One of the key sources that has a significant impact on today's businesses, both for business development strategies and business processes, is data. Many businesses need to collect data from more and more sources every day to analyze and optimize their operational processes. As the number of these sources can sometimes reach millions, processes such as the collection, classification, management, storage of the desired data are also important. For this reason, data virtualization is needed to avoid both time and cost loss.
Data virtualization is the reproduction of real data in a virtual environment. In this way, tests, experiments and analyses performed when working with real data can be carried out without compromising the accuracy and security of the real data. Virtualization also enables multiple users to access data at the same time, or the same data can be accessed by multiple applications.
In other words, data virtualisation allows you to use virtual data to test, experiment and analyse data instead of using real data. This allows you to test, experiment and analyse without compromising the sensitivity and security of real data. Virtualisation also enables multiple users or multiple applications to access the same data. Virtualisation plays an important role in areas such as data management, data security, data access and data integration.
Data virtualization processes implemented with data virtualization tools are done using virtual data instead of real data. This process begins primarily with the data collection phase. At this stage, the goal is to collect and study real data. This data can be obtained from your business database or from another source. Then the data is anonymized. Because when doing data virtualization, in order to protect the privacy of real data, the data needs to be anonymized. In this step, the personal information, unique identifiers or other confidential information of the data is deleted.
In the third phase, the data modeling process is started. Modeling data means creating data sets. In this step, the data is made available as an example. At the end of this stage, a data set is formed that has the same distribution, statistical properties and other characteristics as the real data. Virtual data is created using the dataset formed as a result of the data modeling step. In this step, virtual data of similar qualities to real data, but maintaining the confidentiality of real data, is formed. Finally, testing and testing is carried out using virtual data. In this phase, data virtualization allows testing and experimentation with virtual data instead of tests and experiments with real data. In this way, performance tests are performed and the scalability and stability of the systems are tested without compromising the accuracy and security of the actual data.
Data virtualization has many benefits for businesses. In general, these benefits can be summarized as follows:
· Testing and testing of data:
Tests and trials with real data may result in data loss or a breach of the security of sensitive data. Data virtualization eliminates these risks by using virtual data instead of real data.
· Data security:
Analyses that are intended to be carried out with real data can carry great risks in terms of data security. Data virtualization, on the other hand, allows testing and testing of real data without compromising the security of real data.
· Performance tests:
Data virtualization enables the performance of applications and systems to be tested only with virtual data, not using real data. This allows you to perform performance tests without compromising performance, taking into account the sensitivity of real data, without the data falling into the hands of malicious people and compromising performance.
· Access:
By implementing data virtualization, more than one user or application can access the same data. This makes data sharing and collaboration with other institutions and organizations easier.
· Integration:
Data virtualization also makes the integration of different data sources easier. In this way, the data integration process will be accelerated and the data quality will improve.
· Agile Development:
Agile Development methodology applications are among the most widely used systems in recent times. In order to implement this methodology, iterations must also be made continuously. For this reason, it is important to regularly test the software. For this reason, tests with virtual data rather than tests with real data provide significant convenience at this stage.
Data virtualization offers a variety of benefits for businesses. Especially thanks to data virtualization, tests and experiments with virtual data can be performed instead of tests and experiments with real data. This allows you to test and experiment without compromising the accuracy and security of real data.
Data virtualization allows multiple users or applications to access a lot of data. This simplifies data sharing and collaboration, and accelerates the data needed for data analytics processes.
Businesses facilitate the integration of different data sources thanks to data virtualization. This speeds up the data integration process and improves data quality.
Data virtualization can be used in many industries. The financial sector in particular is one of the most important in this regard. Can use data virtualization in the financial sector for data security, testing, trial and performance testing. Again in the healthcare sector, it can be analyzed for data virtualization, data security, testing, trial and performance tests. On the other hand, it can use data virtualization to use anonymous data when using clinical trials or patient data.
Another area where data virtualization can be used most actively is the e-commerce sector. In particular, it can use it to understand customer behavior and preferences. It can also be used in scaling and testing processes. In the automotive industry, it can use data virtualization to analyze performance tests and car sensors data.
It can use Internet of things (IoT) data virtualization to test the performance of systems and analyze sensor data, as well as to analyze sensor data. In this direction, the energy sector can also use data virtualization to test sensor data and the effectiveness and performance of systems.
Data virtualization technology will become more important in the future. Because the volume of data obtained in many sectors is increasing day by day. As a result, tests and trials with real data will become more difficult. Data virtualization can be overcome by using virtual data instead of real data. On the other hand, data security is becoming more and more important every day. Therefore, tests and trials with real data can carry risks in terms of data security. Data virtualization allows testing and experimentation without compromising the security of real data.
As well as these, data analytics is becoming more and more important for businesses every day. Data virtualization can be used to accelerate data analytics processes and improve data quality. Artificial intelligence and machine learning, which are becoming widespread and integrated into our lives in many areas, also need training data for all the technologies it seeks to develop. Since data virtualization makes it easier to generate this data, it will become important for the further use of these technologies in the future.
If you also want to protect the security of your ever-increasing volume of data for your business and to obtain the most accurate results by processing this data in the most optimal way, you can get support from Komtaş experts
The data lake is where long-term data containers gather that capture, clean, and explore any raw data format at scale. Data subsets are powered by low-cost technologies that many downstream possibilities can benefit from, including data warehouses, and recommendation engines.
Data integration is a complex process by which data from different data sources and IT systems of a company is combined, enhanced, enriched and cleaned
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