Data analytics and data quality management
The client: An innovative commercial bank
The bank requested a service that would provide high-tech financial services for private clients and businesses. The bank has dozens of information systems and a corporate data warehouse. Over time, the client had become concerned about the quality of the data contained in the repository.
In addition, only a limited number of users had access to the existing repository and primary data sources. As a rule, these were technical specialists of the service organization (contractor). The business user was forced to turn to intermediaries to obtain a specific data set. A large number of intermediate connections within work operations entailed additional labor costs.
Problems faced by the client:
The client set several strategic goals:
The data management tasks:
Master Data Management and data analytics system
The implementation consisted of 3 projects:
In the first project, BlitzBrain built a corporate warehouse based on MPP Greenplum and also set up an automatic collection of data from primary sources of business value.
Then, the BlitzBrain data specialists created a data and information system — a knowledge base concerning the meaning of specific data so that the user could determine where they were located.
As part of the second project, BlitzBrain built an MDM system — a toolkit for managing and monitoring data quality. Its task was to resolve the problem of missing information, existing duplicates, and erroneous data. As a result of this system operation, it was possible to form the client’s golden record.
A customer's ‘golden record’ is the most reliable, consistent, and complete view of each company's data object (customer, product, counterparty, etc.). It contains all the attributes necessary to describe the portrait of the client. This data can be accessed by employees in order to use the relevant information.
Measuring and improving data quality in primary systems allows specialists to identify problem areas in data sources and eliminate them. BlitzBrain experts formulated and described a methodology for calculating a system of indicators, which was programmed and calculated daily to monitor data quality.
The third project was responsible for implementing the Tableau BI analytics tool and building analytical reports.
With the help of a business intelligence (BI) system, business-relevant information was provided in the form of interactive reports that enabled analysts and managers of various levels to make business decisions in real-time.
As part of the project, 16 data sources from the corporate data warehouse were used. The planned volume at the start of the project was more than 50 TB.
Project implementation period: 18 months.
Benefits for the client: