Data analysis is a process consisting of steps to process and organize raw data derived from Database ...and removing inaccurate or useless information, transforming it into actionable and modelable information. Data analytics can be performed on many data sets. Examples of data analytics include a company evaluating the feedback it receives by compiling data about its employees, conducting performance evaluations, or developing a survey for its customers.
In this article, we will discuss the definition of data analysis and the steps involved in data processing. We will also provide examples of data analysis.
What is data analysis?
Data analysis is the modeling process in which useful and meaningful information is revealed after the raw data collected has been sorted. In the business world, raw data must be transformed into useful information to drive action, adopting a more systematic approach when setting strategy and making critical decisions. This is why most companies leverage the insights and ideas provided by modern data analytics when making decisions.
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What is the purpose of data analysis and reporting?
Data analysis is conducted to solve current questions using scientific methods, facilitate the storage and sharing of processed data, and improve decision-making. Modern data analysis aims to increase efficiency by improving business performance. It is an effective method for assessing customer feedback and identifying future changes in sectors such as manufacturing, healthcare, and finance.
Through modern analysis programs and preparation tools,BI reports (Business Intelligence), data analysis can be done quickly in the digital environment, and due to the elimination of manual calculations, it can save costs and labor for businesses.
What are the methods of data analysis?
There are five different methods for analyzing data, and all of these methods make data analysis more effective and powerful. Various factors, such as the type of data being analyzed, the expected outcome of the analysis, and the size of the dataset, are important factors when choosing an analysis method.
Text analysis
Using databases, it uncovers relationships, patterns, and semantic patterns within large data sets. This method, also called data mining, aims to transform raw data into useful information.
Statistical analysis
It is a method that seeks answers to current or potential questions by scanning historical data. It attempts to find a prediction by following the steps of data collection, analysis, interpretation, presentation, and modeling. There are two types: descriptive analysis and inferential analysis.
Descriptive analysis
It is a method for analyzing all data or a sample taken from a numerical data set. It summarizes and visualizes existing data and presents it to the user. It applies calculations such as mean and standard deviation to continuous data; it applies calculations such as percentage and frequency to categorical data. It facilitates numerical classification of data.
deductive analysis
It is a method that analyzes a sample taken from the entire data set. Since different samples can be chosen from the same set, different results can be obtained in the analysis. Based on the sample data, the goal is to draw inferences about the entire data set.
Diagnostic analysis
A company's research topic may not be limited to what happened or what will happen, but also to why the situation under investigation occurred. It is a method that examines similar problem patterns to find the cause of the detected problem. It aims to speed up the problem-solving process by analyzing data behavior patterns.
Predictive analysis
It is a method that predicts potential events by analyzing current or retrospective data. The availability of the estimate provided depends on the details of the analysis performed, the accuracy of the data set taken, and its adaptability to future problems. Recording a sample of data for known reasons is important to shed light on the solution to potential problems. For example, an inference about future sales can be made using sales data from previous years.
Descriptive analysis
It's a method that enables selection of the best strategy among various action plans by scanning available data. Based on descriptive and predictive analytics, it analyzes insights into actions to be taken rather than simply tracking data. Since there are areas where predictive and descriptive analytics fall short, most companies opt for descriptive analytics to improve data performance.
Data analysis steps
Data analysis is the process of uncovering useful information or patterns embedded in raw data with the help of appropriate tools. Transforming seemingly useless information into valuable, actionable data is a key component of data analysis for a successful organization. The stages of advanced data analysis are:
Setting goals
Before beginning analysis processes, data requirements must be defined so that useful information can be distinguished from non-useful information. Analysis inputs and variables can be categorical or numeric.
1. Identify needs
The scope of the topic to be analyzed, the reason for the analysis, the measurement technique, and the intended outcome should be agreed upon with all stakeholders, and the study should be conducted collaboratively.
2. Determine the questions
For data analysis to achieve the desired results, it is essential to pose meaningful questions during the process. Since identifying the questions that need to be answered will determine the path to be followed during the analysis, success at this stage is directly proportional to the success of the analysis outcome.
Data collection
This stage is the stage where scattered primary information obtained from various sources such as databases, web pages, media, customer surveys, archives, and first-party company data is gathered. Although the data obtained after this stage will undergo a cleaning process, priority should be given to ensuring that the information to be pulled into the data pool is selected according to specific criteria and purpose. The time interval within which the planned data will be selected for analysis must be determined.
steps Data processing
It is the stage where all collected data is processed and organized in a manner suitable for analysis.
Clear data
To reduce the margin of error in data that has completed its data processing phase, a rescan is performed and errors are removed from the system. Depending on the type of data you are collecting, different cleaning processes are performed.
Data modeling
After the data has been processed, organized, and cleaned, the modeling phase begins. At this stage, data analysis can begin, with data deemed redundant being removed. In order to speed up modeling and reduce its cost,
- Identify a path where data management control can be achieved,
- It is important to integrate effective technology (hardware and software).
The analysis is performed using specified tools and the results are then interpreted as per requirements.
Determine KPI
When a KPI (key performance indicator) is defined, it examines the progress made in data analysis toward a defined goal and reveals the rate at which the goal is achieved. Defining KPIs is important for monitoring performance and maintaining efficiency in the analysis steps.
autonomous technology
In a digital world, manual data analysis will significantly increase the workload and cost. To avoid this situation, autonomous technologies such as artificial intelligence and machine learning can be used to ensure data is analyzed in the most efficient manner.
Improve and iterate
Failure in any of the steps could jeopardize the accuracy of the entire analysis, and repeating some steps could mean repeating the analysis process. Data analysis is an iterative process.
Transmission
The results of the data analysis are prepared for presentation in various formats, depending on the target audience. One of the preferred ways to present data as easily understandable and quickly consumable information is to enrich it with graphs, tables, and visuals. Additionally, preparing a presentation script and transforming the entire process into a story will enable the information to be transformed into a durable, easy-to-follow format.
