Data Analytics Solutions

Empower Your Decisions with Artificial Intelligence and Data Analytics!

Data analysis is the process of extracting meaningful and useful information from data sets. This process is used to process raw data to drive decision-making, strategy development and operational improvements. Data analysis is performed in many fields with different methods and provides critical data that will enable businesses to achieve their goals.

Stages of Data Analysis

Data Collection and Preparation
Accurate and clean data is essential for successful analysis. Data is collected from various sources and cleaned. At this stage, missing data is corrected, incorrect data is removed and all data is made available for analysis.

Data Exploration (Exploratory Data Analysis – EDA)
This stage involves getting an overview of the data. The data is examined through visual analysis (graphs, histograms, etc.) and basic statistics. EDA is used to understand the structure of the data and to identify the necessary directions for analysis.

Data Modeling and Statistical Analysis
At this stage, mathematical and statistical models are used to understand specific patterns or relationships in the data. For example, methods such as regression analysis, classification, clustering can be used. Modeling helps to reveal trends, relationships and predictions in the data.

Interpretation of Results and Reporting
After analyzing the data, the results are explained and made meaningful. These results are presented in graphs, tables and reports. This stage provides information on how the analyzed data will contribute to decision-making processes.

Methods of Data Analysis

Descriptive Analytics
They are analyses that look at past data to understand what happened. For example, analyzing how sales have fared over the past year.

Predictive Analytics
Analyses aimed at predicting future events from past data. For example, predicting future purchasing decisions by examining customer behavior.

Normative Analytics (Prescriptive Analytics)
They are analyses that show what needs to be done for an organization to achieve certain goals. This type of analysis makes recommendations based on results from data sets.

Exploratory Analytics
It is a freer type of analysis used to explore the data. The aim is to uncover hidden relationships or patterns in the data.

Application Areas of Data Analysis

Business Strategies and Decision Making
Companies make strategic decisions by analyzing customer behavior, sales, marketing campaigns and other data. For example, correctly identifying the target audience through customer segmentation.

Health Data Analysis in Insurance Companies
By analyzing customers’ health data, insurance companies can set policy prices more accurately. For example, with past health information and genetic data, they can predict people’s future health risks. By offering customized insurance offers to customers, they can both prepare policies that are more tailored to customers’ needs and reduce the company’s costs.

Financial Analysis
Financial data analysis is used to create investment strategies, assess risks, monitor costs and track financial health. For example, stock market analysis and credit risk assessments.

Marketing and Sales Analysis
Companies shape their marketing strategies by analyzing customer feedback, the effectiveness of advertising campaigns, and sales data. For example, developing content and advertising recommendations tailored to the target audience.

Industrial and Production Analysis
Data analysis is used to increase efficiency, reduce costs and improve quality in production processes. For example, efficiency and failure analysis on production lines.