What is data analysis?

What is data analysis?

It is the collection of data, examination, and analysis, interpretation, and displaying the data in various formats, such as charts, pictures, etc., with various techniques. At present, technology has been developed that helps in data analysis. This makes it easier to analyze in-depth information. to make better decisions

What are qualitative and quantitative data? What are the differences?

-Quantitative data is numeric data that can be counted Quantitative data usually comes in tabular form. To answer statistical questions, such as how many people are compared this month? How much is the difference compared to last month? Are there a lot of clicks into the cart? etc.

-Qualitative information Is information that can be described or explained in the form of letters, pictures, or videos, which can be used to give a logical answer. What is the cause, why and how?

Data analysis techniques

1. Text analysis

It is an analysis of quantitative and qualitative data. to convert it into readable data the information is slightly different but can be differentiated, such as the word bright, which has a different meaning depending on the context, the weather is bright, you are very bright, etc.

2. Data mining (KKD)

It is a process of checking for abnormalities, patterns, and relationships to predict future results, such as grouping and organizing patterns in order. and abnormality detection

3. Statistical analysis

Statistical analysis involves surveying and presenting enormous amounts of quantitative data. To detect trends and patterns through statistical analysis there are many techniques.

-Descriptive statistics Descriptive statistical data analysis describes, summarizes and visualizes basic properties of data through charts and reports, such as frequency, trends, distribution, etc.

-Inferential Statistics Inferential statistical analysis of data is used to conclude the entire population. based on a sample of the population, etc.

4. In-depth analysis Also known as root cause analysis, aims to answer the question: 5. 

Predictive analysis

Forecast uses past data to predict future events, creating analytical models. By using this type of analysis You can predict customer needs. Predict customer churn and determine the number of target customers that can be converted into sales for the government

6. Simulation

It includes all the information and insights you have. and transforms them into actionable insights. In short, it outlines the best practices to follow in a given situation.

So why is data analysis necessary?

It is preparation for future situations and improving products and services. Help in deciding and creating a plan with data This can determine more effective strategies to improve processes, prevent problems, and detect growth opportunities. and decide where to focus investments

Benefits of data analysis

1. Predict behavior: Analysis of historical and real-time data can reveal trends and patterns. 

2. Increase productivity Data analytics can help you improve daily processes and increase team productivity while increasing public satisfaction.

3. Decision-making based on data Instead of relying on intuition or experience Data provides clear evidence to support decision-making.

4. Increase competitive advantage By analyzing data You can learn what works well. What's good (and what's bad) for your own business? But you can also detect the weaknesses and strengths of the competition. Reveal opportunities for improvement or a unique perspective for your marketing strategy.

Data analysis steps

1. Set goals for what data you want to analyze.

2. Collect information Once goals are set, various information can be collected from each channel.

3. Check the accuracy of the information.

4. Use data analysis tools.

5. Analyze data It is the most important step in the data analysis process when you extract value from your data. Once you have integrated data analysis tools, You will be able to apply those tools to your data.

6. Display data in various formats

7. Summarize the results of the data.