What Is Data Collection?

Data collection is the process of collecting information, measurement and evaluation using systematic methods. Why do we collect data? The general purpose of data collection is to measure the accuracy of research hypotheses, to develop business, to understand social behavior, natural phenomena, etc.

Why Data Is Important?

The importance of data in the business world is increasing day by day. It is a key point in decision-making processes and, when properly collected and processed, it provides many advantages to companies. Since many organizations are aware of this, they have started to change their operations to be data-driven. In fact, the concept of “data-driven company” has emerged in recent years. Related data can help organizations to make informed decisions, finding solutions to their problems, reduces guessing game and help companies to follow right strategies and more.

Data Collection Procedure

It is very important to choose a tool that is suitable for your purpose for collecting data. In general, the most classic method of data collection is forms. It is a convenient tool for systematizing observations, photographs, interviews, answers to questions, etc. As the data you receive grows, a more meaningful framework will begin to appear before you.
In order to ensure quality and control, it is imperative to keep the objectives of the collected data clear and to document the protocols throughout the process.
After passing the official protocols and establishing data collection methods, data collection begins by conducting research in the field or digitally.

How Do You Process Collected Data?

After the data is collected, it is pre-processed. Raw data is cleaned and errors are checked. In this way, unnecessary, incomplete or incorrect data is eliminated and a more reliable database is created. The clean data is then inserted into a data warehouse program and transform into more understandable shape. In this way, raw data is entered and turned into usable data. In the next step, the entered data is processed for interpretation. Machine learning algorithms are used in the processing process. However, this process may vary depending on the processed data source and the purpose of use of the data. The other stage, the data interpretation part, is the stage where the data becomes understandable, readable, visible (graphics, texts
etc). At this stage, companies or institutions can now read the data and take meanings out according to their needs. And finally, in the data storage stage, all data is stored for future use. Properly stored data is quickly and easily accessible when needed.