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24 March 2024 Back
The Importance of Data Analysis for Companies

‘The Importance of Data Analysis for Companies’

Data analysis is critical to the success of companies. Data analysis, also known as data analysis, provides meaning to masses of information and their effective use with the opportunities brought by high technology. With data management, raw data is processed to shape business processes and improve decision-making mechanisms.

Data analysis enables decisions to be made more scientifically and businesses to operate more efficiently in today's business world. Data analysis helps perform many operations such as personalizing a bank's customer interaction, identifying health needs in the healthcare system, and customizing the customer shopping experience.

What is Data Analysis?

Data analysis is the process of collecting, examining, cleaning and modeling raw data and transforming data into useful information in order to make purposeful decisions in an industry. In the process, different analytical and statistical tools are used to evaluate the data. The main goal of the collected data is to reach the result, and to achieve the result, useless data must be removed from the system. With the data, it becomes clear how to follow the path and what to do.

Data analysis should not be taken lightly. Data analysis is used in almost every field, from giant businesses to individual studies. It is very important to transform raw data into useful data in order to create a strategy and make critical decisions in business life and to carry out a systematic study. Most companies benefit from data analysis when making decisions.

Types of Data Analysis

There are types of data analysis developed depending on business and technology.

  • Text Analysis
  • Statistical Analysis
  • Prescriptive Analysis
  • Predictive Analysis
  • Diagnostic Analysis

Text Analysis

Text analysis is also known as data mining. Discovering patterns in large data sets using databases or data mining tools is one of the data analysis methods and transforms raw data into business information.

Statistical Analysis

Statistical analysis answers the question “what happened” using historical data in the form of dashboards. Statistical analysis involves collecting, analyzing, interpreting and modeling data.

Explanatory Analysis

Descriptive analysis analyzes complete data or a sample of summarized numerical data.

Prescriptive Analysis

Prescriptive analysis takes all the insights gleaned from the first three types of analysis and uses these insights to create recommendations about how the company should behave.

Predictive Analysis

Predictive analysis analyzes the sample from complete data.

Diagnostic Analysis

Diagnostic analysis is used to detect behavioral patterns of data.

Other Data Analysis Tools and Methods

Artificial Intelligence and Machine Learning

One of the most important points that technology has reached is artificial intelligence technologies. The operation of this technology depends on data. Without data, AI won't work. Without artificial intelligence, data has no meaning on its own, it is fed by data.

The analysis techniques in question are as follows;

  • Artificial neural networks
  • Decision trees
  • Evolutionary algorithms
  • Fuzzy logic

Mathematics and Statistics

The mathematical and statistical type of analysis enables gaining meaningful insights from raw data using numerical calculations. The techniques are as follows;

  • Descriptive analysis
  • Discriminant analysis
  • Distribution analysis
  • Factor analysis
  • Time series analysis
  • Regression analysis

Graphics and Visualization Tools

Graphics and visual models are used to present meaningful insights gained from data in a format that everyone can understand. Graphics and visualization tools include charts (bubble chart, area chart, column-bar chart, radar chart, pie chart), frame diagrams, maps (dot map, flow map, and regional map), and scatter diagrams.

Why is Data Analysis and Reporting Important for Businesses?

Data analysis has many advantages, from individual transactions to businesses.

1. Making Efficient Decisions

Businesses need data to make informed and accurate decisions. With data analysis methods, raw data is transformed into useful data. Based on these results, new business plans, marketing strategies and different decisions regarding the future of the business can be implemented. The expenses of the business can be reduced, and missing or disrupted business processes can be improved. All processes in the business can be made more efficient.

2. Determining the Target Audience

The business should work towards audiences who are interested in its products and services. In this context, it is necessary to determine the customer profile they appeal to in order to use money, time and other resources correctly and to create a specific target audience. Data analysis is used to determine the target audience. It guides you on which audiences you should focus on in order to identify your target audience.

3. Knowing the Target Audience Correctly

With data analysis, it is determined which target audience the products and services are suitable for. With the data left by customers, information can be obtained about their interests, likes, spending level and purchasing experience. In this context, it is possible to achieve greater efficiency with personalizations, arrangements and planning.

How to Perform Data Analysis?

Data analysis processes are based on scientific and social technical methods. Nowadays, scientific technical method is used more in business life. However, in some cases, the social technique method is also used. Both methods are very important.

There are 5 basic steps applied for data analysis. These steps;

1. Setting Goals

The data to be obtained as a result of data analysis should be determined in advance. This should be done in a way that reveals the data. When determining a target for data analysis, classifications such as age, gender and income level can be made.

2. Data Collection

Data collection is at the heart of the process. After the needs and goals are determined, the data sought in this direction must be collected. In this context, it is very important to pay attention to collecting data that can answer the questions determined. The most commonly used methods to collect data today include computers, social media and blogs, mobile applications and websites.

3. Data Processing

After the data is collected, the processing process begins. The data is structured to be suitable for analysis.

4. Data Cleansing

The collected data must be cleared of those that are not suitable for analysis. Data that does not answer the specified questions and will lead to misleading results should be removed.

5. Data Modeling

Once the data is processed and cleaned, the modeling phase begins. The data analysis process begins during the modeling process. It is necessary to plan in a way that data management control can be achieved during the data analysis process. The data analysis process takes place using the tools specified for analysis. After the data analysis process, the results are interpreted.

6. Conclusion

As a result of the analysis, the results and report regarding the data analysis are announced. Data analysis is implemented in the form of testing. Analysis results are reported.