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Smart data quality management
Improve data quality, maximize data usage and potential.
Smart data quality management
It is often asked how to obtain more reliable, timely, accurate, consistent, complete and unique data. What is the best way to prepare ideal input data for specialists in various disciplines and provide fast data to engineers solving analytical problems? The answer is simple: take a closer look and implement a modern end-to-end DiQS solution to ensure data quality within the tasks at your enterprise.
In IT infrastructure
By improving the quality of data in your enterprise, you begin to maximize its potential. DiQS (full name - Data information Quality System) is suitable for any IT infrastructure, meeting your specific business needs with a reliable and flexible internal mechanism and an easy-to-use business-oriented interface.

It is a modern end-to-end data quality solution that meets the specific needs of your business and is reliable, easy to use, flexible, powerful, scalable, and fast. A holistic approach will improve the quality of your data and introduce a culture of data management, help avoid risky decisions and save money.
Researching information is the first step in any data project.

Use the intelligent, automated DiQS algorithms to discover information to understand the quality of your data and empower users to make smarter and more informed decisions, thereby preventing costly mistakes at the beginning or during tasks.

Store all your information for future projects in verified data catalogs.
Monitoring
Data quality assurance rule management can be collaborative, easy to use, and provides results for a wide range of business users and third-party applications.

Evaluate, track and improve the quality of your data, regardless of the subject area.
Quality control
Reporting
Use the intelligent DiQS system so that information about the current level of data quality is available when you need to make a quick and effective decision. See trends, view full history, compare versions, and get accurate and timely visual reports based on insights from all parts of your company. Do it all directly from the web application in a complete, comprehensive self-service scenario.
Firewall
Protect your systems from "bad", low-quality information with a powerful data quality firewall.

Use an extensive set of pre-designed algorithms for this, or create your own.

Use information from a wide variety of sources to make your data more complete and complete.

Extend your current cleanup and mapping processes by adding new components (data sources), preventing users from creating new information quality issues in your systems.

Please be sure that any edited or newly entered data is accurate.
Flexibility and open standards, DiQS is a platform-independent, open standards-based (Web Services) system using data models that are easily ported to existing database platforms. This solution can be easily configured using built-in administrative applications without the need for external tools or third-party applications.

Complete audit history, keep a complete audit history of all changes made. This information can be easily used to determine who, when, what, and why, as well as to create advanced reports.

Full range of reports, use a set of reports that allow managers to track the status of the process and performance of data managers, both at the moment and over a period of time.

Customizable workflows, all aspects of the process of solving any problem are completely customizable, including the ability to specify the number of process steps, set various conditions, access rights and actions to be performed. Different workflows can be used for different types of problems, systems, or objects.

Simplify and automate the setup process, including automatic error detection, automatic project setup, and evaluation of results.

The development of the system towards Artificial Intelligence (AI), in the near future AI will be used in the process of comparing, cleaning and classifying data based on machine learning, DiQS is already actively learning, observing the actions of users (especially when they solve any problems) and detecting anomalies in loading and assessing the quality of new data.
Peculiarities