Data cleanup, or data rubbing, is the process of detecting and correcting or removing data or records that are incorrect from a database. It also includes correcting or removing unformatted or duplicate data or records. The data removed in this process is often referred to as “dirty data”. Data cleaning is a necessary process to protect data quality. Large businesses with extensive datasets or assets typically use automated tools and algorithms to detect such records and correct common errors (such as missing zip codes in customer records).
The most powerful big data circles have rigorous data cleanup tools and processes to ensure that data quality is protected and trust in datasets is high for all types of users.
Data architecture is a set of rules, policies, standards, and models that govern and determine the type of data collected, and show how this data is used, stored, managed, and integrated within an enterprise and database systems.
Retail analytics is the analysis of data generated by retail transactions in order to make profitable business decisions.
Emotion analysis is the capture and monitoring of ideas, feelings, or feelings expressed by customers who have had various types of interactions, such as social media posts, customer service calls, and surveys.
We work with leading companies in the field of Turkey by developing more than 200 successful projects with more than 120 leading companies in the sector.
Take your place among our successful business partners.
Fill out the form so that our solution consultants can reach you as quickly as possible.
By combining our 30+ years of experience and experience with the centers of excellence in our ecosystem, we prepare your organization to lead the digital transformation!
