Vedeg is a data engine that creates a data warehouse structure by providing analysis, clearing and tracking of data on different platforms. Its main purpose is to store out-of-date data of a corporation or organization, enable quick and easy analysis of the said data, identify all requirements of the business and help renew and develop its functions. It is to facilitate the business intelligence that corporations or organizations need.
Relational databases store all data on events and transactions, and are constantly in data input and output. They always contain the most up-to-date data. Vedeg enables periodic analysis by compiling and archiving the data in these databases and other data collected from external sources (xls, txt, csv, etc.).
Vedeg separates the analysis load and workload in corporations/organizations. While analyzing the compiled data in the databases kept in different locations, the relational databases of the corporation/organization continue to host up-to-date data and meet the query demands.
In addition to its data hosting task, Vedeg also provides the transformation of raw data into enhanced information thanks to its features such as extract-transform-load (ETL), reporting and data mining.
ETL (Extract-Transform-Load): It is the process of extracting the data to be used from other external sources, and clearing, combining, transforming and uploading the data according to the business model. When large-scale data are separated from operational systems and uploaded to the data warehouse, they are taken through the ETL process. The goal here is to optimize the data planned to be used for business intelligence.
According to Article 11 of the Law No. 6698 on the Protection of Personal Data (“KVKK”), individuals have certain rights over their own data. These primarily include their rights to find out whether their personal data have been reprocessed, and find out which data have been processed.
This is where Vedeg comes into play and clears all the mess. With Vedeg, the data on different platforms are analyzed, cleared, tracked and transformed into a data warehouse.
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