Data is viewed by organizations as valuable “assets”. Because as an organization, high-quality data that you can rely on when making strategic decisions is extremely valuable. However, with the increasing demand for data and the emergence of all kinds of new technologies, collecting data is much easier than ever. But while data is one of the most important business drivers in the business world, it must be recognized that not all data is actually valuable. One of the biggest problems a company can face is the impact of “bad” data and the problems that can arise. Dirty data is one of them. Because data quality is extremely important for you to make the right decisions. Any data that is not enough, containing incorrect information, can distract you from the goal. So what is dirty data? What is the reason and what does it cost? Let's take a look together.
Dirty data refers to data that is wrong for a company. This inaccuracy not only means that the data is not correct, the correct data can also be “dirty”. For example, if it is not related to your business or project, it is dirty data. Here the data quality is high, but there is data that does not work for you as an institution. Outdated data is also seen as dirty. Because it contains information that no longer works for you and can cause you to make wrong decisions that are not suitable for functioning today. Data can be contaminated in many ways. On the other hand, the quality problems that cause the data to be called “dirty” are:
A company's use of bad data can significantly affect its performance and, in some cases, be disastrous. Therefore, data quality should be made a priority of your organization and constantly checked and updated by the employees responsible for the data.
Poor data quality has been a problem since the advent of information systems. Because poor data quality results in reduced customer and employee satisfaction, increased costs, and poor decision making. In addition, poor data quality not only leads to high financial costs, but also weakens an organization's competitive position and undermines critical business goals. To avoid all this, you need to prioritize data quality in your organization and eliminate various factors that can cause data contamination. So how is the data contaminated?
Because other day-to-day business activities are prioritized, data quality is often at the bottom of the to-do list for companies. However, the information on which business decisions are made is only as good as the data on which it is based. The five main consequences of dirty data quality are:
Cleaning up dirty data may seem quite complicated, but this problem can be solved by improving data quality. Of course, it is very important to check the data at hand and eliminate the information that is incorrect. In the case of new data inputs, establishing various control mechanisms and making employees more informed about this can improve data quality.
All dirty data directly affect management and business processes. Therefore, to improve the quality of the data, it is necessary to make a plan and issue a correct guideline for your organization before starting cleaning and editing. When a planned process is followed, you can improve the quality of your data and get rid of dirty data in a shorter time.
Getting started by standardizing your data allows you to take the right path to work. Determine the data collection paths, the same recording of data and data entry points, and proceed along this line. The most important thing is that you set strict rules for auditing. Because it is necessary to clean up the dirty data that remains after creating standards for new data. For this, you can apply the following methods:
Data is increasingly used for business intelligence, decision-making, compliance and reporting. The quality of the data is very important in this respect. Because every organization wants to be able to trust all the data in its system and can consolidate its steps in line with the data it obtains. To improve this quality, it is the right method to focus on increasing and expanding data management, in addition to technical aspects. Planning the right processes is extremely important, especially in terms of data validation, documentation of the complexity of the database, data definitions and operating procedures. The implementation of these structures is a crucial preparation for having quality data in the future and being able to use it effectively.
Komtaş provides services on all the technologies and data quality processes you need to manage all these processes. If you want to make the right decisions for your company by improving the quality of your data and achieve your goals with confident steps, you can use Komtaş's Data Management and Data Quality services.
Unstructured data is unfiltered information to which a fixed editing policy is not applied. It is often referred to as raw data.
Tongyi Qianwen, Çin’in en büyük teknoloji şirketlerinden biri olan Alibaba tarafından geliştirilen yapay zeka destekli dil modelidir. GPT (Generative Pre-trained Transformer) gibi büyük dil modellerinden ilham alarak oluşturulan Tongyi Qianwen, özellikle Çince dilinde güçlü bir dil işleme ve metin üretimi kapasitesine sahiptir.
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