Problem

Implementing 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.
That will help to improve the quality of operational and production processes; businesses need quality real-time data and timely responses to various parameter deviations.

Solution

Implementing 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.
That will help to improve the quality of operational and production processes; businesses need quality real-time data and timely responses to various parameter deviations.

Result:

• 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 modelling efficiency by 15%;
• Proactive detection and prevention of 30% of incidents before users experience them

Implementation stages

PROJECT

Discussion of goals, objectives, and technical implementation approaches (3–5 days)

SCENARIO

Feasibility study based on the discovered customer issues (1–3 days)

PILOT PROJECT

Pilot implementation of the solution (3-5 months)

Scaling

Scaling