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Implementing a data quality framework
5
+30%
the number of errors in data products has decreased
by
quality of data in Ozero has improved
times
Customer
The largest tourism organization in the Russian Federation – more than 3 million clients/users
CHALLENGES/FEATURES
a large number of duplicates in disparate heterogeneous data across all sources
Ensure data quality control at all stages of the data movement lifecycle – from source systems to data transfer to external systems
Task
Technical solution
- We conducted an analysis of the customer's business processes and needs
2. Developed a data quality management subsystem, which includes:
Grafana-based tools for visualizing process quality metrics, data errors, and more
PostgreSQL-based quality metrics storage component
metrics generation component based on the Python module
3. Based on BI solutions, we created a set of dashboards that allow for quick and transparent management decision-making based on data products.
Business values
Guarantee of data accuracy
Extensive data processing capabilities
real-time monitoring of the quality of downloaded data on all layers of the lake
list of sources for integration
Automatic emails to managers in case of problems with the display windows
30% improvement in lake data quality
5x reduction in errors in data products
formation of "verified" data marts
detecting data problems in real time
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Development of software
and Big Data solutions
and Big Data solutions
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