The implementation of many automated information systems creates disparate and unstructured data silos, which are accumulated in storage systems and can not always be quickly and accurately processed.
When running data science projects, up to 70% of employee time can be spent on data wrangling, verification, and unification.
To improve the quality of operational and production processes, businesses need quality real-time data, as well as a timely response to various parameter deviations.
A self-learning system for continuous monitoring of streaming data quality and identifying significant deviations in operational and production processes. The solution is capable of real-time collection and processing of data from various information systems and corporate stores, identifying anomalies, and informing company employees to prevent abnormal situations and failures.
The advantage of the system is that it adapts to the user's behavior, analyzes data without external assistance, and monitors their quality using and continuously retraining hundreds of thousands of independent mathematical models to track each performance indicator individually in different dimensions.
- Accelerating the response time for unfair suppliers and, as a result, reducing the number of customer claims by up to 40%;
- Automatic detection of errors in the bonus accrual system (without human assistance) and increasing customer satisfaction;
- Increasing the modeling efficiency by 15%;
- Proactive detection and prevention of 30% of incidents before users experience them
Discussion of goals, objectives, and technical implementation approaches (3–5 days)
Feasibility study based on the discovered customer issues (1–3 days)
Pilot implementation of the solution (3-5 months)
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