FAQ About Data Analysis

Data analysis is the process of analyzing raw data in order to reach a conclusion. Turning data into actionable knowledge is the difference between faltering and success for businesses and organizations of all types. Maximizing the value of information requires data analysis.

First, data is collected. At this stage, data is collected from sources and transferred to the system where it will be stored.

The next stage is the preparatory stage. At this stage, the data is ready for use and stored in a system accessible to users and applications. In order for the data quality to be at the highest level, it is necessary to clean the data and turn it into information that can be easily accessed and queried.

The next stage is the analysis stage, that is, the modeling. Data; It can be analyzed with end-to-end analytics platforms such as SAP, Oracle, or SAS, or processed at scale with tools such as Apache Spark.

The final step is decision making. The knowledge gained is ready for action. Data visualization and reporting helps to understand analysis results. 

Data analysis can be used in different ways in many fields. But it can be divided into four main types.

• First, descriptive analytics are used to summarize and visualize historical data. In other words, it tells organizations what has already happened.
• Another type is diagnostic analysis. Diagnostic analysis examines not only what happened, but also why the condition occurred. Analysts should conduct a thorough investigation that will enable them to identify trends and cause-effect relationships to perform diagnostic analysis.
• Unlike the first two types of analysis, this type of analysis talks about the future, not the past. Predictive analysis predicts probable outcomes based on statistical models and identified trends from historical data.
• Another advanced type of analysis is prescriptive analysis. The final stage has been reached in the field of data-based decision making, thanks to prescriptive analyzes that suggest the best solutions through predictive analysis. 

Data analysis can be used in any field where it is needed. E.g;

• Production
• Health
• Telecommunication
• Sport
• Sales

and the list goes on.

Big data analysis uses large-scale datasets to reveal new relationships between variables and provides a better understanding of large amounts of information. 

• Text analysis,
• Statistical analysis,
• Descriptive analysis,
• Inferential analysis,
• Diagnostic analysis,
• Predictive analysis,
• Prescriptive analysis. 

Data analysis; It helps businesses answer key questions and perform better. From financial analytics to understanding customer behavior, using data analytics can move your business forward and help reduce risk.

In addition, by making data analysis, businesses; can reduce overall costs, improve processes and make better decisions in a short time. 

When it comes to making better decisions for the future, the method used is data analysis. In this way, companies can evaluate their information more effectively and determine their future strategies more accurately. In many subjects such as growth, sales and investment, companies apply to data to see their future.

One of the benefits of data analysis is its contribution to applications aimed at increasing customer satisfaction. Thanks to data analysis, the feedback received from the customers is evaluated more accurately, and in this way, services and products that provide effective customer satisfaction can be offered. Increased customer satisfaction also means higher income and prestige for the business. 

With the understanding of the importance of data, some old methods have become useless and new programs have been developed for analysis. These programs provide great convenience in managing data and save time for data analysis specialists with the benefits they provide. It is of great importance to choose the program that is suitable for the need among the data analysis programs.

There are many data analysis tools and they are divided into 3 main categories. There are many programs that are divided into three as visual, numerical and verbal and are used to meet different needs. Many businesses can make better decisions by using these tools together. Analyzing data and drawing meaningful conclusions is becoming critical for many lines of business. 

A data analyst's responsibilities are everything about the data.

• Managing the delivery of user satisfaction surveys and reporting on results using data visualization softwares.
• Working with the business line owners to develop requirements, define success metrics, manage and execute analytical projects and evaluate results.
• Monitoring the practices, processes and systems to identify opportunities for improvement.
• Gathering new data to answer client questions, collating and organizing data from multiple resources.
• Designing, building, testing and maintaining backend code.
• Establishing data processes, defining data quality criteria and implementing data quality processes.
• Working as a part of the team to evaluate and analyze key data that will be used to shape future business strategies.

As hard skills a data analyst needs some strong mathematical and statistical ability. Also knowledge of programming languages such as Python, Oracle and SQL is necessary. It is also important to have an analytical mindset. 

When it comes to soft skills a data analyst really needs excellent communications skills.

Among the data analysis methods, the quantitative data analysis method includes the collection and analysis of numerical data. Different quantitative analysis methods can be used according to the type of research carried out, the number of dependent and independent variables, the desired result and hypothesis sources. In order to achieve the right results, you need to use the right analysis method that responds to your goals and objectives.

According to the collected data, quantitative analysis methods,

• Chi-square (Pearson Chi-square Test),
• ANOVA,
• Correlation Analysis,
• 95% Confidence Interval,
• McNemar Test,
• Fisher's exact Test.

The real importance of data analysis is in the way of increasing the success of the company. Data analysis methods for making the right investment decisions and measuring customer behavior are critical. Collecting the right data and analyzing them in line with the objectives with the right methods will benefit the company. However, if these transactions are done incorrectly, it can harm companies and customers.

When the data analysis model is created correctly, it contributes significantly to the growth of your company. It will make your job easier to use data analysis methods to accurately determine the moves that your company will make in the future. In this way, your company can interpret the available data more effectively. It will be easier for you to take the right steps by noticing the opportunities and threats that may arise in the future.

Data analysis specialist collects data that can be useful for your company in this world of endless data. These experts analyze the data using specific software, tools and methods and draw meaningful conclusions from this data for your company. This makes it easier for you to make the right strategic decisions within your company.

Data analysts usually have knowledge in the statistical sciences. These people can be found in certain departments within the companies or they can be taken from an outside service provider as a service. In this area, which requires a lot of attention, it is critical to collect and analyze the data that the company needs. These operations, which support operations, provide information on sales forecasts and strategies.

With the development of data analysis methods, some old methods have become slow and inefficient. Various data analysis programs have been developed for the analysis of these data, and data management and analysis have been accelerated. These programs, which are frequently used by data analysis specialists, are determined in accordance with the needs. Although there are many data analysis tools, there are basically 3 types.

These methods, which are divided into three different categories as visual, verbal and numerical, should be used appropriately and correctly. It is important to obtain accurate data and determine company policy with these methods, which are a helpful factor in the development of companies. Data analysis methods are selected according to the company's target, purpose and industry.

Visual data analysis tools show results as graphs and maps. Numerical data reflects the analysis results numerically. Verbal analysis methods, on the other hand, evaluate about conceptualization, problem identification and application. The method, which consists of collecting correct data, data analysis and data visualization, offers great advantages.

The data analyst gives both strategic and tactical support to the company he works for. It adds business value to the firm. It analyzes the information gathered from each source. The main qualifications of data analysts that give the company an advantage by working in the private and public sectors are as follows:

• Good knowledge of MS Office programs,
• Having a bachelor's or master's degree,
• Have a good command of both spoken and written English,
• Having a minimum of 1 year experience,
• Having sufficient technical knowledge,
• Being prone to analytical programs such as SPSS,
• Researching new models and techniques,
• To have the ability and desire to constantly learn,
• Developed communication skills.

Data Analysis is defined as the modeling process that supports the processes of finding useful information, reaching results and making decisions as a result of collecting, extracting and processing raw data.