Data-based is one of the terms that is becoming more popular in organizations, large or small. Almost every type of company, especially online marketing, believes that digitalization is a necessity. Because companies embarking on digital transformation care about using data smarter and setting goals as a result of that data. But to be data-driven, you need to use the right data. You can also do this with data analysis. If you have done research and collected data as a result, you should analyze this data to extract information that you can use. Because an incorrect data can undermine both management and business processes. You can mislead about the volume of work, be misleading about customers, so you can make the wrong decisions for your new goals. It is data analysis that prevents all this from happening. The process is more complicated because large companies have more data, but you can benefit from the benefits of digitalization by taking advantage of data analysis services. What is bridal data analysis? What kind of benefits can it bring to your company? Let's take a look at the answers to all these questions together.
Data analysis is the thorough and careful review and interpretation of data collected through a study. Data analysis then yields results that can be used to accurately answer research questions. Data analysis takes place in both qualitative research and quantitative research. It covers processes, tools, and techniques for data analysis and management, including data collection, organization, and storage. The main purpose of data analysis is to draw statistical conclusions to determine the cause of problems, to guide for solutions, or to give insight into trends. Data analysis is becoming increasingly important in organizations as a means of analyzing, shaping, making decisions and improving business results.
Data analysis as an umbrella term uses available data as raw material. Reveals new trends, insights, and patterns that dramatically simplify business-critical decision-making Data analysis is the collection, cleaning, duplication, integration, and interpretation of data in order to arrive at a dataset with useful information for preparing a report or compiling a dashboard. The process of data analysis begins with a clearly defined research question.
Data analysis is important because the data gives you the answers to the questions that need to be answered right now. It also gives you the ability to generate insights into the behavior of your target group. The data you get as a result of data analysis can be website statistics, Google ranking, keyword positions, link profiles, social media statistics, reactions to your social networks, email statistics. These resources are useful on their own, but the main thing is to combine and interpret this data. Because the information contained in all these sources can be brought together in the common denominator and much more useful information can be obtained. Therefore, data analysis is much more valuable than any data. Also based on the data collected, you can determine what is going well, what is not going well, and what are the areas open to improvement. It is very important to be able to make the right decisions within your company and thus have the right knowledge to grow and develop.
When analyzing data, it is important to look for results that can confirm your assumptions. However, this leads to results based on bias and therefore incorrect information during a data analysis. Therefore, at this point in your research, it is also important to look at the collected data as independently as possible and allow the data to be transformed into information.
The process begins with collecting data, finding patterns, and then using those patterns to make predictions. These forecasts can be used to set goals or make decisions. For example, in sales, you can use data analysis to estimate how much a particular product will sell next month. Knowing this number helps you set goals for your team and plan inventory.
To begin with, its data is collected and added to a database. Then I proceed to the stage of cleaning and editing. At this stage, the model is determined and the data is edited. The relationships between the patterns are then searched and a model is developed that explains these relationships. This is the modeling stage. When a sufficiently useful model is found, it is used to make predictions. This is the testing phase. These estimates are communicated to decision-makers (such as managers) in the form of reports. Although data analysis techniques vary, the basic stages take place in this way:
The number of different types of data analysis is very large, but they all fall into one of four categories: descriptive, diagnostic, predictive and normative. Other data analysis techniques are classified as sub-branches of these major types.
Descriptive analysis focuses on what happened in the past. It does not look ahead, but offers a comprehensive picture of how events unfolded. The main benefit of descriptive data analysis is that it helps people understand exactly what happens and why. For example, managers may want to know when daily sales exceed a certain amount or how many units each employee sold this month. It can also be used for fraud detection. Credit card companies constantly monitor transactions for suspicious activity that may indicate credit card fraud.
Diagnostic analysis attempts to answer why a problem occurs by looking at the factors that cause an event. This type of analysis can help companies understand not only what happened, but why it happened and how to prevent it from happening again. Diagnostic analyses can also be performed as root cause analysis, retrospective analysis, detail analysis, and regression analysis. Fundamental cause analysis is an analytical process used to determine the root causes of adverse events such as manufacturing defects. When it comes to data analysis methods, going into detail is also quite important. For example, a retailer might use detail analysis to discover that inventory levels are low because sales in a particular region have increased over the past six months.
Predictive analysis uses current data to determine future outcomes or trends. Companies often use this method when developing new products or services because it gives customers an idea of what they will want in the future based on their past behavior. It is used in topics such as direct marketing, pricing, retail. For example, the chance to determine which leads are most likely to respond to a marketing campaign can give the ability to determine the optimal price for a product or service based on underlying demand.
Prescriptive analysis, or prescriptive analysis, takes predictive analytics a step further by using historical trends and data to suggest future actions. This type of data analysis is very useful in optimizing resources and identifying new business opportunities, such as expansion. Normative analytics can be used to make decisions or make recommendations that will help them make better and faster decisions. By creating a normative data analysis model, you can start a new product line, end an existing one. You can hire more people in the sales department.
If you want to convert all the data you collect as a company and are in your database into healthy information and routers, you need to do data analysis. It is very important that the data analysis is carried out correctly and that there are no erroneous data. Therefore, you can get help from Komtaş's data services to make the right decisions that can affect all processes such as management, production, sales, customer experience, and find the most suitable solutions for all your data-driven processes, especially data analysis.
Variational Autoencoders (VAE), derin öğrenme dünyasında güçlü bir modeldir ve verilerin gizli yapılarının keşfedilmesi için kullanılır.
Generative Adversarial Networks (GANs), iki sinir ağını (jeneratör ve ayırt edici) birbiriyle yarışan bir öğrenme mekanizmasında eğiterek gerçekçi veriler üreten yapay zeka modelleridir. Bu teknolojinin farklı kullanım alanlarına yönelik birçok türevi geliştirilmiştir
Neural Networks (Sinir Ağları), yapay zeka ve makine öğrenmesinin temel yapı taşlarından biridir. İnsan beyninin işleyişinden ilham alınarak tasarlanan bu yapılar, karmaşık problemlerin çözümünde ve veri işleme süreçlerinde kullanılır.
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