let us now focus on the data analysis process. You might be wondering how the data is analyzed? The data analysis process is performed in multiple stages. These phases are listed below:

  • Data Requirement Specification
  • Data Collection
  • Data Cleaning
  • Data Analysis
  • Data Interpretation
  • Data Visualization

The following is a detailed explanation of each phase involved in the data mining process. 

  1. Data Requirement Specification:

The relevant data is required to provide input to the data analysis process. This input data is collected based on the customers’ needs or an individual directing the analysis process. Generally, analysts take a survey for collecting data from people called respondents. Also, they fetch data from various other resources according to the customer’s requirements. So, the obtained data may be of any kind, like qualitative or quantitative data. 

  1. Data Collection: 

We have discussed that the data is gathered from multiple resources, like respondents, satellites, numerous devices, online resources, interviews, documents, etc. Once you get the precise idea about the data requirements, start collecting data. The data collected from various sources are combines together in the data collection phase. After collecting the data, organize it in a well-structured manner for the data analysis process. The data analysis process requires organized and processed data. You should represent the data into the row and column format. 

  1. Data Cleaning:
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Once you properly organize the data, look for any unwanted or incomplete information inside the data set. The data cleaning phase of the data analysis process involves transforming data into an understandable form. You can eliminate any unwanted or irrelevant data from the data set. Additionally, you can add some extra information to missing places. Another type of cleaning involves removing white spaces, errors, or duplicate facts. 

To ensure that the data in the data set is clean, you need to perform several tasks, like record matching, deduplication, quality of existing data, and determining inaccurate data. Before you use the data set for the analysis process, it should be clean. 

  1. Data Analysis:

When you get clean data free from noise and errors, you can proceed to the analysis process. You can use any one of the above analysis methods to execute the data analysis process. This process is also called exploratory data analysis because it involves additional data cleaning and data requirements. All these processes run iteratively to carry out an effective data analysis process. You can also utilize several data analysis tools that make you comfortable in interpreting and understanding outcomes. 

  1. Data Interpretation:

Once you finish the data analysis process, you can move forward for the data interpretation phase. In this stage, you can view the data based on the previous data outcomes and come to a conclusion about the results. Before you move towards the data interpretation phase, you should first execute the data analysis phase, as the input to this phase is the result of the data analysis stage. 

  1. Data Visualization:

The last phase of the data analysis process is data visualization. It implies representing the results of analyzed data in graphs, tables, charts, etc. Such representations in the graphical form are easily understandable by humans. Looking at the visual representation enables us to get a clear idea about the particular project or product. It represents the summary of the data. 

Advantages of the Data Analysis Process:

Data plays a primary role in any business or company. They must provide the best data about their products that appeal to customers to buy them immediately after reading the data. So, companies must know how to implement the data analysis process. Let us see how the data analysis process is beneficial for companies or organizations.

  1. Enhances the decision-making process:

We have discussed that the data interpretation phase of the data analysis process uses the data analysis phase’s outcomes.  And these outcomes are concluded based on the previous data results. This enables businesses to make a proper and better decision. Also, customers get a 360-degree view of a particular product.

  1. Improves marketing strategies:

When you know what your customers need regarding the particular product, the data analysis process enables you to effectively market your product. You can collaborate with clients through campaigns and use that data for improving target results. 

  1. Improved customer service:

Another benefit of data analysis is that it enables businesses to improve customer relations, look after their needs, and offer better and personalized services. Doing this will, in turn, enhance the productivity and growth of businesses. 

  1. Carry out all operations effectively:

One of the significant advantages of the data analysis process is that it helps businesses save their time and money. When they get a clear idea about customers’ needs, less money and time will be spent on marketing. Less expenditure on marketing, in turn, increases revenue. 

Conclusion

The data analysis process is collecting the data, verifying it, cleaning it, transforming it into the desired format, and using any data model to represent the filtered and transformed data. We have covered data analysis, the need for data analysis, principles of analysis, several data analysis methods, the data analysis process, and the advantages of data analysis in the business domain. We hope we have explained all the significant topics of the data analysis in this article. 

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