Data Analysis: Overview and processes

Data Analysis is a process of inspecting, cleansing, transforming, and modeling official data with useful reports and conclusions that support decision-making and structuring business strategy planning in an organization. Useful data analytics tools are leveraged to analyze data from all over the world. Initially, it was a major aspect of the branch of Data Science.  In this blog, we will discuss Data Analysis: Overview and processes.

 

Data analysis includes several approaches depending upon company requirements. The major approaches of data analysis are:

 

• Data Mining
• Predictive Analysis
• Statistical Analysis
• Business Intelligence 
• Text Analysis

 

In layman’s terms, data analysis analyzes raw but useful data to a conclusion to help businesses plan their moves to sustain market competition. It also affects organizational performance management positively. These data analytics processes make use of high-end applications operating on machine learning algorithms, simulation, and automated systems. 

Benefits of Data Analytics

Talking about data analysis, various positive factors improve organizational performance of any company. Below, I have listed down a few:

• Improves decision making

Companies leverage data analytics tools to make use of valuable information they obtain from their collected data. This analyzed report guides companies in making strategic business plans, product planning, raw materials, inputs, etc. 

• Marketing becomes more effective

Businesses must first try to understand their targeted audiences. Once you know your customers’ awareness, financial problems, and values, you can easily share your innovative business ideas. Data analysis refers to undergoing numerous marketing campaigns and interpret its effects and improvements.

• Improves customer services

Data analytics provide deep insights into their customers and business clients. This usually helps companies customize their services according to customer needs and market demands. It also allows you to build authorized trust and authenticity and attract more audiences. 

Types of Data Analysis

• Data Mining

Data mining is the process of extracting a useful amount of unknown data from a mass of data collection. This process involves computer science methods, and the data is summarized to a conclusion that can be used for further analysis or to predict future market trends.

• Business Intelligence

Business intelligence analysis uses techniques and tools for data acquisition and transforming those large unstructured data. This is done to identify, plan, and structure the strategic business plans. The main goal is to easily interpret large volumes of data and bring out new opportunities for business growth and market sustenance.

• Statistical Analysis

Statistics is the study of collection, analysis, interpretation, presentation, and organization of data. Initially, two major statistical methods are applied: 

 

• Descriptive statistics: In this category, a whole data collection is summarized with numerical descriptors such as Mean, Standard Deviation for continuous data, and Frequency, Percentage for Categorical Data.
• Inferential Statistics: This process includes Hypothesis Testing (answering yes/no questions of collected data), Estimation Testing (estimating numerical characteristics of the information), Correlation (describing associated information), Regression (modeling relationships within the data)

• Predictive Analysis

As the name states, predictive analysis is done to predict future oncoming’s in any organization by analyzing current and historical data of the company. It is used to identify any risks or opportunities that need appropriate decision-making.

• Text Analysis

Text analysis includes the process of deriving high-quality data from collected text data. It involves structuring the text input, using Statistical Pattern Learning, and evaluating and interpreting the output.

Data Analysis Process

The Data Analysis process includes the following phases:

 

• Data Requirements

The primary stage is the requirement of data for analysis. The data that is necessary as inputs require identification; hence the data can be numerical or categorical.

• Data Collection

As the name suggests, the second step is the data collection process of gathering targeted information and variables. The emphasis should be on the accuracy of the data that needs to be analyzed. Accurate data collection ensures the right business strategy planning and decision-making. This data is collected from various sources such as company databases, web pages, social media platforms, etc. Since the data is large and resources are many; therefore these data are completely unstructured and sometimes may also contain irrelevant information.

• Data Processing

The collected data is then processed for analysis. Take it as an example of a dairy factory where the milk is first processed to form cheese. Similarly, the data requires correct structuring by placing them in their rightful rows and columns of a table while using a data analytics tool or application.

• Data Cleaning

The processed data might still be incomplete since the data has not been filtered. This means the processed data might contain duplicates or errors, preventing you from getting the true report for analysis. 

Therefore, proper data cleaning is done where the useless data is removed.

• Data Analysis

Data analysis is the process where the data is organized and ready for interpretation. There are various data analytics tools to understand and interpret data to derive conclusions. Data visualization can also be counted to examine graphical data to receive insights. 

 

This process often also requires additional data cleaning or collection.

• Communication

The last step is the communication step. This step refers to forming a data report for sharing it with the users and implementing further actions. The data can be in the form of tables, charts, graphs, anything that is easily understandable.

 

Many data analytics tools provide excellent facilities for obtaining information with unique codes and formats.

 

As we say, data analysis helps improve organizational performance management sufficiently with its efficient work styles. Data analysis helps maximize market efficiency and enhance a company’s earnings. 

 

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